A Shared Control Framework for Mobile Robots with Planning-Level Intention Prediction

TL;DR

This paper introduces a shared control framework for mobile robots that predicts human intentions at the planning level using deep reinforcement learning, enabling adaptive trajectory adjustment and improved safety in human-robot collaboration.

Contribution

It proposes a novel intention prediction method based on intention domains and a path replanning algorithm that jointly optimize for human intent and safety, trained entirely in simulation.

Findings

Reduces operator workload significantly

Enhances safety without sacrificing efficiency

Validated through extensive simulations and real-world tests

Abstract

In mobile robot shared control, effectively understanding human motion intention is critical for seamless human-robot collaboration. This paper presents a novel shared control framework featuring planning-level intention prediction. A path replanning algorithm is designed to adjust the robot's desired trajectory according to inferred human intentions. To represent future motion intentions, we introduce the concept of an intention domain, which serves as a constraint for path replanning. The intention-domain prediction and path replanning problems are jointly formulated as a Markov Decision Process and solved through deep reinforcement learning. In addition, a Voronoi-based human trajectory generation algorithm is developed, allowing the model to be trained entirely in simulation without human participation or demonstration data. Extensive simulations and real-world user studies demonstrate that the proposed method significantly reduces operator workload and enhances safety, without compromising task efficiency compared with existing assistive teleoperation approaches.