Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles.In this paper,we propose a novel path pl...Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles.In this paper,we propose a novel path planning algorithm-Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer,Particle swarm optimization(PSO),for fine-tuning and enhancement.In Phase 1,the start and goal trees are initialized at the starting and goal positions,respectively,and the intermediary tree is initialized at a random unexplored region of the search space.The trees were grown until one met the other and then merged and re-initialized in other unexplored regions.If the start and goal trees merge,the first solution is found and passed through a minimization process to reduce unnecessary nodes.Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path.After simulating two special benchmark configurations and six practice configurations with special cases,the results of the study concluded that the proposed method is capable of handling small to large,simple to complex continuous environments,whereas it was very tedious for the previous method to achieve.展开更多
The feature map is a characteristic of high computational efficiency,but it is seldom used in path planning due to its lack of expression of environmental details.To solve this problem,a global path planning algorithm...The feature map is a characteristic of high computational efficiency,but it is seldom used in path planning due to its lack of expression of environmental details.To solve this problem,a global path planning algorithm based on the feature map is proposed based on the directionality of line segment features.First,the robot searches the path along the direction of the target position but turns to search in the direction parallel to the obstacle,which it approaches until the line between the robot and the target position does not intersect with obstacles.Then it turns to the target position,keep searching the path.Meanwhile,the problems of the direction selection of turning point,corner point and obstacle circumvention in the searching process are analysed and corresponding solutions are put forth.Finally,a path optimisation algorithm with variable parameters is proposed,making the optimised path shorter and smoother.Simulation experiments demonstrate that the proposed algorithm is superior to A*algorithm in terms of computation time and path length,especially of the computation efficiency.展开更多
Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in thes...Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.展开更多
Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,tradition...Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive ability.As a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment adaptability.To improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation method.First,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition.And then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short distance.Moreover,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence level.Finally,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes number.The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.展开更多
基金funded by International University,VNU-HCM under Grant Number T2021-02-IEM.
文摘Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles.In this paper,we propose a novel path planning algorithm-Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer,Particle swarm optimization(PSO),for fine-tuning and enhancement.In Phase 1,the start and goal trees are initialized at the starting and goal positions,respectively,and the intermediary tree is initialized at a random unexplored region of the search space.The trees were grown until one met the other and then merged and re-initialized in other unexplored regions.If the start and goal trees merge,the first solution is found and passed through a minimization process to reduce unnecessary nodes.Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path.After simulating two special benchmark configurations and six practice configurations with special cases,the results of the study concluded that the proposed method is capable of handling small to large,simple to complex continuous environments,whereas it was very tedious for the previous method to achieve.
基金National Natural Science Foundation of China,Grant/Award Number:61803246。
文摘The feature map is a characteristic of high computational efficiency,but it is seldom used in path planning due to its lack of expression of environmental details.To solve this problem,a global path planning algorithm based on the feature map is proposed based on the directionality of line segment features.First,the robot searches the path along the direction of the target position but turns to search in the direction parallel to the obstacle,which it approaches until the line between the robot and the target position does not intersect with obstacles.Then it turns to the target position,keep searching the path.Meanwhile,the problems of the direction selection of turning point,corner point and obstacle circumvention in the searching process are analysed and corresponding solutions are put forth.Finally,a path optimisation algorithm with variable parameters is proposed,making the optimised path shorter and smoother.Simulation experiments demonstrate that the proposed algorithm is superior to A*algorithm in terms of computation time and path length,especially of the computation efficiency.
基金Supported by the Natural Science Foundation of Jiangsu Province (BK20211037)the Science and Technology Development Fund of Wuxi (N20201011)the Nanjing University of Information Science and Technology Wuxi Campus District graduate innovation Project。
文摘Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.
基金funded by the National Natural Science Foundation of China-Liaoning Joint Fund(Grants:U20A20197)the National Natural Science Foundation of China(Grants:62173064)the Fundamental Research Funds for the Central Universities(Grants:N2326005).
文摘Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive ability.As a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment adaptability.To improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation method.First,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition.And then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short distance.Moreover,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence level.Finally,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes number.The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.
