控制理论与应用2025，Vol.42Issue(10)：1946-1956,11.DOI:10.7641/CTA.2024.30528
面向动态人群场景的机器人时空图分层导航
Hierarchical navigation of spatio-temporal graph for crowds
摘要
Abstract
Crowd environment is one of the important application scenarios for ground mobile robots.However,due to pedestrians' decision-making abilities and the inherent uncertainty of their behavior,achieving autonomous and safe navigation in crowded environments has emerged as a significant challenge in the field of service robots.To address this,this paper proposes a spatio-temporal-graph-based hierarchical crowd navigation(STG-HCN)that provides both global and local solutions.Firstly,the STG-HCN introduces Delaunay triangulation to describe the topological relationship of crowd-s.This approach gives rise to a spatio-temporal graph search algorithm,facilitating the rapid generation of global paths.Secondly,considering the dynamic attributes of pedestrians,the paper further integrates pedestrian speed and perspective information.This leads to the proposal of a forward attention potential field based on a Gaussian distribution,aimed at evaluating the impact of the global path on pedestrians.Furthermore,time-varying collision avoidance constraints are for-mulated for dynamic pedestrians,and a receding horizon optimization framework is introduced to solve the time-varying constrained optimization problem,thereby significantly enhancing the motion safety of the robot's global path tracking.To validate the effectiveness of the proposed algorithm,this paper conducts extensive simulation and real-world experiments.Simulation results indicate that STG-HCN can reduce the average number of collisions by 55.7％compared to the bench-mark algorithms.Additionally,real-world experiments conducted using a Fetch robot demonstrate the algorithm's efficacy in practical crowd scenes.关键词
地面移动机器人/动态路径规划/滚动时域优化/自主导航Key words
service robots/dynamic path planning/receding horizon control/crowd navigahtion引用本文复制引用
林思超,周智千,任君凯,曾志文,常梦迪,郑志强,卢惠民..面向动态人群场景的机器人时空图分层导航[J].控制理论与应用,2025,42(10):1946-1956,11.基金项目
国家自然科学基金项目(U1913202,U22A2059,62203460),湖南省自然科学基金重大项目(2021JC0004)资助.Supported by the National Science Foundation of China(U1913202,U22A2059,62203460)and the Major Project of Natural Science Foundation of Hunan Province(2021JC0004). （U1913202,U22A2059,62203460）
