Joint Communication and Motion Planning for Cobots

TL;DR

This paper introduces a unified framework for robots to communicate and plan motions jointly in human co-working environments, improving safety and efficiency by reducing conflicts and deadlocks.

Contribution

It proposes a novel joint communication and motion planning method that models human perception and incorporates social compliance, advancing robot-human interaction strategies.

Findings

Reduces conflicts between robots and humans.

Effectively resolves potential deadlocks.

Computes feasible motion and communication strategies efficiently.

Abstract

The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation robot behavior. Movement among humans is one of the most fundamental -- and yet critical -- problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks.