For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collisi...For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A^(*)have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A^(*)(ARa A^(*)),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A^(*)features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A^(*)to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A^(*)outperforms Anytime Repairing A^(*)(ARA^(*))and Anytime D^(*)(AD^(*))in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A^(*)can ensure the safe flight of quadrotors.展开更多
基金the support of the National Natural Science Foundation of China(No.52272382)the Aeronautical Science Foundation of China(No.20200017051001)the Fundamental Research Funds for the Central Universities,China。
文摘For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A^(*)have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A^(*)(ARa A^(*)),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A^(*)features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A^(*)to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A^(*)outperforms Anytime Repairing A^(*)(ARA^(*))and Anytime D^(*)(AD^(*))in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A^(*)can ensure the safe flight of quadrotors.
