Robust Sensitivity-Aware Chance-Constrained MPC for Efficient Handling of Multiple Uncertainty Sources
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| Title: | Robust Sensitivity-Aware Chance-Constrained MPC for Efficient Handling of Multiple Uncertainty Sources |
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| Authors: | Zhu, James, Simeon, Thierry, Cognetti, Marco |
| Contributors: | Cognetti, Marco |
| Source: | IEEE Robotics and Automation Letters. 10:10330-10337 |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Publication Year: | 2025 |
| Subject Terms: | [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Optimization and Optimal Control, Planning under Uncertainty, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Robust/Adaptive Control |
| Description: | Robust motion planning under uncertainty is critical for unlocking real-world robotics applications. This paper introduces SupeR-MPC, a computationally-efficient, sensitivity-aware, chance-constrained optimization framework that systematically accounts for multiple sources of uncertainty, including state estimation error, model parameter uncertainty, obstacle localization error, and process noise. This approach advances sensitivity-aware robust control by integrating chance-constrained optimization to handle the uncertainty models of Kalman-filtering methods. To demonstrate robustness against multiple uncertainty sources, SupeR-MPC was validated on a range of systems and environments, from a simple 2D example to a multi-agent dynamic obstacle avoidance scenario. Comparisons against existing MPC methods show that SupeR-MPC significantly improves constraint satisfaction and robustness while maintaining real-time computational efficiency. These results highlight the effectiveness of sensitivity-aware chance constraints in enhancing real-world robotic decision-making under uncertainty. |
| Document Type: | Article |
| File Description: | application/pdf |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2025.3597863 |
| Access URL: | https://hal.science/hal-05208652v1/document https://doi.org/10.1109/lra.2025.3597863 https://hal.science/hal-05208652v1 |
| Rights: | IEEE Copyright |
| Accession Number: | edsair.doi.dedup.....dfa958e7d434be38fa9a3935ff3bdb79 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Robust motion planning under uncertainty is critical for unlocking real-world robotics applications. This paper introduces SupeR-MPC, a computationally-efficient, sensitivity-aware, chance-constrained optimization framework that systematically accounts for multiple sources of uncertainty, including state estimation error, model parameter uncertainty, obstacle localization error, and process noise. This approach advances sensitivity-aware robust control by integrating chance-constrained optimization to handle the uncertainty models of Kalman-filtering methods. To demonstrate robustness against multiple uncertainty sources, SupeR-MPC was validated on a range of systems and environments, from a simple 2D example to a multi-agent dynamic obstacle avoidance scenario. Comparisons against existing MPC methods show that SupeR-MPC significantly improves constraint satisfaction and robustness while maintaining real-time computational efficiency. These results highlight the effectiveness of sensitivity-aware chance constraints in enhancing real-world robotic decision-making under uncertainty. |
|---|---|
| ISSN: | 23773774 |
| DOI: | 10.1109/lra.2025.3597863 |