Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation

TL;DR

This paper introduces the Probabilistic Gap Planner (PGP), a novel two-step approach for social robot navigation that improves long-term maneuvering by identifying gaps in crowds, leading to safer and more efficient interactions.

Contribution

The paper presents PGP, a new conflict avoidance planner that incorporates cooperativity into probabilistic collision risk models for better crowd navigation.

Findings

PGP improves safety by reducing collisions.

Agents maintain more space and create less tension.

PGP operates in real-time on a mobile robot.

Abstract

In Social Robot Navigation, autonomous agents need to resolve many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to select a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning into two separate steps: Conflict avoidance for finding good, macroscopic trajectories, and cooperative collision avoidance (CCA) for resolving the next interaction optimally. We propose the Probabilistic Gap Planner (PGP) as a conflict avoidance planner. PGP modifies an established probabilistic collision risk model to include a general assumption of cooperativity. PGP biases the short-term CCA planner to head towards gaps in the crowd. In extensive simulations with crowds of varying density, we show that using PGP in addition to state-of-the-art CCA planners improves the agents' performance: On average, agents keep more space to others, create less tension, and cause fewer collisions. This typically comes at the expense of slightly longer paths. PGP runs in real-time on WaPOCHI mobile robot by Honda R&D.