Sampling-based path planning is widely used in robotics,particularly in high-dimensional state spaces.In the path planning process,collision detection is the most time-consuming operation.Therefore,we propose a learni...Sampling-based path planning is widely used in robotics,particularly in high-dimensional state spaces.In the path planning process,collision detection is the most time-consuming operation.Therefore,we propose a learning-based path planning method that reduces the number of collision checks.We develop an efficient neural network model based on graph neural networks.The model outputs weights for each neighbor based on the obstacle,searched path,and random geometric graph,which are used to guide the planner in avoiding obstacles.We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments.The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.展开更多
基金This work is supported by Shenzhen Science and Technology Program,China(RCBS20221008093305007 and 20231115141459001)Young Elite Scientists Sponsorship Program by CAST(2023QNRC001),China.
文摘Sampling-based path planning is widely used in robotics,particularly in high-dimensional state spaces.In the path planning process,collision detection is the most time-consuming operation.Therefore,we propose a learning-based path planning method that reduces the number of collision checks.We develop an efficient neural network model based on graph neural networks.The model outputs weights for each neighbor based on the obstacle,searched path,and random geometric graph,which are used to guide the planner in avoiding obstacles.We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments.The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.
