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This paper introduces a novel method for generating safe pose trajectories for fully actuated multirotors using a Model Predictive Control (MPC) framework that integrates viability theory with data-driven techniques. By employing dynamically computed axis-aligned bounding boxes for obstacle avoidance, the approach ensures formal safety guarantees while maintaining computational efficiency without the need for exhaustive offline reachability analysis. Numerical simulations on a tilted hexarotor validate the method, showcasing its effectiveness in navigating cluttered environments in real-time.
Real-time safe navigation in cluttered environments is achievable for multirotors without exhaustive reachability analysis.
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.
