摘要
针对农业机器人在大棚中执行搬运任务时的高效率且尽可能沿地图中心行驶的需求,提出一种基于骨架提取和启发式算法的路径规划方法。首先设计了自适应阈值的地图骨架路径关键点提取步骤,用关键点指导启发式搜索树的构建。然后采用膨胀RS曲线代替直线进行树的生长,保证机器人的运动学约束和防碰撞,同时提出一种混合碰撞检测方法,进一步提高算法效率。最后设计关键点扩展步骤,提高算法的鲁棒性。仿真结果表明,简单地图下,本算法效率约是RRT*-Connect算法的5倍、Hybrid A*算法的7倍,复杂地图下约是RRT*-Connect算法的300倍、Hybrid A*算法的9倍,能够满足机器人的运动学约束。
A path planning method based on skeleton extraction and heuristic algorithm is proposed to meet the requirements of high efficiency and driving along the center of the map as much as possible when agricultural robots carry out transport tasks in the greenhouse.Firstly,the key point extraction step of map skeleton path with adaptive threshold is designed,and a heuristic search tree construction method guided by key points is proposed.Then,the expansion RS curves are used to replace the straight lines for tree growth to ensure the kinematic constraint and anti-collision of the robot.At the same time,a hybrid collision detection method is proposed to further improve the efficiency of the algorithm.Finally,the key point expansion steps are designed to improve the robustness of the algorithm.Simulation results show that the efficiency of the proposed algorithm is about 5 times that of RRT*-Connect algorithm and 7 times that of Hybrid A*algorithm in simple maps,and 300 times that of RRT*-Connect algorithm and 9 times that of Hybrid A*algorithm in complex maps,which can meet the kinematic constraints of robots.
作者
刘义亭
陆宇豪
李佩娟
郁汉琪
吴清政
杜俊峰
LIU Yiting;LU Yuhao;LI Peijuan;YU Hanqi;WU Qingzheng;DU Junfeng(School of Automation,Nanjing Institute of Technology,Nanjing 211167,China;School of Information Science and Engineering,Southeast University,Nanjing 210096,China;Graduate Department,Nanjing Institute of Technology,Nanjing 211167,China;Industrial center,Nanjing Institute of Technology,Nanjing 211167,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2023年第3期301-308,共8页
Journal of Chinese Inertial Technology
基金
国家自然科学基金青年基金(61903184)
江苏省自然科学基金青年基金(BK20181017,BK2019K186)
南京工程学院引进人才科研启动基金(YKJ2018822)
中国博士后科学基金第67批面上项目(2020M671292)
江苏省博士后科研资助计划(B类)(2019K186)
2021年度省重点研发计划(产业前瞻与共性关键技术)(BE2021016-5)。
关键词
农业搬运机器人
路径规划
波前算法
混合A*
启发式搜索
agricultural transport robot
path planning
wavefront algorithm
hybrid A*
heuristic search
