Viewing Robot Navigation in Human Environment as a Cooperative Activity

TL;DR

This paper proposes a cooperative navigation planner for robots in human environments that predicts human trajectories and plans robot paths accordingly, enabling human-like, efficient, and socially compliant navigation in shared spaces.

Contribution

The work introduces a novel cooperative navigation planner that jointly optimizes human and robot trajectories using social constraints, improving navigation behavior in complex environments.

Findings

The planner replicates human-like navigation in open and confined spaces.

It effectively predicts human trajectories and plans cooperative paths.

Experiments show improved navigation efficiency and social compliance.

Abstract

We claim that navigation in human environments can be viewed as cooperative activity especially in constrained situations. Humans concurrently aid and comply with each other while moving in a shared space. Cooperation helps pedestrians to efficiently reach their own goals and respect conventions such as the personal space of others. To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space. In this work, we present a navigation planner that is able to plan such cooperative trajectories, simultaneously enforcing the robot's kinematic constraints and avoiding other non-human dynamic obstacles. Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior not only in open spaces but also in confined areas. Besides adapting the robot trajectory, the planner is also able to proactively propose co-navigation solutions by jointly computing human and robot trajectories within the same optimization framework. We demonstrate richness and performance of the cooperative planner with simulated and real world experiments on multiple interactive navigation scenarios.