1. 广东工业大学 机电工程学院,广州,510006
2. School of Electromechanical Engineering, Guangdong University of Technology,Guangzhou,China,510006
3. 东莞理工学院 机械工程学院,广东,东莞,523808
纸质出版:2020
移动端阅览
卢俊华, 朱海飞, 梁经伦, 等. 双足爬壁机器人三维壁面环境全局路径规划[J]. 哈尔滨工业大学学报, 2020,52(1):148-155.
LU Junhua, ZHU Haifei, LIANG Jinglun, et al. Global path planning for a biped wall-climbing robot in 3D wall environment[J]. 2020, 52(1): 148-155.
卢俊华, 朱海飞, 梁经伦, 等. 双足爬壁机器人三维壁面环境全局路径规划[J]. 哈尔滨工业大学学报, 2020,52(1):148-155. DOI: 10.11918/201812058.
LU Junhua, ZHU Haifei, LIANG Jinglun, et al. Global path planning for a biped wall-climbing robot in 3D wall environment[J]. 2020, 52(1): 148-155. DOI: 10.11918/201812058.
为求解双足爬壁机器人在三维壁面环境中的全局路径
提出了一种结合壁面可过渡性分析、全局壁面序列搜索和壁面过渡落足点优化的规划方法. 首先
为得到双足爬壁机器人在壁面间过渡的可行性
通过机器人可达工作空间与壁面简化处理将其转化成几何图形相交测试问题. 然后
用图搜索方法找出全局壁面序列
再以路径最短为目标建立数学模型优化求解壁面序列中相邻壁面间最优过渡落足点
最终得到最优全局路径. 以双足爬壁机器人W-Climbot为对象做仿真验证
仿真结果表明该方法在5至20个壁面构成的三维环境中
机器人在不同壁面间的可过渡性分析与全局壁面序列搜索过程平均耗时只需2 ms
求解得到优化全局路径的比例为95%
平均耗时均在4 s以内. 该方法可以为双足爬壁机器人提供优化的全局路径并为其下一步的运动规划奠定基础.
To solve the global path for a biped wall-climbing robot in a 3D wall environment
a planning method combining wall transition analysis
searching of global wall sequences
and optimization of adhesion points when performing wall transitions is proposed. Firstly
to obtain the feasibility for the biped wall-climbing robot to transit between walls
the robot’s reachable workspace and the wall are simplified to transform the problem into a geometric intersection test. Then
the global wall sequence is found by using a graph search method. Finally
to obtain the optimal global path
a mathematical model is established to compute the optimal adhesion point for transiting between adjacent walls. A biped wall-climbing robot
named W-Climbot
is used for simulation. The simulation results show that the proposed method takes only 2ms on average to analyze the feasibility of transition between different walls and search the global wall sequence in a three-dimensional environment composed of 5 to 20 walls. The success rate to obtain the optimal global path is 95%
and the average time consumed is less than 4s. This method can provide optimized global paths for biped wall-climbing robots and lay a foundation for motion planning.
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