典型文献
Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Tianyi Zhang
作者机构：
Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen 518055,China;Department of Electronic Engineering,The Chinese University of Hong Kong,Hong Kong,China;Shenzhen Research Institute,The Chinese University of Hong Kong,Hong Kong,China
文献出处：
引用格式：
[1]Tianyi Zhang-.Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning)[J].自动化学报（英文版）,2022(01):64-74
A类：
Heuristics
B类：
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AB值：
0.580522
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