Beyond Terminal Constraints: Reliable Robot Control with Learned Safe Sets
Overview
This research paper introduces new classes of safe sets for robot control that are broader than classical control-invariant sets. The authors demonstrate that safety can be guaranteed by constraining different nodes of the prediction horizon in Model Predictive Control (MPC) schemes, rather than relying solely on terminal state constraints. Their methods ensure recursive feasibility and can employ a safe-abort strategy when the safe set is an inner approximation of a control-invariant set. Extensive simulations on a 6-joint robot manipulator show fewer constraint violations compared to state-of-the-art methods while maintaining comparable tracking performance and computational efficiency. Hardware experiments further support the practicality of these schemes for real-world applications.
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Environment: simulation
“ds can ensure safety through a safe-abort strategy, even in the absence of recursive feasibility. We validate our approaches in extensive simulations on a 6-joint robot manipulator, showing fewer constraint violations than state-of-the-art methods while maintaining comparable tracking performance and computational efficiency. Hardware experiments further demonstrate the practicality of our schemes for real-world robo”
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- arXiv Robotics — research abstractsSignalBeyond Terminal Constraints: Reliable Robot Control with Learned Safe Sets
This research paper introduces new classes of safe sets for robot control that are broader than classical control-invariant sets. The authors demonstrate that safety can be guaranteed by constraining different nodes of the prediction horizon in Model Predictive Control (MPC) schemes, rather than relying solely on terminal state constraints. Their methods ensure recursive feasibility and can employ a safe-abort strategy when the safe set is an inner approximation of a control-invariant set. Extensive simulations on a 6-joint robot manipulator show fewer constraint violations compared to state-of-the-art methods while maintaining comparable tracking performance and computational efficiency. Hardware experiments further support the practicality of these schemes for real-world applications.
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