Visual Robot Obstacle Avoidance Planning and Simulation Using Mapped Point Clouds
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.
万方数据
维普
