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This paper introduces a shared control framework for mobile robots that uses planning-level intention prediction to improve human-robot collaboration. The framework represents future human motion intentions as an "intention domain" constraint for path replanning, which is jointly optimized with intention-domain prediction using deep reinforcement learning. A Voronoi-based trajectory generation algorithm enables training in simulation without human data, and the method demonstrates reduced operator workload and enhanced safety in both simulations and real-world experiments.
Ditch the joystick: this shared control framework anticipates your navigation goals for mobile robots, slashing workload and boosting safety without sacrificing speed.
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.
