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

作　　者：Yefeng YANG Tao HUANG Tianqi WANG Wenyu YANG Han CHEN Boyang LI Chih-yung WEN 

机构地区：[1]Department of Aeronautical and Aviation Engineering,The Hong Kong Polytechnic University,Hong Kong 999077,China [2]Center for Control Theory and Guidance Technology,Harbin Institute of Technology,Harbin 150001,China [3]Research Center for Unmanned Autonomous Systems,The Hong Kong Polytechnic University,Hong Kong 999077,China [4]School of Engineering,The University of Newcastle,Callaghan NSW 2308,Australia

出　　处：《Science China(Information Sciences)》2024年第5期193-210,共18页中国科学（信息科学）（英文版）

基　　金：supported by Research Center of Unmanned Autonomous Systems(RCUAS),The Hong Kong Polytechnic University(Grant No.P0046487).

摘　　要：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.

关 键 词：rapidly exploring random tree(RRT) adaptive clustering network decoupling actor critic(AC) path planning 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]