Graph neural network based method for robot path planning

作     者：Xingrong Diao Wenzheng Chi Jiankun Wang 

作者机构：Shenzhen Key Laboratory of Robotics Perception and IntelligenceDepartment of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Robotics and Microsystems CenterSchool of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215021China Jiaxing Research InstituteSouthern University of Science and TechnologyJiaxing 314000China 

出 版 物：《Biomimetic Intelligence & Robotics》 (仿生智能与机器人（英文）)

年 卷 期：2024年第4卷第1期

页      面：80-87页

核心收录：

学科分类：08[工学] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

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. 

主　　题：Graph Neural Network(GNN) Collision detection Sampling-based path planning 

摘      要：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.