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

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. T o 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 V oronoi-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. OBILE robots have advanced significantly in locomotion, perception, and navigation. However, they still struggle to handle demanding real-world tasks such as search and rescue. Their limitations in perception and cognitive awareness prevent them from adapting to complex and unpredictable environments. A promising direction to overcome these challenges is the integration of a human operator into the system, which is often referred to as a shared control framework. As a result, system performance can be substantially improved. In many tasks, mobile robots are expected to reach a target location or follow a predefined path.