Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.展开更多
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect...Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.展开更多
This study examines the methods to plan the development of offshore oilfields over the years,which are used to support the decision-making on the development of offshore oilfields.About 100 papers are analysed and cat...This study examines the methods to plan the development of offshore oilfields over the years,which are used to support the decision-making on the development of offshore oilfields.About 100 papers are analysed and categorised into different groups of main early-stage decisions.The present study stands in contrast to the contributions of the operations research and system engineering review articles,on the one hand,and the petroleum engineering review articles,on the other.This is because it does not focus on one methodological approach,nor does it limit the literature analysis by offshore oilfield characteristics.Consequently,the present analysis may offer valuable insights,for instance,by identifying environmental planning decisions as a recent yet highly significant concern that is currently being imposed on decision-making process.Thus,it is evident that the incorporation of safety criteria within the technical-economic decision-making process for the design of production systems would be a crucial requirement at development phase.展开更多
Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper pr...Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.展开更多
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo...This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.展开更多
As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This pap...As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.展开更多
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predica...Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures,existing direct generation approaches exhibit severe performance degradation.This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement.The system implements a complete generate-verify-repair cycle through six core processing components:semantic comprehension extracts structural constraints,language planner generates text plans,symbol translator performs structure-preserving mapping,consistency checker conducts static screening,Stanford Research Institute Problem Solver(STRIPS)simulator executes step-by-step validation,and VAL(Validator)provides semantic verification.A repair controller orchestrates four targeted strategies addressing typical failure patterns including first-step precondition errors andmid-segment statemaintenance issues.Comprehensive evaluation on PlanBench Mystery Blocksworld demonstrates substantial improvements over baseline approaches across both language models and reasoning models.Ablation studies confirm that each architectural component contributes non-redundantly to overall effectiveness,with targeted repair providing the largest impact,followed by deep constraint extraction and stepwise validation,demonstrating that superior performance emerges from synergistic integration of these mechanisms rather than any single dominant factor.Analysis reveals distinct failure patterns betweenmodel types—languagemodels struggle with local precondition satisfaction while reasoning models face global goal achievement challenges—yet the validation-driven mechanism successfully addresses these diverse weaknesses.A particularly noteworthy finding is the convergence of final success rates across models with varying intrinsic capabilities,suggesting that systematic validation and repair mechanisms play a more decisive role than raw model capacity in lexical-prior-free scenarios.This work establishes a rigorous evaluation framework incorporating statistical significance testing and mechanistic failure analysis,providingmethodological contributions for fair assessment and practical insights into building reliable planning systems under extreme constraint conditions.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio...Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.展开更多
A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
Systematically analyzing the impact mechanisms of policy on Land Use Conflict(LUC)is crucial for constructing effective conflict mitigation strategies.However,previous research on how policy influences LUC remains rel...Systematically analyzing the impact mechanisms of policy on Land Use Conflict(LUC)is crucial for constructing effective conflict mitigation strategies.However,previous research on how policy influences LUC remains relatively limited.Focusing on the indirect driving role of policy on LUC,this study proposed County Development Level(CDL)under Major Function Oriented Zone Planning(MFOZP)guidance as an intermediary variable,bridging the implicit influence of MFOZP and the explicit changes in LUC.Using the Beijing-Tianjin-Hebei(BTH)region in China as a case study,we analyzed the spatio-temporal evolution characteristics of LUC and CDL for the periods 2000-2010 and 2010-2020,before and after MFOZP implementation.Panel models and Geographically Weighted Regression(GWR)were employed to explore the mechanism by which CDL influences LUC under MFOZP guidance.The results show that:1)MFOZP implementation effectively alleviates land use pressure from regional development,with LUC continuously declining at a rate of 2.41%,while CDL exhibits slight growth(3.84%),during 2010-2020.2)Under MFOZP guidance,CDL reduces pressure on Land Use Structure Conflict(LUSC)and Land Use Process Conflict(LUPC),enhances its inhibitory effect on Land Use Function Conflict(LUFC),and significantly contributes to LUC coordination,with notable spatial heterogeneity.3)The coupling relationship between CDL and LUC has improved post-implementation.Based on this,tailored LUC coordination strategies are proposed for different functional zones.This study confirms the effectiveness of MFOZP in coordinating LUC and provides a scientific reference for LUC research under policy frameworks and the governance of LUC in the BTH region.展开更多
Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These iss...Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-d...The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.展开更多
Objective:This study aimed to explore the readiness for advance care planning(ACP)among older adults in Macao’s day service centers and investigate the influencing factors.Methods:A cross-sectional study was conducte...Objective:This study aimed to explore the readiness for advance care planning(ACP)among older adults in Macao’s day service centers and investigate the influencing factors.Methods:A cross-sectional study was conducted from October to December 2022 using a convenience sampling method.A total of 312 older adults were selected from 13 day service centers for older adults in Macao,China.The Advance Care Planning Acceptance Questionnaire and the Family Adaptation,Partnership,Growth,Affection,Resolve(APGAR)Scale were used to survey the older adults.Results:A total of 306 older adults completed the survey.The score for advance care planning readiness was 65.55±10.69,and 59.5%of participants(n=182)were willing to participate in ACP.The family function score was 7.24±2.51,while 70.3%of participants were from a highly functional family.The higher family function indicating a higher readiness for advance care planning(r=0.396,P<0.001).The multiple linear regression analysis indicated that the variables“age,”“knowledge of ACP,”“experience with ACP,”and“received resuscitation of yourself,relatives or friends”combined with“family function”can influence advance care planning readiness among older adults(R^(2)=0.317,F=27.898,P<0.001).Conclusions:Older adults in Macao’s day service centers were willing to engage in ACP.The importance of family involvement is highlighted in the ACP readiness.Health education and improved family communication are vital for promoting ACP,which ensures individuals receive care when they lack the capacity to make that choice.Additionally,healthcare professionals should enhance communication and education with older adults during the medical care process.展开更多
The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving...The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving timely and compatible solutions to treat diverse skin injuries.In situ bioprinting has emerged as a key new technology,since it reduces risks during the implantation of printed scaffolds and demonstrates superior therapeutic effects.However,maintaining printing fidelity during in situ bioprinting remains a critical challenge,particularly with respect to model layering and path planning.This study proposes a novel optimization-based conformal path planning strategy for in situ bioprinting-based repair of complex skin injuries.This strategy employs constrained optimization to identify optimal waypoints on a point cloud-approximated curved surface,thereby ensuring a high degree of similarity between predesigned planar and surface-mapped 3D paths.Furthermore,this method is applicable for skin wound treatments,since it generates 3D-equidistant zigzag curves along surface tangents and enables multi-layer conformal path planning to facilitate the treatment of volumetric injuries.Furthermore,the proposed algorithm was found to be a feasible and effective treatment in a murine back injury model as well as in other complex models,thereby showcasing its potential to guide in situ bioprinting,enhance bioprinting fidelity,and facilitate improvement of clinical outcomes.展开更多
As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-int...As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.展开更多
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Ashit Kumar Dutta would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025017.
文摘Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
基金the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering(CENTEC),which is financed by the Portuguese Foundation for Science and Technology(Fundação para a Ciência e a Tecnologia FCT)under contract UIDB/UIDP/00134/2020.
文摘This study examines the methods to plan the development of offshore oilfields over the years,which are used to support the decision-making on the development of offshore oilfields.About 100 papers are analysed and categorised into different groups of main early-stage decisions.The present study stands in contrast to the contributions of the operations research and system engineering review articles,on the one hand,and the petroleum engineering review articles,on the other.This is because it does not focus on one methodological approach,nor does it limit the literature analysis by offshore oilfield characteristics.Consequently,the present analysis may offer valuable insights,for instance,by identifying environmental planning decisions as a recent yet highly significant concern that is currently being imposed on decision-making process.Thus,it is evident that the incorporation of safety criteria within the technical-economic decision-making process for the design of production systems would be a crucial requirement at development phase.
基金supported by the Natural Science Foundation of China(Grants U2166211)Zhejiang Provincial Natural Science Foundation of China(Grants LY24E070006 and LMS25E070002).
文摘Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.
基金CHINA POSTDOCTORAL SCIENCE FOUNDATION(Grant No.2025M771925)Young Scientists Fund(C Class)(Grant No.32501636)Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(Grant No.2572025JT04).
文摘This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.
基金support of the National Key Research and Development Plan(Grant No.2021YFB3302501)the financial support of the National Science Foundation of China(Grant No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(Grant No.DUT24LAB129).
文摘As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金supported by the Information,Production and Systems Research Center,Waseda University,and partly supported by the Future Robotics Organization,Waseda Universitythe Humanoid Robotics Institute,Waseda University,under the Humanoid Project+1 种基金the Waseda University Grant for Special Research Projects(grant numbers 2024C-518 and 2025E-027)was partly executed under the cooperation of organization between Kioxia Corporation andWaseda University.
文摘Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures,existing direct generation approaches exhibit severe performance degradation.This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement.The system implements a complete generate-verify-repair cycle through six core processing components:semantic comprehension extracts structural constraints,language planner generates text plans,symbol translator performs structure-preserving mapping,consistency checker conducts static screening,Stanford Research Institute Problem Solver(STRIPS)simulator executes step-by-step validation,and VAL(Validator)provides semantic verification.A repair controller orchestrates four targeted strategies addressing typical failure patterns including first-step precondition errors andmid-segment statemaintenance issues.Comprehensive evaluation on PlanBench Mystery Blocksworld demonstrates substantial improvements over baseline approaches across both language models and reasoning models.Ablation studies confirm that each architectural component contributes non-redundantly to overall effectiveness,with targeted repair providing the largest impact,followed by deep constraint extraction and stepwise validation,demonstrating that superior performance emerges from synergistic integration of these mechanisms rather than any single dominant factor.Analysis reveals distinct failure patterns betweenmodel types—languagemodels struggle with local precondition satisfaction while reasoning models face global goal achievement challenges—yet the validation-driven mechanism successfully addresses these diverse weaknesses.A particularly noteworthy finding is the convergence of final success rates across models with varying intrinsic capabilities,suggesting that systematic validation and repair mechanisms play a more decisive role than raw model capacity in lexical-prior-free scenarios.This work establishes a rigorous evaluation framework incorporating statistical significance testing and mechanistic failure analysis,providingmethodological contributions for fair assessment and practical insights into building reliable planning systems under extreme constraint conditions.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
基金supported by the National Natural Science Foundation of China(No.U2433214)。
文摘Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.
基金supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
文摘A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
基金Under the auspices of the National Key Research and Development Program of China(No.2018YFD1100803)the Fundamental Research Fund for the Central Universities(Ph.D.Top Innovative Talents Fund of CUMTB)(No.BBJ2024029)。
文摘Systematically analyzing the impact mechanisms of policy on Land Use Conflict(LUC)is crucial for constructing effective conflict mitigation strategies.However,previous research on how policy influences LUC remains relatively limited.Focusing on the indirect driving role of policy on LUC,this study proposed County Development Level(CDL)under Major Function Oriented Zone Planning(MFOZP)guidance as an intermediary variable,bridging the implicit influence of MFOZP and the explicit changes in LUC.Using the Beijing-Tianjin-Hebei(BTH)region in China as a case study,we analyzed the spatio-temporal evolution characteristics of LUC and CDL for the periods 2000-2010 and 2010-2020,before and after MFOZP implementation.Panel models and Geographically Weighted Regression(GWR)were employed to explore the mechanism by which CDL influences LUC under MFOZP guidance.The results show that:1)MFOZP implementation effectively alleviates land use pressure from regional development,with LUC continuously declining at a rate of 2.41%,while CDL exhibits slight growth(3.84%),during 2010-2020.2)Under MFOZP guidance,CDL reduces pressure on Land Use Structure Conflict(LUSC)and Land Use Process Conflict(LUPC),enhances its inhibitory effect on Land Use Function Conflict(LUFC),and significantly contributes to LUC coordination,with notable spatial heterogeneity.3)The coupling relationship between CDL and LUC has improved post-implementation.Based on this,tailored LUC coordination strategies are proposed for different functional zones.This study confirms the effectiveness of MFOZP in coordinating LUC and provides a scientific reference for LUC research under policy frameworks and the governance of LUC in the BTH region.
文摘Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金supported by the National Key R&D Program of China(No.2022YFB3104502)the National Natural Science Foundation of China(No.62301251)+2 种基金the Natural Science Foundation of Jiangsu Province of China under Project(No.BK20220883)the open research fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2024D04)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
文摘Objective:This study aimed to explore the readiness for advance care planning(ACP)among older adults in Macao’s day service centers and investigate the influencing factors.Methods:A cross-sectional study was conducted from October to December 2022 using a convenience sampling method.A total of 312 older adults were selected from 13 day service centers for older adults in Macao,China.The Advance Care Planning Acceptance Questionnaire and the Family Adaptation,Partnership,Growth,Affection,Resolve(APGAR)Scale were used to survey the older adults.Results:A total of 306 older adults completed the survey.The score for advance care planning readiness was 65.55±10.69,and 59.5%of participants(n=182)were willing to participate in ACP.The family function score was 7.24±2.51,while 70.3%of participants were from a highly functional family.The higher family function indicating a higher readiness for advance care planning(r=0.396,P<0.001).The multiple linear regression analysis indicated that the variables“age,”“knowledge of ACP,”“experience with ACP,”and“received resuscitation of yourself,relatives or friends”combined with“family function”can influence advance care planning readiness among older adults(R^(2)=0.317,F=27.898,P<0.001).Conclusions:Older adults in Macao’s day service centers were willing to engage in ACP.The importance of family involvement is highlighted in the ACP readiness.Health education and improved family communication are vital for promoting ACP,which ensures individuals receive care when they lack the capacity to make that choice.Additionally,healthcare professionals should enhance communication and education with older adults during the medical care process.
基金supported in part by the National Natural Science Foundation of China(Nos.52205532 and 624B2077)the National Key Research and Development Program of China(No.2023YFB4302003).
文摘The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving timely and compatible solutions to treat diverse skin injuries.In situ bioprinting has emerged as a key new technology,since it reduces risks during the implantation of printed scaffolds and demonstrates superior therapeutic effects.However,maintaining printing fidelity during in situ bioprinting remains a critical challenge,particularly with respect to model layering and path planning.This study proposes a novel optimization-based conformal path planning strategy for in situ bioprinting-based repair of complex skin injuries.This strategy employs constrained optimization to identify optimal waypoints on a point cloud-approximated curved surface,thereby ensuring a high degree of similarity between predesigned planar and surface-mapped 3D paths.Furthermore,this method is applicable for skin wound treatments,since it generates 3D-equidistant zigzag curves along surface tangents and enables multi-layer conformal path planning to facilitate the treatment of volumetric injuries.Furthermore,the proposed algorithm was found to be a feasible and effective treatment in a murine back injury model as well as in other complex models,thereby showcasing its potential to guide in situ bioprinting,enhance bioprinting fidelity,and facilitate improvement of clinical outcomes.
基金funded by the Technology Project of State Grid Jibei Electric Power Supply Co.,Ltd.(Grant Number:52018F240001).
文摘As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.
