aiqbench analysis
This research advances industrial aerial robotics by integrating viability theory into Model Predictive Control (MPC) to ensure formal safety guarantees for multirotors in unstructured environments. The approach dynamically computes bounding boxes for obstacle avoidance, eliminating the need for exhaustive offline reachability analysis while maintaining real-time computational efficiency. Such algorithmic improvements are critical for deploying autonomous drones in complex, cluttered scenarios where safety and performance must be simultaneously optimized.
Towards safe and optimal flight: Viability Kernel MPC for Fully Actuated Multirotor
arXiv:2608.25459v1 Announce Type: new Abstract: Industrial aerial robotics demands safety guarantees for navigation in unstructured environments while optimizing performance and computational efficiency. This paper presents a method for generating safe pose trajectories for fully actuated multirotors within a Model Predictive Control (MPC) framework, leveraging both viability theory and data-driven methods. Obstacle avoidance is enforced through dynamically computed axis-aligned bounding boxes, providing formal safety guarantees without exhaustive offline reachability analysis. Numerical simulations on a fully actuated tilted hexarotor validate the approach, demonstrating successful navigation in cluttered environments with real-time computational performance.
Abstract:Industrial aerial robotics demands safety guarantees for navigation in unstructured environments while optimizing performance and computational efficiency. This paper presents a method for generating safe pose trajectories for fully actuated multirotors within a Model Predictive Control (MPC) framework, leveraging both viability theory and data-driven methods. Obstacle avoidance is enforced through dynamically computed axis-aligned bounding boxes, providing formal safety guarantees without exhaustive offline reachability analysis. Numerical simulations on a fully actuated tilted hexarotor validate the approach, demonstrating successful navigation in cluttered environments with real-time computational performance.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2608.25459 [cs.RO]
(or arXiv:2608.25459v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.25459 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 182 words
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