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A Planning-Level Intention Prediction Framework for Mobile Robot Shared Control

The paper proposes a shared control framework for mobile robots with planning-level intention prediction, using an intention domain to constrain path replanning and jointly modeling intention-domain prediction and replanning as a Markov decision process solved via deep reinforcement learning. The authors also design a Voronoi-based human trajectory generation algorithm that allows the model to be trained entirely in simulation without human participation or demonstration data; simulations and real-world user studies show the method reduces operator workload and improves safety without degrading task efficiency.

Part of the event: Paper on shared control framework for mobile robots accepted by IEEE T-RO

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