Towards Minimally-Intrusive Navigation in Densely-Populated Pedestrian Flow

Zhou，Tong1; Qi，Senmao2; Lyu，Erli2; Cen，Guangdu2; Wang，Jiaole2; Meng，Max Q.H.1,3,4

10.1109/ROBIO54168.2021.9739572

2021

978-1-6654-0536-2

2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021

334-339

27-31 Dec. 2021

Sanya, China

Mobile robot navigation has achieved great success in last decades. However, traditional methods usually aim at minimizing trajectory length or time, the planned trajectories may hinder pedestrians from walking. To solve this problem, we propose two penalty terms called flow disturbance penalty (FDP) and individual disturbance penalty (IDP) which describe the disturbance of the vehicle in macro and micro levels, respectively. In order to conduct minimally-intrusive navigation, in the micro level navigation, we adopt sampling-based method to minimize not only distance to goal but also IDP. In the macro level navigation, A*-based algorithm has been adopted to minimize FDP while achieving the optimal trajectory length. In addition, we build a triangulation map which gives an abstract representation of the environment to improve real-time performance. Extensive experiments have been carried out to compare our method with the state-of-the-art methods. The results have validated that the proposed method not only can provide minimal disturbance, but also has better real-time performance.

Legged locomotion Learning systems Navigation Search methods Conferences Biomimetics Real-time systems

其他

英语

EI

20221611977390

Intelligent robots ; Mobile robots ; Trajectories

Robotics:731.5 ; Robot Applications:731.6

2-s2.0-85128189413

Scopus

1.Chinese University of Hong Kong,Department of Electronic Engineering Engineering,Hong Kong,Hong Kong
2.Harbin Institute of Technology(Shenzhen),School of Mechanical Engineering and Automation,Shenzhen,518055,China
3.Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
4.Shenzhen Research Institute,Chinese University of Hong Kong,Shenzhen,China

Zhou，Tong,Qi，Senmao,Lyu，Erli,et al. Towards Minimally-Intrusive Navigation in Densely-Populated Pedestrian Flow[C],2021:334-339.