Scenario-based model predictive control with probabilistic human predictions for human–robot coexistence
Oleinikov A. Soltan S. Balgabekova Z. Bemporad A. Rubagotti M.
January 2024Elsevier Ltd
Control Engineering Practice
2024#142
This paper proposes a real-time motion planning scheme for safe human–robot workspace sharing relying on scenario-based nonlinear model predictive control (NMPC), a well-known approach for solving stochastic NMPC problems. A scenario tree is generated via higher-order Markov chains to provide probabilistic predictions of the human motion. Scenario-based NMPC is then used to generate point-to-point motions of the robot manipulator based on the above-mentioned human motion predictions, accounting for safety constraints via speed and separation monitoring (SSM). This means that the robot speed is always modulated to be able to stop before a possible collision with the human occurs. After proving theoretical properties on recursive feasibility and closed-loop stability of the proposed motion planning strategy, this is tested experimentally on a Kinova Gen3 robot interacting with a human operator, showing superior performance with respect to an NMPC scheme not relying on human predictions and to a fixed-path SSM strategy.
Nonlinear model predictive control , Physical human–robot interaction , Robot motion planning
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Department of Robotics and Mechatronics, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 010000, Kazakhstan
Department of Computer Science, University of Milan, Via Giovanni Celoria 18, Milan, 20133, Italy
IMT School for Advanced Studies, Piazza S.Francesco 19, Lucca, 55100, Italy
Department of Robotics and Mechatronics
Department of Computer Science
IMT School for Advanced Studies
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