Visual Robot Obstacle Avoidance Planning and Simulation Using Mapped Point Clouds

doi:10.16182/j.issn1004731x.joss.23-0618

Abstract

Abstract:

In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle recognition and path planning are achieved. Experimental data from ROS based picking robots demonstrates the feasibility of this algorithm and significantly improves the efficiency of harvesting operations.

Key words: binocular vision, path planning, point cloud segmentation, ROS simulation, picking obstacle avoidance

CLC Number:

TP249

Huo Hanlin, Zou Xiangjun, Chen Yan, Zhou Xinzhao, Chen Mingyou, Li Chengen, Pan Yaoqiang, Tang Yunchao. Visual Robot Obstacle Avoidance Planning and Simulation Using Mapped Point Clouds[J]. Journal of System Simulation, 2024, 36(9): 2149-2158.

Figures/Tables 13

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Table 1

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测试方法	平均规划时间/s	迭代次数	平均代价函数/mm	平均机械臂执行时间/s
RRT*	9.56	200	465.82	7.45
M-Informed RRT*	7.23	200	461.93	6.34

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