Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning

TL;DR

This paper introduces a map-based deep reinforcement learning method enabling robots to navigate safely among pedestrians employing diverse collision avoidance strategies, improving interaction success rates in various scenarios.

Contribution

It proposes a novel sensor and pedestrian map-based neural network approach for crowd-aware navigation accommodating multiple pedestrian strategies.

Findings

Outperforms existing methods in success rate across scenarios

Effective in both simulation and real-world robot tests

Handles pedestrians with different collision avoidance behaviors

Abstract

It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA). However, their performances commonly need to be further improved for practical applications, where pedestrians follow multiple different collision avoidance strategies. In this paper, we propose a map-based deep reinforcement learning approach for crowd-aware robot navigation with various pedestrians. We use the sensor map to represent the environmental information around the robot, including its shape and observable appearances of obstacles. We also introduce the pedestrian map that specifies the movements of pedestrians around the robot. By applying both maps as inputs of the neural network, we show that a navigation policy can be trained to better interact with pedestrians following different collision avoidance strategies. We evaluate our approach under multiple scenarios both in the simulator and on an actual robot. The results show that our approach allows the robot to successfully interact with various pedestrians and outperforms compared methods in terms of the success rate.