Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
Discussions on Chinese modernization are offering African countries both conceptual inspiration and practical references as they explore their own sustainable development paths.
Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning p...Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning problems.However,Q-learning faces challenges in search and update efficiency.To address these issues,we propose an improved Q-learning(IQL)algorithm.We use an enhanced Ant Colony Optimization(ACO)algorithmto optimizeQtable initialization.We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma.The IQL algorithm is extensively tested in three grid environments of different scales.The results validate the accuracy of themethod and demonstrate superior path-planning performance compared to traditional approaches.The algorithm reduces the number of trials required for convergence,improves learning efficiency,and enables faster adaptation to environmental changes.It also enhances stability and accuracy by reducing the standard deviation of trials to zero.On grid maps of different sizes,IQL achieves higher expected returns.Compared with the original Q-learning algorithm,IQL improves performance by 12.95%,18.28%,and 7.98% on 10*10,20*20,and 30*30 maps,respectively.The proposed algorithm has promising applications in robotics,path planning,intelligent transportation,aerospace,and game development.展开更多
Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and addr...Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and address only one bandgap,limiting independent control of multiple gaps.Here,we investigate multiple isolated Dirac points in a trefoil-knot-modified honeycomb lattice,and show that a single SBP generally inverts all relevant Dirac points simultaneously,whereas the tailored combinations of SBPs enable selective and programmable band inversion at targeted gaps.The excitation-dependent responses reveal strong modal selectivity.This capability is exploited to realize independently controllable multi-channel signal splitting,which is unattainable with a single SBP.The results enable SBPs as an effective design degree of freedom for programmable and reconfigurable topological elastic devices.展开更多
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
Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear stre...Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.展开更多
To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this p...To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.展开更多
This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture ...This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.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
When a porous rock is subjected to overall compressive loading,either increasing pore pressure or decreasing confining pressure could result in rock failure.The stress path and the applied pressure change rate may aff...When a porous rock is subjected to overall compressive loading,either increasing pore pressure or decreasing confining pressure could result in rock failure.The stress path and the applied pressure change rate may affect the initiation and propagation of fractures within brittle materials.Understanding the physical mechanisms leading to failure is crucial for underground engineering applications and geo-energy exploration and storage.We conducted triaxial compression experiments on porous Bentheim sandstone samples at different stress paths and pressure change rates.First,at a constant confining pressure of 35 MPa and pore pressure of 5 MPa,intact cylindrical samples were axially loaded up to about 85%of the peak strength.Subsequently,the axial piston position was fixed,and then either the pore pressure was increased or the confining pressure was decreased at two different rates(0.5 MPa/min or 2 MPa/min),leading to final catastrophic failure.The mechanical results revealed that samples subjected to higher rates of decreasing effective confining pressure exhibited larger stress drop rates,higher slip rates,higher total breakdown work,higher rates of acoustic emissions(AEs)before failure,and higher post-failure AE decay rates.In contrast,the applied stress path did not significantly affect rock failure characteristics.Comparison of located AE events with post-mortem microstructures of deformed samples shows a good agreement.The AE source type determined from the P-wave first-motion polarity shows that shear failure dominated the fracture process when approaching failure.Gutenberg-Richter b-values revealed a significant decrease before failure in all tests.Our results indicate that,in contrast to the stress path,the rate of effective stress change strongly affects fracturing behavior and AE rate changes.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
In recent years,unmanned aerial vehicles(UAVs)cooperative path planning is attracting more and more research attention.For the multi-UAV cooperative path planning problem,the path planning problem in three-dimensional...In recent years,unmanned aerial vehicles(UAVs)cooperative path planning is attracting more and more research attention.For the multi-UAV cooperative path planning problem,the path planning problem in three-dimensional(3D)environment is transformed into an optimization problem by introducing the fitness function and constraints such as minimizing path length,maintaining a low and stable flight altitude,and avoiding threat zones.A multi-strategy hybrid grey wolf optimization(MSHGWO)algorithm is proposed to address this problem.Firstly,a chaotic Cubic mapping is introduced to initialize the grey wolf positions to make its initial position distribution more uniform.Secondly,an adaptive adjustment weight factor is designed,which can adjust the movement weight based on the rate of fitness value decrease within a unit Euclidean distance,thereby improving the quality of the population.Finally,an elite opposition-based learning strategy is introduced to improve the population diversity so that the population jumps out of the local optimum.Simulation results indicate that the MSHGWO is capable of generating constraint-compliant paths for each UAV in complex 3D environments.Furthermore,the MSHGWO outperforms other algorithms in terms of convergence speed and solution quality.Meanwhile,flight experiments were conducted to validate the path planning capability of MSHGWO in real-world obstacle environments,further demonstrating the feasibility of the proposed multi-UAV cooperative path planning approach.展开更多
Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow ...Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow River Culture”with“Cultural Confidence”.It proposes an integrated“Objective-Content-Path-Support”framework.Through the synergy of three-dimensional objectives,adaptation of stratified content,innovation in four-dimensional pathways,and support from a three-dimensional guarantee system,this framework establishes a closed-loop operational mechanism of“Curriculum-Practice-Evaluation-Feedback”.The study focuses on core issues in integrating Yellow River culture into university education practices,such as content construction,methodological pathways,and institutional guarantees.It aims to provide a systematic reference for universities to fulfill their fundamental task of“fostering virtue and cultivating talent”and to serve the national strategies for ecological protection and high-quality development in the Yellow River Basin.展开更多
Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the c...Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.展开更多
The oxygen evolution reaction(OER)suffers from sluggish kinetics,necessitating efficient electrocatalysts to reduce overpotentials in water splitting.Currently recognized OER mechanisms primarily include the adsorbate...The oxygen evolution reaction(OER)suffers from sluggish kinetics,necessitating efficient electrocatalysts to reduce overpotentials in water splitting.Currently recognized OER mechanisms primarily include the adsorbate evolution mechanism(AEM),lattice oxygen mechanism(LOM),and oxide path mechanism(OPM).Compared to AEM,limited by scaling relationships,and LOM,constrained by stability issues,the OPM offers a promising alternative by enabling direct O-O bond formation via dual active sites,thus bypassing^(*)OOH intermediates and lattice O involvement and achieving a balance between activity and durability.However,activating the OPM process requires precise control over the spatial and electronic structure of active sites,making the design of OPM-based catalysts challenging.While previous reviews have focused on homo/heteronuclear diatomic perspectives of OPM-based catalysts,it is urgent to systematically summarize design strategies to provide a rational reference for their development.Herein,a review of design strategies for OPM-based OER catalysts across three scales is comprehensively presented,including in-situ engineering,doping-enabled sites reconstruction,and introducing new sites for nanoparticles,direct synthesis or post-treatments for molecular catalysts,and doping or template strategies for atom pairs or arrays.The unique advantage of atom arrays is also highlighted,and their future research directions and possible strategies are discussed.This review provides a systematic summary and forward-looking perspectives for rationally designing high-performance OPM-based OER catalysts.展开更多
To address real-time path planning requirements for multi-unmanned aerial vehicle(multi-UAV)collaboration in environments,this study proposes an improved multi-agent deep deterministic policy gradient algorithm with p...To address real-time path planning requirements for multi-unmanned aerial vehicle(multi-UAV)collaboration in environments,this study proposes an improved multi-agent deep deterministic policy gradient algorithm with prioritized experience replay(PER-MADDPG).By designing a multi-dimensional state representation incorporating relative positions,velocity vectors,and obstacle distance fields,we construct a composite reward function integrating safe obstacle avoidance,formation maintenance,and energy efficiency for environment perception and multiobjective collaborative optimization.The prioritized experience replay mechanism dynamically adjusts sampling weights based on temporal difference(TD)errors,enhancing learning efficiency for high-value samples.Simulation experiments demonstrate that our method generates real-time collaborative paths in 3D complex obstacle environments,reducing training time by 25.3%and 16.8%compared to traditional MADDPG and multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithms respectively,while achieving smaller path length variances among UAVs.Results validate the effectiveness of prioritized experience replay in multi-agent collaborative decision-making.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
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.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
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548+3 种基金BK20250876)Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
文摘Discussions on Chinese modernization are offering African countries both conceptual inspiration and practical references as they explore their own sustainable development paths.
基金Financial supports from the National Natural Science Foundation of China(GrantNo.52374123&51974144)Project of Liaoning Provincial Department of Education(GrantNo.LJKZ0340)Liaoning Revitalization Talents Program(Grant No.XLYC2211085)are greatly acknowledged.
文摘Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning problems.However,Q-learning faces challenges in search and update efficiency.To address these issues,we propose an improved Q-learning(IQL)algorithm.We use an enhanced Ant Colony Optimization(ACO)algorithmto optimizeQtable initialization.We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma.The IQL algorithm is extensively tested in three grid environments of different scales.The results validate the accuracy of themethod and demonstrate superior path-planning performance compared to traditional approaches.The algorithm reduces the number of trials required for convergence,improves learning efficiency,and enables faster adaptation to environmental changes.It also enhances stability and accuracy by reducing the standard deviation of trials to zero.On grid maps of different sizes,IQL achieves higher expected returns.Compared with the original Q-learning algorithm,IQL improves performance by 12.95%,18.28%,and 7.98% on 10*10,20*20,and 30*30 maps,respectively.The proposed algorithm has promising applications in robotics,path planning,intelligent transportation,aerospace,and game development.
基金Project supported by the National Natural Science Foundation of China(Nos.12232015 and12572106)the National Key R&D Program of China(Nos.2024YFB3408700,2024YFB3408701,2024YFB3408703)the Natural Science Foundation of Shaanxi Province of China(No.2023-JC-YB-073)。
文摘Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and address only one bandgap,limiting independent control of multiple gaps.Here,we investigate multiple isolated Dirac points in a trefoil-knot-modified honeycomb lattice,and show that a single SBP generally inverts all relevant Dirac points simultaneously,whereas the tailored combinations of SBPs enable selective and programmable band inversion at targeted gaps.The excitation-dependent responses reveal strong modal selectivity.This capability is exploited to realize independently controllable multi-channel signal splitting,which is unattainable with a single SBP.The results enable SBPs as an effective design degree of freedom for programmable and reconfigurable topological elastic devices.
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support from the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX24_2822)the Graduate Innovation Program of China University of Mining and Technology(Grant No.2024WLKXJ205)the National Natural Science Foundation of China(Grant No.52474157).
文摘Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.
基金supported by the National Natural Science Foundation of China(Grant No.52374156).
文摘To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.
基金support from the National Natural Science Foundation of China(Grant No.42407256)the State Key Laboratory of Hydraulics and Mountain River Engineering,China(Grant No.SKHL2113)the Sichuan Science and Technology Program(Grant No.2024YFHZ0341).
文摘This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.
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.
文摘When a porous rock is subjected to overall compressive loading,either increasing pore pressure or decreasing confining pressure could result in rock failure.The stress path and the applied pressure change rate may affect the initiation and propagation of fractures within brittle materials.Understanding the physical mechanisms leading to failure is crucial for underground engineering applications and geo-energy exploration and storage.We conducted triaxial compression experiments on porous Bentheim sandstone samples at different stress paths and pressure change rates.First,at a constant confining pressure of 35 MPa and pore pressure of 5 MPa,intact cylindrical samples were axially loaded up to about 85%of the peak strength.Subsequently,the axial piston position was fixed,and then either the pore pressure was increased or the confining pressure was decreased at two different rates(0.5 MPa/min or 2 MPa/min),leading to final catastrophic failure.The mechanical results revealed that samples subjected to higher rates of decreasing effective confining pressure exhibited larger stress drop rates,higher slip rates,higher total breakdown work,higher rates of acoustic emissions(AEs)before failure,and higher post-failure AE decay rates.In contrast,the applied stress path did not significantly affect rock failure characteristics.Comparison of located AE events with post-mortem microstructures of deformed samples shows a good agreement.The AE source type determined from the P-wave first-motion polarity shows that shear failure dominated the fracture process when approaching failure.Gutenberg-Richter b-values revealed a significant decrease before failure in all tests.Our results indicate that,in contrast to the stress path,the rate of effective stress change strongly affects fracturing behavior and AE rate changes.
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In recent years,unmanned aerial vehicles(UAVs)cooperative path planning is attracting more and more research attention.For the multi-UAV cooperative path planning problem,the path planning problem in three-dimensional(3D)environment is transformed into an optimization problem by introducing the fitness function and constraints such as minimizing path length,maintaining a low and stable flight altitude,and avoiding threat zones.A multi-strategy hybrid grey wolf optimization(MSHGWO)algorithm is proposed to address this problem.Firstly,a chaotic Cubic mapping is introduced to initialize the grey wolf positions to make its initial position distribution more uniform.Secondly,an adaptive adjustment weight factor is designed,which can adjust the movement weight based on the rate of fitness value decrease within a unit Euclidean distance,thereby improving the quality of the population.Finally,an elite opposition-based learning strategy is introduced to improve the population diversity so that the population jumps out of the local optimum.Simulation results indicate that the MSHGWO is capable of generating constraint-compliant paths for each UAV in complex 3D environments.Furthermore,the MSHGWO outperforms other algorithms in terms of convergence speed and solution quality.Meanwhile,flight experiments were conducted to validate the path planning capability of MSHGWO in real-world obstacle environments,further demonstrating the feasibility of the proposed multi-UAV cooperative path planning approach.
基金Philosophy and Social Sciences Research Project of Shandong Higher Education Institutions:“Research on the Double Helix Mechanism of Yellow River Culture Empowering Ideological and Political Education in Universities from the Perspective of Cultural Confidence Cultivation”(2025ZSYB077)Youth Key Project of Shandong Humanities and Social Sciences Research Project,“Research on Integrating Yellow River Culture into the Cultivation of University Students’Cultural Confidence”Shandong Higher Education Institutions Young Innovation Team Program:“Yellow River Delta Ecological Protection and Governance Innovation Team”(2023RW036).
文摘Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow River Culture”with“Cultural Confidence”.It proposes an integrated“Objective-Content-Path-Support”framework.Through the synergy of three-dimensional objectives,adaptation of stratified content,innovation in four-dimensional pathways,and support from a three-dimensional guarantee system,this framework establishes a closed-loop operational mechanism of“Curriculum-Practice-Evaluation-Feedback”.The study focuses on core issues in integrating Yellow River culture into university education practices,such as content construction,methodological pathways,and institutional guarantees.It aims to provide a systematic reference for universities to fulfill their fundamental task of“fostering virtue and cultivating talent”and to serve the national strategies for ecological protection and high-quality development in the Yellow River Basin.
文摘Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.
基金funding from the National Natural Science Foundation of China(22378289)the Key Central Government Guides Local Funds for Science and Technology Development(YDZJSX2022A021)the special fund for Science and Technology Innovation Teams of Shanxi Province(202304051001026)。
文摘The oxygen evolution reaction(OER)suffers from sluggish kinetics,necessitating efficient electrocatalysts to reduce overpotentials in water splitting.Currently recognized OER mechanisms primarily include the adsorbate evolution mechanism(AEM),lattice oxygen mechanism(LOM),and oxide path mechanism(OPM).Compared to AEM,limited by scaling relationships,and LOM,constrained by stability issues,the OPM offers a promising alternative by enabling direct O-O bond formation via dual active sites,thus bypassing^(*)OOH intermediates and lattice O involvement and achieving a balance between activity and durability.However,activating the OPM process requires precise control over the spatial and electronic structure of active sites,making the design of OPM-based catalysts challenging.While previous reviews have focused on homo/heteronuclear diatomic perspectives of OPM-based catalysts,it is urgent to systematically summarize design strategies to provide a rational reference for their development.Herein,a review of design strategies for OPM-based OER catalysts across three scales is comprehensively presented,including in-situ engineering,doping-enabled sites reconstruction,and introducing new sites for nanoparticles,direct synthesis or post-treatments for molecular catalysts,and doping or template strategies for atom pairs or arrays.The unique advantage of atom arrays is also highlighted,and their future research directions and possible strategies are discussed.This review provides a systematic summary and forward-looking perspectives for rationally designing high-performance OPM-based OER catalysts.
基金supported by the open project of National Key Laboratory of Air-Based Information Perception and Fusion(No.202462)。
文摘To address real-time path planning requirements for multi-unmanned aerial vehicle(multi-UAV)collaboration in environments,this study proposes an improved multi-agent deep deterministic policy gradient algorithm with prioritized experience replay(PER-MADDPG).By designing a multi-dimensional state representation incorporating relative positions,velocity vectors,and obstacle distance fields,we construct a composite reward function integrating safe obstacle avoidance,formation maintenance,and energy efficiency for environment perception and multiobjective collaborative optimization.The prioritized experience replay mechanism dynamically adjusts sampling weights based on temporal difference(TD)errors,enhancing learning efficiency for high-value samples.Simulation experiments demonstrate that our method generates real-time collaborative paths in 3D complex obstacle environments,reducing training time by 25.3%and 16.8%compared to traditional MADDPG and multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithms respectively,while achieving smaller path length variances among UAVs.Results validate the effectiveness of prioritized experience replay in multi-agent collaborative decision-making.
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 Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
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.
