基于G-RRT*算法的煤矸石分拣机器人路径规划

朱子祺; 李创业; 代伟

doi:10.13272/j.issn.1671-251x.2021090015

基于G-RRT*算法的煤矸石分拣机器人路径规划

doi: 10.13272/j.issn.1671-251x.2021090015

1.
国家能源集团神东煤炭集团公司洗选中心, 陕西榆林　719300
2.
郑州机电工程研究所, 河南郑州　450015
3.
中国矿业大学信息与控制工程学院, 江苏徐州　221116

基金项目: 国家自然科学基金面上项目（61973306）。

详细信息

作者简介:
朱子祺（1982-），男，江苏徐州人，高级工程师，研究方向为煤炭加工及高质化、煤矿智能化，E-mail：Studyzhu@163.com

中图分类号: TD67
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出版历程
- 收稿日期: 2021-09-05
- 修回日期: 2022-03-07
- 网络出版日期: 2022-03-22

Path planning of coal gangue sorting robot based on G-RRT* algorithm

1.
Washing Center, CHN Energy Shendong Coal Group Co., Ltd., Yulin 719300, China
2.
Zhengzhou Institute of Electric and Mechanical Engineering, Zhengzhou 450015, China
3.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

摘要

摘要: 由于煤矸石分拣环境复杂，为了避免机器人与障碍物发生碰撞，提高分拣效率，对机器人进行路径规划十分必要。分析了煤矸石分拣系统原理，将煤矸石分拣机器人路径规划问题归结为在障碍物环境下规划出一条从给定起点到目标点的无碰撞路径，且需同时满足速度快、避免与障碍物碰撞2个约束条件。结合笛卡尔空间和关节空间的优点，提出一种在关节空间进行路径规划、在笛卡尔空间进行碰撞检测的煤矸石分拣机器人路径规划方案，该方案不需要对机器人进行运动学求逆，且可避免在关节空间中描述障碍物。针对RRT*路径规划算法存在盲目性的问题，提出一种变概率的目标偏置策略，并将其引入RRT*算法，得到G-RRT*算法。变概率的目标偏置策略在无障碍物区域增大目标偏置概率值，以增强算法的目标导向性；而在障碍物区域减小目标偏置概率值，以保证算法的避障能力。G-RRT*算法将变概率的目标偏置策略与RRT*算法相结合，既保留了RRT*算法路径长度渐进最优的特点，也提高了算法的目标导向性，可极大地提高路径规划效率。实验结果表明，与加入固定概率目标偏置策略的RRT-Connect算法和RRT算法相比，采用G-RRT*算法得到的路径长度平均值最小，说明G-RRT*算法更适用于煤矸石分拣机器人路径规划。
- 煤矸石分拣 /
- 机器人 /
- 路径规划 /
- 快速扩展随机树 /
- 变概率的目标偏置策略 /
- G-RRT*算法
Abstract: The coal gangue sorting environment is complex. In order to avoid the collision between robot and obstacles and improve sorting efficiency, it is necessary to carry out path planning for robot. The principle of coal gangue sorting system is analyzed. The path planning problem of coal gangue sorting robot is summed up as planning a collision-free path from a given starting point to a target point in the environment of obstacles, and the two constraints of high speed and avoiding collision with obstacles must be met at the same time. Combining the advantages of Cartesian space and joint space, a path planning scheme for coal gangue sorting robot with path planning in joint space and collision detection in Cartesian space is proposed. The scheme does not need to carry out kinematic inversion of the robot, and can avoid describing obstacles in joint space. In order to solve the problem of blindness in the improved rapidly-exploring random trees (RRT*) path planning algorithm, a variable probability target bias strategy is proposed and introduced into RRT* algorithm to obtain the G-RRT* algorithm. The target bias strategy with variable probability increases the target bias probability in the obstacle-free area so as to enhance the target orientation of the algorithm. In the obstacle area, the target bias probability value is reduced to ensure the obstacle avoidance capability of the algorithm. The G-RRT* algorithm combines the variable probability target bias strategy with RRT* algorithm. The G-RRT* algorithm not only retains the asymptotic optimal path length characteristic of RRT* algorithm, but also improves the target orientation of the algorithm, and can improve the path planning efficiency greatly. The experimental results show that compared with RRT-Connect algorithm and RRT algorithm with fixed probability target bias strategy, the G-RRT* algorithm can get the shortest average path length, and is more suitable for path planning of coal gangue sorting robot.
- coal gangue sorting /
- robot /
- path planning /
- rapidly-exploring random trees /
- variable probability target bias strategy /
- G-RRT* algorithm

HTML全文

图 1 煤矸石分拣系统

Figure 1. Coal gangue sorting system

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图 2 机器人路径规划方案

Figure 2. Robot path planning scheme

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图 3 机器人机械结构

Figure 3. Robot mechanical structure

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图 4 机器人连杆和障碍物简化模型

Figure 4. Simplified model of robot link and obstacle

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图 5 RRT算法节点扩展

Figure 5. Node extension of RRT algorithm

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图 6 RRT*算法节点扩展

Figure 6. Node extension of RRT* algorithm

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图 7 目标偏置策略效果对比

Figure 7. Comparison of target bias strategy effects

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图 8 引入固定概率目标偏置的RRT算法实验结果

Figure 8. Experimental results of RRT algorithm introducing fixed probability target bias

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图 9 引入固定概率目标偏置的RRT-Connect算法实验结果

Figure 9. Experimental results of RRT-Connect algorithm introducing fixed probability target bias

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图 10 G-RRT*算法实验结果

Figure 10. Experimental results of G-RRT* algorithm

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图 11 3种算法实验结果对比

Figure 11. Comparison of experimental results of three algorithms

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表 1 算法性能参数对比

Table 1. Comparison of algorithm performance parameters

算法平均时间/s 路径长度
平均值/rad

RRT 5.66 7.57
RRT-Connect 2.98 11.86
G-RRT* 4.34 6.79

下载: 导出CSV

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