Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 149–156

Abstract

Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with low- (< 0.15 ppsm), mid- (< 0.65 ppsm), and high- (< 1 ppsm) pedestrian densities. We present comparative results between two low-level obstacle avoidance methods and a baseline of shared control. Results show a 10% drop in relative time to goal on the highest density tests, and no other efficiency metric decrease. Moreover, autonomous navigation showed to be comparable to shared-control navigation with a lower relative jerk and significantly higher fluency in commands indicating high compatibility with the crowd. We conclude that the reactive controller fulfils a necessary task of fast and continuous adaptation to crowd navigation, and it should be coupled with high-level planners for environmental and situational awareness.

Authors 6

1. Diego Felipe PAEZ GRANADOS

   ETH Zurich

   Affiliation as printed

   Swiss Federal School of Technology in Zurich - ETH Zurich and SPZ,SCAI Lab,Switzerland

   SCAI Lab, Swiss Federal School of Technology in Zurich - ETH Zurich and SPZ, Switzerland

2. Yujie He

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Swiss Federal School of Technology in Lausanne - EPFL,LASA Laboratory,Switzerland

   LASA Laboratory, Swiss Federal School of Technology in Lausanne - EPFL, Switzerland

3. David J. Gonon

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Swiss Federal School of Technology in Lausanne - EPFL,LASA Laboratory,Switzerland

   LASA Laboratory, Swiss Federal School of Technology in Lausanne - EPFL, Switzerland

4. Bastian Leibe Aachen Visual Computing Institute

   RWTH Aachen University

   Affiliation as printed

   Visual Computing Institute, RWTH,Germany

   Visual Computing Institute, RWTH, Germany

5. Kenji Suzuki

   University of Tsukuba

   Affiliation as printed

   University of Tsukuba,Artificial Intelligence Lab,Japan

   Artificial Intelligence Lab, University of Tsukuba, Japan

6. Aude Gemma Billard

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Swiss Federal School of Technology in Lausanne - EPFL,LASA Laboratory,Switzerland

   LASA Laboratory, Swiss Federal School of Technology in Lausanne - EPFL, Switzerland