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Generative Adversarial Network based Heuristics for sampling-based path planning

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 64-74页

作者： Tianyi Zhang Jiankun Wang Max Q.-H.Meng Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhen 518055China

sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.

关键词： Generative adversarial network(GAN) optimal path planning robot path planning sampling-based path planning

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Graph neural network based method for robot path planning

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Biomimetic Intelligence & Robotics 2024年第1期4卷 80-87页

作者： Xingrong Diao Wenzheng Chi Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Robotics and Microsystems Center School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215021China Jiaxing Research Institute Southern University of Science and TechnologyJiaxing 314000China

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.

关键词： Graph Neural Network(GNN) Collision detection sampling-based path planning

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