On the Benefits of Robot Platooning for Navigating Crowded Environments

TL;DR

This study evaluates robot platooning versus greedy strategies for navigating various crowded environments, showing platooning's advantages in some scenarios and proposing an adaptive hybrid approach for improved performance.

Contribution

It introduces an adaptive strategy that switches between platooning and greedy behaviors, optimizing navigation in different crowd scenarios.

Findings

Platooning is less disruptive in passive and counter-flow crowds.

Greedy strategy outperforms in perpendicular-flow crowds.

Adaptive strategy combines strengths of both approaches.

Abstract

This paper studies how groups of robots can effectively navigate through a crowd of agents. It quantifies the performance of platooning and less constrained, greedy strategies, and the extent to which these strategies disrupt the crowd agents. Three scenarios are considered: (i) passive crowds, (ii) counter-flow crowds, and (iii) perpendicular-flow crowds. Through simulations consisting of up to 200 robots, we show that for navigating passive and counter-flow crowds, the platooning strategy is less disruptive and more effective in dense crowds than the greedy strategy, whereas for navigating perpendicular-flow crowds, the greedy strategy outperforms the platooning strategy in either aspect. Moreover, we propose an adaptive strategy that can switch between platooning and greedy behavioral states, and demonstrate that it combines the strengths of both strategies in all the scenarios considered.