With the accelerating urbanization process,the load demand of urban power grids is constantly increasing,giving rise to a batch of ultra-large urban power grids featuring large electricity demand,dense load distributi...With the accelerating urbanization process,the load demand of urban power grids is constantly increasing,giving rise to a batch of ultra-large urban power grids featuring large electricity demand,dense load distribution,and tight construction land constraints.This paper establishes a network planning method for urban power grids based on series reactors and MMC-MTEDC,focusing on four aspects:short-circuit current suppression,accommodation of external power supply,flexible inter-regional power support,and voltage stability enhancement in load centers.It proposes key indicators including node short-circuit current margin,line thermal stability margin,maximum fault-induced regional power loss,and voltage recovery time,thereby constructing an evaluation system for MMT-MTEDC network planning in urban power grids.Based on the Shenzhen power grid planning data,simulations using DSP software reveal that series reactors reduce short-circuit current by up to 5.0%,while the MMC-MTEDC system enhances node short-circuit margins by 4.212.9%and shortens voltage recovery time by 19.8%.Additionally,the MMC-MTEDC system maintains 3.34-6.76 percentage points higher thermal stability margins than conventional AC systems and enables complete avoidance of external power curtailment during N-2 faults via power reallocation between terminals.Compared with traditional AC or point-to-point HVDC schemes,the proposed hybrid planning method better adapts to the spatial and reliability demands of ultra-large receiving-end grids.This methodology provides practical insights into coordinated AC/DC development under high load density and strong external power reliance.Future work will extend the approach to include electromagnetic transient constraints and lightweight MMC station designs for urban applications.展开更多
A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path...A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocityobstacle strategy for satisfying timeliness and effectiveness.The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing.Subsequently,the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the threedimensional velocity obstacle method,which describes the maneuvering space of collision avoidance as the intersection space of half space.To further handle the dynamic and collisionavoidance constraints,a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs.Simulation experiments demonstrate the effectiveness of the proposed method.展开更多
Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial ...Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.展开更多
Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity.To comprehensively analyze this scheme,maneuvering sc...Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity.To comprehensively analyze this scheme,maneuvering schemes for maximum debris removal with minimum fuel consumption,including task assignment,sequence planning,and trajectory planning,must be formulated.The coupling between variables’dimensions and optimization results in task assignment poses challenges,as debris removal is repetitive and uncertain,leading to a vast search space.This paper proposes a novel Greedy Randomized Adaptive Search Procedure with Large Neighborhood and Crossover Mechanisms(GRASP-LNCM)to address this problem.The hybrid dynamic iteration mechanism improves computational efficiency and enhances the optimality of results.The model innovatively considers unsuccessful single removal by using a quantitative method to assess removal percentage.In addition,to improve the efficiency of sequence and trajectory planning,a Suboptimal Search Algorithm(SSA)based on the Lambert property and accelerated Multi-Revolution Lambert Problem(MRLP)solving strategy is established.Finally,a real Iridium-33 debris removal mission is studied.The simulation demonstrates that the proposed algorithm achieves state-of-the-art performance in several typical scenarios.Compared to the contact-based scheme,the new one is simpler,saving more fuel under certain conditions.展开更多
To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a...To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.展开更多
This paper introduces an Improved Bidirectional Jump Point Search(I-BJPS)algorithm to address the challenges of the traditional Jump Point Search(JPS)in mobile robot path planning.These challenges include excessive no...This paper introduces an Improved Bidirectional Jump Point Search(I-BJPS)algorithm to address the challenges of the traditional Jump Point Search(JPS)in mobile robot path planning.These challenges include excessive node expansions,frequent path inflexion points,slower search times,and a high number of jump points in complex environments with large areas and dense obstacles.Firstly,we improve the heuristic functions in both forward and reverse directions to minimize expansion nodes and search time.We also introduce a node optimization strategy to reduce non-essential nodes so that the path length is optimized.Secondly,we employ a second-order Bezier Curve to smooth turning points,making generated paths more suitable for mobile robot motion requirements.Then,we integrate the Dynamic Window Approach(DWA)to improve path planning safety.Finally,the simulation results demonstrate that the I-BJPS algorithm significantly outperforms both the original unidirectional JPS algorithm and the bidirectional JPS algorithm in terms of search time,the number of path inflexion points,and overall path length,the advantages of the I-BJPS algorithm are particularly pronounced in complex environments.Experimental results from real-world scenarios indicate that the proposed algorithm can efficiently and rapidly generate an optimal path that is safe,collision-free,and well-suited to the robot’s locomotion requirements.展开更多
Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction ...Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction of production.In this new method,the annual production of old and new wells is predicted by year first and then is summed up to yield the production for the planning period.It shows that the changes in the production of old wells in old blocks can be fitted and predicted using the vapor pressure model(VPM),with precision of 80%e95%,which is 6.6%e13.2%higher than that of other life cycle models.Furthermore,a new production prediction process and method for new wells have been established based on this life cycle model to predict the production of medium-to-shallow gas reservoirs in western Sichuan Basin,with predication error of production rate in 2021 and 2022 being 6%and 3%respectively.The new method can be used to guide the medium-and long-term planning or annual scheme preparation for gas development.It is also applicable to planning for large single gas blocks that require continuous infill drilling and adjustment to improve gas recovery.展开更多
A new dynamic path planning method in high dimensional workspace, radial based probabilistic roadmap motion (RBPRM) planning method, is presented. Different from general probabilistic roadmap motion planning methods, ...A new dynamic path planning method in high dimensional workspace, radial based probabilistic roadmap motion (RBPRM) planning method, is presented. Different from general probabilistic roadmap motion planning methods, it uses straight lines as long as possible to construct a path graph, so the final path obtained from the graph is relatively shorter and straighter. Experimental results show the efficiency of the algorithm in finding shorter paths in sparse environment.展开更多
The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains thr...The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.展开更多
Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a ...Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a suitable method to solve the UAV Autonomous Motion Planning(AMP)problem can improve the success rate of UAV missions to a certain extent.In recent years,many studies have used Deep Reinforcement Learning(DRL)methods to address the AMP problem and have achieved good results.From the perspective of sampling,this paper designs a sampling method with double-screening,combines it with the Deep Deterministic Policy Gradient(DDPG)algorithm,and proposes the Relevant Experience Learning-DDPG(REL-DDPG)algorithm.The REL-DDPG algorithm uses a Prioritized Experience Replay(PER)mechanism to break the correlation of continuous experiences in the experience pool,finds the experiences most similar to the current state to learn according to the theory in human education,and expands the influence of the learning process on action selection at the current state.All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV.The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm,while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.展开更多
This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits...This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.展开更多
To improve the survivability during an emergency situation, an algorithm for aircraft forced landing trajectory planning is proposed. The method integrates damaged aircraft modelling and trajectory planning into an op...To improve the survivability during an emergency situation, an algorithm for aircraft forced landing trajectory planning is proposed. The method integrates damaged aircraft modelling and trajectory planning into an optimal control framework, in order to deal with the complex aircraft flight dynamics, a solving strategy based on Gauss pseudospetral method （GPM） is presented. A 3-DOF nonlinear mass-point model taking into account the wind is developed to approximate the aircraft flight dynamics after loss of thrust. The solution minimizes the forced landing duration, with respect to the constraints that translate the changed dynamics, flight envelope limitation and operational safety requirements. The GPM is used to convert the trajectory planning problem to a nonlinear programming problem （NLP）, which is solved by sequential quadratic programming algorithm. Simulation results show that the proposed algorithm can generate the minimum-time forced landing trajectory in event of engine-out with high efficiency and precision.展开更多
To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots(CSPRs),point-to-point trajectory planning demands an und...To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots(CSPRs),point-to-point trajectory planning demands an under-constrained cable-suspended parallel robot(UCPR)with variable angle and height cable mast as described in this paper.The end-effector of the UCPR with three cables can achieve three translational degrees of freedom(DOFs).The inverse kinematic and dynamic modeling of the UCPR considering the angle and height of cable mast are completed.The motion trajectory of the end-effector comprising six segments is given.The connection points of the trajectory segments(except for point P3 in the X direction)are devised to have zero instantaneous velocities,which ensure that the acceleration has continuity and the planned acceleration curve achieves smooth transition.The trajectory is respectively planned using three algebraic methods,including fifth degree polynomial,cycloid trajectory,and double-S velocity curve.The results indicate that the trajectory planned by fifth degree polynomial method is much closer to the given trajectory of the end-effector.Numerical simulation and experiments are accomplished for the given trajectory based on fifth degree polynomial planning.At the points where the velocity suddenly changes,the length and tension variation curves of the planned and unplanned three cables are compared and analyzed.The OptiTrack motion capture system is adopted to track the end-effector of the UCPR during the experiment.The effectiveness and feasibility of fifth degree polynomial planning are validated.展开更多
By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning...By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.展开更多
Introduction: Postpartum family planning is the prevention of pregnancies during the 12 months following childbirth. Few studies have been devoted to postpartum family planning in Mali. Our work will contribute to red...Introduction: Postpartum family planning is the prevention of pregnancies during the 12 months following childbirth. Few studies have been devoted to postpartum family planning in Mali. Our work will contribute to reducing unmet need for family planning. Objective: To study the use of contraceptive methods in the postpartum period in the obstetrics and gynecology unit of Timbuktu hospital. Materials and Methods: This was a descriptive and analytical cross-sectional study with prospective collection of data from January 1, 2022 to December 31, 2023. All women who gave birth having chosen and benefited from a contraceptive method were included. The statistical test used was the Fisher test with a significance threshold fixed at 5%. Results: The frequency of contraception in the postpartum period was 17.03%. The average age of clients was 26.14% with extremes of 14 and 45 years. They were paupiparous at 56.4% with an inter-birth interval of less than 12 months at 12.3%. More than half of the counseling (58.5%) was done during postnatal visits. The methods chosen were implants at 48.1%, injectable progestins at 21.3%, intrauterine device at 14.7%, miro-progestin pills at 13.5%, tubal ligation at 1 .4% and condoms at 1%. The regular follow-up rate was 51.1% of cases and 25.6% had no follow-up. Conclusion: The overall rate of postpartum family planning of 17.08% remains low. Improving FP staff skills will reduce unmet needs and contribute to increasing contraceptive prevalence in Timbuktu.展开更多
In this paper we present two strategies of AUV (Autonomous Underwater Vehicle) region detection and an approach to decompose the detection region according to the direction of the ocean current. In the task of local d...In this paper we present two strategies of AUV (Autonomous Underwater Vehicle) region detection and an approach to decompose the detection region according to the direction of the ocean current. In the task of local detection and identification, the algorithm against the ocean current was proposed. In the tasks of closing obstacle, going back or moving, the fuzzy logic theory was used to solve the effect of ocean current. In one of our strategies the concept of weighted journey based on the angle between heading and ocean current is suggested and the TSP's exact optimal result is utilized to solve the global path planning. Simulations demonstrate the feasibility of this approach.展开更多
Autonomous-rail rapid transit(ART)is a new medium-capacity rapid transportation system with punctuality,comfort and convenience,but low-cost construction.Combined velocity planning is a critical approach to meet the r...Autonomous-rail rapid transit(ART)is a new medium-capacity rapid transportation system with punctuality,comfort and convenience,but low-cost construction.Combined velocity planning is a critical approach to meet the requirements of energy-saving and punctuality.An ART velocity pre-planning and re-planning strategy based on the combination of punctuality dynamic programming(PDP)and pseudospectral(PS)method is proposed in this paper.Firstly,the longitudinal dynamics model of ART is established by a multi-particle model.Secondly,the PDP algorithm with global optimal characteristics is adopted as the pre-planning strategy.A model for determining the number of collocation points of the real-time PS method is proposed to improve the energy-saving effect while ensuring computation efficiency.Then the enhanced PS method is utilized to design the velocity re-planning strategy.Finally,simulations are conducted in the typical scenario with sloping roads,traffic lights,and intrusion of the pedestrian.The simulation results indicate that the ART with the proposed velocity trajectory optimization strategy can meet the punctuality requirement,and obtain better economy efficiency compared with the punctuality green light optimal speed advisory(PGLOSA).展开更多
The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure,small amount of calculation and relatively mature in theory.This paper puts forward the&...The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure,small amount of calculation and relatively mature in theory.This paper puts forward the"Integral method"focusing on solving the problem of local minimization.The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole,By changing the parameters of the repulsive force field,robots can quickly get out of the minimum point and move to the target point.This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method,which projects a feasible path successfully and verifies this method.展开更多
The spinors applied to describe position and attitude of robot are studied. In dual spaces, the terminal trace of robot is planned through the mapping point, of attitude spinors. As a handy method directly perceived t...The spinors applied to describe position and attitude of robot are studied. In dual spaces, the terminal trace of robot is planned through the mapping point, of attitude spinors. As a handy method directly perceived through the sense, the spinor method directly converges tracking error in the planning. It promotes the dynamic accuracy of trace operation. It is also suitable to the exerciser with redundant freedom.展开更多
An optimal motion planning scheme based on the quasi-Newton method is proposed for a rigid spacecraft with two momentum wheels. A cost functional is introduced to incorporate the control energy, the final state errors...An optimal motion planning scheme based on the quasi-Newton method is proposed for a rigid spacecraft with two momentum wheels. A cost functional is introduced to incorporate the control energy, the final state errors and the constraints on states. The motion planning for determining control inputs to minimize the cost functional is formulated as a nonlinear optimal control problem. Using the control parametrization, one can transform the infinite dimensional optimal control problem to a finite dimensional one that is solved via the quasi-Newton methods for a feasible trajectory which satisfies the nonholonomic constraint. The optimal motion planning scheme was applied to a rigid spacecraft with two momentum wheels. The simulation results show the effectiveness of the proposed optimal motion planning scheme.展开更多
基金Shenzhen Power SupplyCo.,Ltd.Grant number 090000KC24040028.
文摘With the accelerating urbanization process,the load demand of urban power grids is constantly increasing,giving rise to a batch of ultra-large urban power grids featuring large electricity demand,dense load distribution,and tight construction land constraints.This paper establishes a network planning method for urban power grids based on series reactors and MMC-MTEDC,focusing on four aspects:short-circuit current suppression,accommodation of external power supply,flexible inter-regional power support,and voltage stability enhancement in load centers.It proposes key indicators including node short-circuit current margin,line thermal stability margin,maximum fault-induced regional power loss,and voltage recovery time,thereby constructing an evaluation system for MMT-MTEDC network planning in urban power grids.Based on the Shenzhen power grid planning data,simulations using DSP software reveal that series reactors reduce short-circuit current by up to 5.0%,while the MMC-MTEDC system enhances node short-circuit margins by 4.212.9%and shortens voltage recovery time by 19.8%.Additionally,the MMC-MTEDC system maintains 3.34-6.76 percentage points higher thermal stability margins than conventional AC systems and enables complete avoidance of external power curtailment during N-2 faults via power reallocation between terminals.Compared with traditional AC or point-to-point HVDC schemes,the proposed hybrid planning method better adapts to the spatial and reliability demands of ultra-large receiving-end grids.This methodology provides practical insights into coordinated AC/DC development under high load density and strong external power reliance.Future work will extend the approach to include electromagnetic transient constraints and lightweight MMC station designs for urban applications.
基金supported by the National Science Fund for Distinguished Young Scholars(52425211)BIT Research Fund Program for Young Scholars(XSQD-202201005).
文摘A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocityobstacle strategy for satisfying timeliness and effectiveness.The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing.Subsequently,the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the threedimensional velocity obstacle method,which describes the maneuvering space of collision avoidance as the intersection space of half space.To further handle the dynamic and collisionavoidance constraints,a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs.Simulation experiments demonstrate the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China (Grant No. 52071097)Hainan Provincial Natural Science Foundation of China (Grant No. 522MS162)Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (Grant No. 2021JCJQ-SYSJJ-LB06910)。
文摘Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.
基金co-supported by the National Natural Science Foundation of China(Nos.U23B6001,62273118,12150008)the Fundamental Research Funds for the Central Universities,China(No.2023FRFK02043)+1 种基金the Natural Science Foundation of Heilongjiang Province,China(No.LH2022F023)China Aerospace Science and Technology Corporation Youth Talent Support Program.
文摘Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity.To comprehensively analyze this scheme,maneuvering schemes for maximum debris removal with minimum fuel consumption,including task assignment,sequence planning,and trajectory planning,must be formulated.The coupling between variables’dimensions and optimization results in task assignment poses challenges,as debris removal is repetitive and uncertain,leading to a vast search space.This paper proposes a novel Greedy Randomized Adaptive Search Procedure with Large Neighborhood and Crossover Mechanisms(GRASP-LNCM)to address this problem.The hybrid dynamic iteration mechanism improves computational efficiency and enhances the optimality of results.The model innovatively considers unsuccessful single removal by using a quantitative method to assess removal percentage.In addition,to improve the efficiency of sequence and trajectory planning,a Suboptimal Search Algorithm(SSA)based on the Lambert property and accelerated Multi-Revolution Lambert Problem(MRLP)solving strategy is established.Finally,a real Iridium-33 debris removal mission is studied.The simulation demonstrates that the proposed algorithm achieves state-of-the-art performance in several typical scenarios.Compared to the contact-based scheme,the new one is simpler,saving more fuel under certain conditions.
基金The National Natural Science Foundation of China(No.62272239,62303214)Jiangsu Agricultural Science and Tech-nology Independent Innovation Fund(No.SJ222051).
文摘To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.
基金supported by the Xinjiang Uygur Autonomous Region Central Guided Local Science and Technology Development Fund Project(No.ZYYD2025QY17).
文摘This paper introduces an Improved Bidirectional Jump Point Search(I-BJPS)algorithm to address the challenges of the traditional Jump Point Search(JPS)in mobile robot path planning.These challenges include excessive node expansions,frequent path inflexion points,slower search times,and a high number of jump points in complex environments with large areas and dense obstacles.Firstly,we improve the heuristic functions in both forward and reverse directions to minimize expansion nodes and search time.We also introduce a node optimization strategy to reduce non-essential nodes so that the path length is optimized.Secondly,we employ a second-order Bezier Curve to smooth turning points,making generated paths more suitable for mobile robot motion requirements.Then,we integrate the Dynamic Window Approach(DWA)to improve path planning safety.Finally,the simulation results demonstrate that the I-BJPS algorithm significantly outperforms both the original unidirectional JPS algorithm and the bidirectional JPS algorithm in terms of search time,the number of path inflexion points,and overall path length,the advantages of the I-BJPS algorithm are particularly pronounced in complex environments.Experimental results from real-world scenarios indicate that the proposed algorithm can efficiently and rapidly generate an optimal path that is safe,collision-free,and well-suited to the robot’s locomotion requirements.
基金funded by the project entitled Technical Countermeasures for the Quantitative Characterization and Adjustment of Residual Gas in Tight Sandstone Gas Reservoirs of the Daniudi Gas Field(P20065-1)organized by the Science&Technology R&D Department of Sinopec.
文摘Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction of production.In this new method,the annual production of old and new wells is predicted by year first and then is summed up to yield the production for the planning period.It shows that the changes in the production of old wells in old blocks can be fitted and predicted using the vapor pressure model(VPM),with precision of 80%e95%,which is 6.6%e13.2%higher than that of other life cycle models.Furthermore,a new production prediction process and method for new wells have been established based on this life cycle model to predict the production of medium-to-shallow gas reservoirs in western Sichuan Basin,with predication error of production rate in 2021 and 2022 being 6%and 3%respectively.The new method can be used to guide the medium-and long-term planning or annual scheme preparation for gas development.It is also applicable to planning for large single gas blocks that require continuous infill drilling and adjustment to improve gas recovery.
文摘A new dynamic path planning method in high dimensional workspace, radial based probabilistic roadmap motion (RBPRM) planning method, is presented. Different from general probabilistic roadmap motion planning methods, it uses straight lines as long as possible to construct a path graph, so the final path obtained from the graph is relatively shorter and straighter. Experimental results show the efficiency of the algorithm in finding shorter paths in sparse environment.
基金financially supported by the National Natural Science Foundation of China(Grant No.51879049)DK-I Dynamic Positioning System Console Project
文摘The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
基金co-supported by the National Natural Science Foundation of China(Nos.62003267,61573285)the Aeronautical Science Foundation of China(ASFC)(No.20175553027)Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JQ-220)。
文摘Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a suitable method to solve the UAV Autonomous Motion Planning(AMP)problem can improve the success rate of UAV missions to a certain extent.In recent years,many studies have used Deep Reinforcement Learning(DRL)methods to address the AMP problem and have achieved good results.From the perspective of sampling,this paper designs a sampling method with double-screening,combines it with the Deep Deterministic Policy Gradient(DDPG)algorithm,and proposes the Relevant Experience Learning-DDPG(REL-DDPG)algorithm.The REL-DDPG algorithm uses a Prioritized Experience Replay(PER)mechanism to break the correlation of continuous experiences in the experience pool,finds the experiences most similar to the current state to learn according to the theory in human education,and expands the influence of the learning process on action selection at the current state.All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV.The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm,while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.
基金supported by the National Defense Foundation of China(No.403060103)
文摘This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.
基金supported by the National Key Basic Research Program of China(973 Program)(No.2011CB707002)
文摘To improve the survivability during an emergency situation, an algorithm for aircraft forced landing trajectory planning is proposed. The method integrates damaged aircraft modelling and trajectory planning into an optimal control framework, in order to deal with the complex aircraft flight dynamics, a solving strategy based on Gauss pseudospetral method （GPM） is presented. A 3-DOF nonlinear mass-point model taking into account the wind is developed to approximate the aircraft flight dynamics after loss of thrust. The solution minimizes the forced landing duration, with respect to the constraints that translate the changed dynamics, flight envelope limitation and operational safety requirements. The GPM is used to convert the trajectory planning problem to a nonlinear programming problem （NLP）, which is solved by sequential quadratic programming algorithm. Simulation results show that the proposed algorithm can generate the minimum-time forced landing trajectory in event of engine-out with high efficiency and precision.
基金National Natural Science Foundation of China(Grant Nos.51925502,51575150).
文摘To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots(CSPRs),point-to-point trajectory planning demands an under-constrained cable-suspended parallel robot(UCPR)with variable angle and height cable mast as described in this paper.The end-effector of the UCPR with three cables can achieve three translational degrees of freedom(DOFs).The inverse kinematic and dynamic modeling of the UCPR considering the angle and height of cable mast are completed.The motion trajectory of the end-effector comprising six segments is given.The connection points of the trajectory segments(except for point P3 in the X direction)are devised to have zero instantaneous velocities,which ensure that the acceleration has continuity and the planned acceleration curve achieves smooth transition.The trajectory is respectively planned using three algebraic methods,including fifth degree polynomial,cycloid trajectory,and double-S velocity curve.The results indicate that the trajectory planned by fifth degree polynomial method is much closer to the given trajectory of the end-effector.Numerical simulation and experiments are accomplished for the given trajectory based on fifth degree polynomial planning.At the points where the velocity suddenly changes,the length and tension variation curves of the planned and unplanned three cables are compared and analyzed.The OptiTrack motion capture system is adopted to track the end-effector of the UCPR during the experiment.The effectiveness and feasibility of fifth degree polynomial planning are validated.
基金funded by National Natural Science Foundation of China(No.62063006)Guangxi Science and Technology Major Program(No.2022AA05002)+1 种基金Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region(No.2022GXZDSY003)Central Leading Local Science and Technology Development Fund Project of Wuzhou(No.202201001).
文摘By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.
文摘Introduction: Postpartum family planning is the prevention of pregnancies during the 12 months following childbirth. Few studies have been devoted to postpartum family planning in Mali. Our work will contribute to reducing unmet need for family planning. Objective: To study the use of contraceptive methods in the postpartum period in the obstetrics and gynecology unit of Timbuktu hospital. Materials and Methods: This was a descriptive and analytical cross-sectional study with prospective collection of data from January 1, 2022 to December 31, 2023. All women who gave birth having chosen and benefited from a contraceptive method were included. The statistical test used was the Fisher test with a significance threshold fixed at 5%. Results: The frequency of contraception in the postpartum period was 17.03%. The average age of clients was 26.14% with extremes of 14 and 45 years. They were paupiparous at 56.4% with an inter-birth interval of less than 12 months at 12.3%. More than half of the counseling (58.5%) was done during postnatal visits. The methods chosen were implants at 48.1%, injectable progestins at 21.3%, intrauterine device at 14.7%, miro-progestin pills at 13.5%, tubal ligation at 1 .4% and condoms at 1%. The regular follow-up rate was 51.1% of cases and 25.6% had no follow-up. Conclusion: The overall rate of postpartum family planning of 17.08% remains low. Improving FP staff skills will reduce unmet needs and contribute to increasing contraceptive prevalence in Timbuktu.
基金Supported by the Research Fund for the Doctoral Program of Higher Education from the Ministry of Education
文摘In this paper we present two strategies of AUV (Autonomous Underwater Vehicle) region detection and an approach to decompose the detection region according to the direction of the ocean current. In the task of local detection and identification, the algorithm against the ocean current was proposed. In the tasks of closing obstacle, going back or moving, the fuzzy logic theory was used to solve the effect of ocean current. In one of our strategies the concept of weighted journey based on the angle between heading and ocean current is suggested and the TSP's exact optimal result is utilized to solve the global path planning. Simulations demonstrate the feasibility of this approach.
基金Supported by National Natural Science Foundation of China(Grant Nos.52072073 and 52025121)National Key R&D Program of China(Grant No.2018YFB1201602).
文摘Autonomous-rail rapid transit(ART)is a new medium-capacity rapid transportation system with punctuality,comfort and convenience,but low-cost construction.Combined velocity planning is a critical approach to meet the requirements of energy-saving and punctuality.An ART velocity pre-planning and re-planning strategy based on the combination of punctuality dynamic programming(PDP)and pseudospectral(PS)method is proposed in this paper.Firstly,the longitudinal dynamics model of ART is established by a multi-particle model.Secondly,the PDP algorithm with global optimal characteristics is adopted as the pre-planning strategy.A model for determining the number of collocation points of the real-time PS method is proposed to improve the energy-saving effect while ensuring computation efficiency.Then the enhanced PS method is utilized to design the velocity re-planning strategy.Finally,simulations are conducted in the typical scenario with sloping roads,traffic lights,and intrusion of the pedestrian.The simulation results indicate that the ART with the proposed velocity trajectory optimization strategy can meet the punctuality requirement,and obtain better economy efficiency compared with the punctuality green light optimal speed advisory(PGLOSA).
文摘The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure,small amount of calculation and relatively mature in theory.This paper puts forward the"Integral method"focusing on solving the problem of local minimization.The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole,By changing the parameters of the repulsive force field,robots can quickly get out of the minimum point and move to the target point.This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method,which projects a feasible path successfully and verifies this method.
文摘The spinors applied to describe position and attitude of robot are studied. In dual spaces, the terminal trace of robot is planned through the mapping point, of attitude spinors. As a handy method directly perceived through the sense, the spinor method directly converges tracking error in the planning. It promotes the dynamic accuracy of trace operation. It is also suitable to the exerciser with redundant freedom.
基金Project supported by the National Natural Science Foundation of China (No. 10372014).
文摘An optimal motion planning scheme based on the quasi-Newton method is proposed for a rigid spacecraft with two momentum wheels. A cost functional is introduced to incorporate the control energy, the final state errors and the constraints on states. The motion planning for determining control inputs to minimize the cost functional is formulated as a nonlinear optimal control problem. Using the control parametrization, one can transform the infinite dimensional optimal control problem to a finite dimensional one that is solved via the quasi-Newton methods for a feasible trajectory which satisfies the nonholonomic constraint. The optimal motion planning scheme was applied to a rigid spacecraft with two momentum wheels. The simulation results show the effectiveness of the proposed optimal motion planning scheme.
