Towards safe and optimal flight: Viability Kernel MPC for Fully Actuated Multirotor
- URL: http://arxiv.org/abs/2608.25459v1
- Date: Wed, 26 Aug 2026 07:26:11 GMT
- Title: Towards safe and optimal flight: Viability Kernel MPC for Fully Actuated Multirotor
- Abstract summary: This paper presents a method for generating safe pose trajectories for fully actuated multirotors within a Model Predictive Control framework.<n>Obstacle avoidance is enforced through computed axis-aligned bounding boxes, providing formal safety guarantees without exhaustive offline reachability analysis.<n> Numerical simulations on a fully actuated tilted hexarotor validate the approach, demonstrating successful navigation in cluttered environments with real-time computational performance.
- Score: 2.8914742944205862
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
arXiv Detail & Related papers (2026-07-22T16:37:05Z) - AWARE: Adaptive Whole-body Active Rotating Control for Enhanced LiDAR-Inertial Odometry under Human-in-the-Loop Interaction [15.502575136823237]
Resource-constrained UAV platforms are often limited to narrow-field-of-view LiDAR sensors.<n>AWAKE is a bio-inspired whole-body active yawing framework that exploits the UAV's own rotational agility to extend the effective sensor horizon.<n>A Safe Flight Corridor mechanism ensures operational safety within this HITL paradigm by decoupling the operator's navigational intent from autonomous yaw optimization.
arXiv Detail & Related papers (2026-04-12T12:07:58Z) - Provably Safe Trajectory Generation for Manipulators Under Motion and Environmental Uncertainties [26.41382315967256]
This article proposes a novel risk-bounded motion planning framework to address the unmet need for safe optimal motion planning.<n>Our approach integrates a rigid manipulator deep Koopman operator (RM-DeKO) model to predict the robot's state distribution under motion uncertainty.<n>We then introduce an efficient hierarchical verification method that combines parallel physics simulations with sum-of-squares (SOS) programming as a filter for fine-grained, formal certification of collision risk.
arXiv Detail & Related papers (2026-03-10T01:48:48Z) - SEP-NMPC: Safety Enhanced Passivity-Based Nonlinear Model Predictive Control for a UAV Slung Payload System [3.5382023072762188]
This paper introduces a Safety Enhanced Passivity-Based MPC (SEP-NMPC) that provides formal guarantees of stability and safety for a quadrotor transporting a payload through cluttered environments.<n> Stability is enforced by embedding a strict passivity inequality, which is derived from a shaped energy storage function with adaptive damping.<n>Safety is guaranteed through high-order control barrier functions (HOCBFs) that render user-defined clearance sets forward-invariant, obliging both the quadrotor and the swinging payload to maintain separation while interacting with static and dynamic obstacles.
arXiv Detail & Related papers (2026-03-09T19:20:08Z) - BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
arXiv Detail & Related papers (2026-02-23T18:19:46Z) - Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models [57.006252510102506]
Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications.<n>We introduce a novel recovery-based shielding framework that enables safe RL with a provable safety lower bound for unknown and non-linear continuous dynamical systems.
arXiv Detail & Related papers (2026-02-12T22:03:35Z) - Spatiotemporal Tubes for Differential Drive Robots with Model Uncertainty [2.4919077201316067]
This paper presents a Spatiotemporal Tube (STT)-based control framework for differential-drive mobile robots with dynamic uncertainties and external disturbances.<n>The proposed framework is validated through simulation studies on a differential-drive robot and benchmarked against state-of-the-art methods.
arXiv Detail & Related papers (2025-12-05T07:43:23Z) - Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
arXiv Detail & Related papers (2025-04-30T13:47:25Z) - Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100.
The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
arXiv Detail & Related papers (2024-10-03T17:50:19Z) - SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework.
Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations.
We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
arXiv Detail & Related papers (2023-12-31T04:14:43Z) - Chance-Constrained Trajectory Optimization for Safe Exploration and
Learning of Nonlinear Systems [81.7983463275447]
Learning-based control algorithms require data collection with abundant supervision for training.
We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
arXiv Detail & Related papers (2020-05-09T05:57:43Z) - Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs)
We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system.
To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
arXiv Detail & Related papers (2020-04-07T12:29:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.