A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit ...A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart(GFS RRT^(*)-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic(MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.展开更多
As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficien...As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficiency and inadequate collision-avoidance ability.To overcome these problems,a specific two-player path planner based on an improved algorithm is designed.First,by combing the artificial attractive field(AAF)of artificial potential field(APF)approach with the random rapidly exploring tree(RRT)algorithm,an improved AAF-RRT algorithm with a changing attractive force proportional to the Euler distance between the point to be extended and the goal point is proposed.Second,a twolayer path planner is designed with path smoothing,which combines global planning and local planning.Finally,as verified by the simulations,the improved AAF-RRT algorithm has the strongest searching ability and the ability to cross the narrow passage among the studied three algorithms,which are the basic RRT algorithm,the common AAF-RRT algorithm,and the improved AAF-RRT algorithm.Moreover,the two-layer path planner can plan a global and optimal path for AUVs if a sudden obstacle is added to the simulation environment.展开更多
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
Given the complexity of live working environments in power distribution networks,where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency,the Bidirectional Node-C...Given the complexity of live working environments in power distribution networks,where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency,the Bidirectional Node-Controlled Rapidly Exploring Random Tree(BNC-RRT)algorithm is proposed.This algorithm guides path search by progressively altering the sampling area and employs a node control mechanism to constrain the random tree expansion and extract effective boundary points.This approach reduces the number of ineffective nodes and collision checks during the search process,thereby enhancing path planning efficiency.Comparative simulation experiments conducted in various scenarios demonstrate that this algorithm re-duces the number of path nodes and improves planning efficiency compared to classical algorithms.Finally,real-world ex-periments on a live working robot developed by our team show that the proposed algorithm shortens the average path length by 8.6%,and reduces the average planning and movement times by 44.7%and 28.7%,respectively,compared to classical path planning algorithms.These results indicate that the algorithm effectively improves path planning efficiency and is suitable for live working tasks in the power distribution industry.展开更多
Given the complexity of live working environments in power distribution networks,where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency,the Bidirectional Node-C...Given the complexity of live working environments in power distribution networks,where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency,the Bidirectional Node-Controlled Rapidly Exploring Random Tree(BNC-RRT)algorithm is proposed.This algorithm guides path search by progressively altering the sampling area and employs a node control mechanism to constrain the random tree expansion and extract effective boundary points.This approach reduces the number of ineffective nodes and collision checks during the search process,thereby enhancing path planning efficiency.Comparative simulation experiments conducted in various scenarios demonstrate that this algorithm reduces the number of path nodes and improves planning efficiency compared to classical algorithms.Finally,real-world experiments on a live working robot developed by our team show that the proposed algorithm shortens the average path length by 8.6%,and reduces the average planning and movement times by 44.7%and 28.7%,respectively,compared to classical path planning algorithms.These results indicate that the algorithm effectively improves path planning efficiency and is suitable for live working tasks in the power distribution industry.展开更多
基金National Natural Science Foundation of China (No.61903078)。
文摘A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart(GFS RRT^(*)-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic(MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.
基金Supported by Zhejiang Key R&D Program 558 No.2021C03157the“Construction of a Leading Innovation Team”project by the Hangzhou Munic-559 ipal government,the Startup funding of New-joined PI of Westlake University with Grant No.560(041030150118)the funding support from the Westlake University and Bright Dream Joint In-561 stitute for Intelligent Robotics.
文摘As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficiency and inadequate collision-avoidance ability.To overcome these problems,a specific two-player path planner based on an improved algorithm is designed.First,by combing the artificial attractive field(AAF)of artificial potential field(APF)approach with the random rapidly exploring tree(RRT)algorithm,an improved AAF-RRT algorithm with a changing attractive force proportional to the Euler distance between the point to be extended and the goal point is proposed.Second,a twolayer path planner is designed with path smoothing,which combines global planning and local planning.Finally,as verified by the simulations,the improved AAF-RRT algorithm has the strongest searching ability and the ability to cross the narrow passage among the studied three algorithms,which are the basic RRT algorithm,the common AAF-RRT algorithm,and the improved AAF-RRT algorithm.Moreover,the two-layer path planner can plan a global and optimal path for AUVs if a sudden obstacle is added to the simulation environment.
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 Key Research and Development Programme of China,Grant/Award Numbers:2022YFB4703600Shandong Province Enterprise Innovation Capacity Improvement Project,Grant/Award Numbers:2023TSGC0442.
文摘Given the complexity of live working environments in power distribution networks,where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency,the Bidirectional Node-Controlled Rapidly Exploring Random Tree(BNC-RRT)algorithm is proposed.This algorithm guides path search by progressively altering the sampling area and employs a node control mechanism to constrain the random tree expansion and extract effective boundary points.This approach reduces the number of ineffective nodes and collision checks during the search process,thereby enhancing path planning efficiency.Comparative simulation experiments conducted in various scenarios demonstrate that this algorithm re-duces the number of path nodes and improves planning efficiency compared to classical algorithms.Finally,real-world ex-periments on a live working robot developed by our team show that the proposed algorithm shortens the average path length by 8.6%,and reduces the average planning and movement times by 44.7%and 28.7%,respectively,compared to classical path planning algorithms.These results indicate that the algorithm effectively improves path planning efficiency and is suitable for live working tasks in the power distribution industry.
基金supported in part by the National Key Research and Development Program of China(Grant No.2022YFB4703600)the Shandong Province Enterprise Innovation Capacity Improvement Project(Grant No.2023TSGC0442).
文摘Given the complexity of live working environments in power distribution networks,where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency,the Bidirectional Node-Controlled Rapidly Exploring Random Tree(BNC-RRT)algorithm is proposed.This algorithm guides path search by progressively altering the sampling area and employs a node control mechanism to constrain the random tree expansion and extract effective boundary points.This approach reduces the number of ineffective nodes and collision checks during the search process,thereby enhancing path planning efficiency.Comparative simulation experiments conducted in various scenarios demonstrate that this algorithm reduces the number of path nodes and improves planning efficiency compared to classical algorithms.Finally,real-world experiments on a live working robot developed by our team show that the proposed algorithm shortens the average path length by 8.6%,and reduces the average planning and movement times by 44.7%and 28.7%,respectively,compared to classical path planning algorithms.These results indicate that the algorithm effectively improves path planning efficiency and is suitable for live working tasks in the power distribution industry.
