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
针对当前路径规划算法在高维复杂环境下,存在障碍物信息处理不充分、搜索效率与路径质量难以平衡等问题,提出了一种基于自适应概率偏差的改进算法——APB-RRT*算法(Adaptive Probability Bias RRT*)。首先依据障碍物类型设定安全距离,...针对当前路径规划算法在高维复杂环境下,存在障碍物信息处理不充分、搜索效率与路径质量难以平衡等问题,提出了一种基于自适应概率偏差的改进算法——APB-RRT*算法(Adaptive Probability Bias RRT*)。首先依据障碍物类型设定安全距离,在保障安全的基础上将采样空间划分为m×m×m个子区域,引入参考路径邻近性评价函数,动态调整子区域采样概率,加快了算法的收敛速度,减少了冗余节点。同时,融合基于环境感知的自适应偏置策略和变步长调节机制,提高了路径搜索效率。最后,为遵循机械臂运动学特性,采用分段贪婪算法结合三次样条插值法对路径进行平滑约束,确保了路径的连续性和可执行性。仿真实验结果表明,该文算法规划出的路径最接近理想最优路径。展开更多
针对传统的快速扩展随机树(rapidly-exploring random tree,RRT*)算法应用于高维空间冗余机械臂路径规划时存在过多无用节点和无效节点而导致规划失败等问题,提出一种改进的节点控制快速扩展随机树(nodes-controlled RRT*,N-RRT*)算法...针对传统的快速扩展随机树(rapidly-exploring random tree,RRT*)算法应用于高维空间冗余机械臂路径规划时存在过多无用节点和无效节点而导致规划失败等问题,提出一种改进的节点控制快速扩展随机树(nodes-controlled RRT*,N-RRT*)算法。首先,提出随机树节点扩展控制策略,改善了随机树节点扩展的盲目性,同时减少了无用节点的生成;然后,针对随机树在扩展时可能产生无效节点的问题,提出随机树节点扩展能力检测策略,减少了无效节点的生成。最后,采用MATLAB软件进行了多障碍静态避障路径规划仿真实验,结果表明,与基本RRT*算法相比,N-RRT*算法可减少规划时间和障碍节点,并能提高规划成功率,为机械臂路径规划提供了一个新的研究思路。展开更多
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.
文摘针对当前路径规划算法在高维复杂环境下,存在障碍物信息处理不充分、搜索效率与路径质量难以平衡等问题,提出了一种基于自适应概率偏差的改进算法——APB-RRT*算法(Adaptive Probability Bias RRT*)。首先依据障碍物类型设定安全距离,在保障安全的基础上将采样空间划分为m×m×m个子区域,引入参考路径邻近性评价函数,动态调整子区域采样概率,加快了算法的收敛速度,减少了冗余节点。同时,融合基于环境感知的自适应偏置策略和变步长调节机制,提高了路径搜索效率。最后,为遵循机械臂运动学特性,采用分段贪婪算法结合三次样条插值法对路径进行平滑约束,确保了路径的连续性和可执行性。仿真实验结果表明,该文算法规划出的路径最接近理想最优路径。
文摘针对传统的快速扩展随机树(rapidly-exploring random tree,RRT*)算法应用于高维空间冗余机械臂路径规划时存在过多无用节点和无效节点而导致规划失败等问题,提出一种改进的节点控制快速扩展随机树(nodes-controlled RRT*,N-RRT*)算法。首先,提出随机树节点扩展控制策略,改善了随机树节点扩展的盲目性,同时减少了无用节点的生成;然后,针对随机树在扩展时可能产生无效节点的问题,提出随机树节点扩展能力检测策略,减少了无效节点的生成。最后,采用MATLAB软件进行了多障碍静态避障路径规划仿真实验,结果表明,与基本RRT*算法相比,N-RRT*算法可减少规划时间和障碍节点,并能提高规划成功率,为机械臂路径规划提供了一个新的研究思路。
