Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yefeng Yang, Tao Huang, Tianqi Wang, Wenyu Yang, Han Chen, Boyang Li, Chih-yung Wen
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引用次数: 0

Abstract

Autonomous robots have garnered extensive utilization in diverse fields. Among the critical concerns for autonomous systems, path planning holds paramount importance. Notwithstanding considerable efforts in its development over the years, path planning for autonomous systems continues to grapple with challenges related to low planning efficiency and inadequate obstacle avoidance response in a timely manner. This study proposes a novel and systematic solution to the path planning problem within intricate office buildings. The solution consists of a global planner and a local planner. To handle the global planning aspect, an adaptive clustering-based dynamic programming rapidly exploring random tree (ACDP-RRT) algorithm is proposed. ACDP-RRT effectively identifies obstacles on the map by leveraging geometric features. These obstacles are then represented as a collection of sequentially arranged convex polygons, optimizing the sampling region and significantly enhancing sampling efficiency. For local planning, a network decoupling actor-critic (ND-AC) algorithm is employed. The proposed ND-AC simplifies the local planner design process by integrating planning and control loops into a neural network (NN) trained via an end-to-end model-free deep reinforcement learning (DRL) framework. Moreover, the adoption of network decoupling (ND) techniques leads to an improved obstacle avoidance success rate when compared to conventional actor-critic (AC)-based methods. Extensive simulations and experiments are conducted to demonstrate the effectiveness and robustness of the proposed approach.

自主机器人的采样高效路径规划和基于行为批判的改进型避障技术
自主机器人在各个领域都得到了广泛应用。在自主系统的关键问题中,路径规划至关重要。尽管多年来自主系统在路径规划方面做出了巨大努力,但仍然面临着规划效率低、避障反应不及时等挑战。本研究针对错综复杂的办公建筑内的路径规划问题提出了一种新颖而系统的解决方案。该解决方案由全局规划器和局部规划器组成。为了处理全局规划方面的问题,提出了一种基于聚类的自适应动态编程快速探索随机树(ACDP-RRT)算法。ACDP-RRT 利用几何特征有效识别地图上的障碍物。然后,将这些障碍物表示为顺序排列的凸多边形集合,从而优化采样区域,显著提高采样效率。在局部规划方面,采用了网络解耦演员批判(ND-AC)算法。所提出的 ND-AC 将规划和控制回路集成到通过端到端无模型深度强化学习(DRL)框架训练的神经网络(NN)中,从而简化了局部规划器的设计过程。此外,与传统的基于行为批判(AC)的方法相比,采用网络解耦(ND)技术提高了避障成功率。为了证明所提方法的有效性和鲁棒性,我们进行了大量的模拟和实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
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