Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors
- URL: http://arxiv.org/abs/2607.24369v1
- Date: Mon, 27 Jul 2026 12:42:38 GMT
- Title: Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors
- Abstract summary: This work presents a motion motion framework for UAV air corridors.<n>The planner is based on a mixed-integer tracking model that enforces external feasibility.
- Score: 1.2617078020344616
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.
Related papers
- NeurRAFT: Robot Motion Planning via Anchor-Level Flow Matching with Clearance-Aware Preference Tuning [5.973392871392725]
We present NeurRAFT, a generative planning framework based on anchor-level flow matching and clearance-aware preference tuning.<n> Experiments show substantial improvements over state-of-the-art planners, while real-world experiments demonstrate zero-shot transfer to a Franka robot.
arXiv Detail & Related papers (2026-08-25T03:31:24Z) - Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems [6.655837620295535]
Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances.<n>We propose a unified framework that integrates differential flatness-based trajectory generation with predictive, constraint-aware control.<n>We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories.
arXiv Detail & Related papers (2026-08-21T01:25:35Z) - Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling [7.761673975696802]
Single-Robot Motion Planning (SRMP) is challenging under complex constraints and computational limits.<n>Recent Model-Based Diffusion (MBD) recasts the SRMP to guide samples from a posterior diffusion trajectories.<n>Our method achieves higher safety & success rates, faster convergence, and lower final costs than baseline planners.
arXiv Detail & Related papers (2026-07-16T01:04:32Z) - Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation [57.9825059129472]
We propose a planner-agnostic attack framework for tolerance-aware manipulation.<n>Our approach shifts the evaluation paradigm to task-level feasibility over goal regions.<n>Online, we formulate the attack as a budgeted maximum-coverage optimization.
arXiv Detail & Related papers (2026-07-04T08:14:44Z) - Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching [3.1807155456602274]
The emphergodic coverage problem is central to UAV-assisted data collection and sensing, robotic exploration, and mobile monitoring.<n>For flying agents, trajectories must balance coverage fidelity against tight energy budgets, no-fly zones, and acceleration limits.<n>We propose a emphepushforward framework that decouples ergodicity from density matching.
arXiv Detail & Related papers (2026-05-13T06:34:08Z) - Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems [54.887871365121775]
Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints.<n>We propose a hybrid multi-robot control framework that jointly optimize task and motion planning.<n>Our approach consistently improves task success over motion-agnostic and VLA-based baselines.
arXiv Detail & Related papers (2026-04-22T22:58:47Z) - Aligning Latent Spaces with Flow Priors [72.24305287508474]
This paper presents a novel framework for aligning learnable latent spaces to arbitrary target distributions by leveraging flow-based generative models as priors.<n> Notably, the proposed method eliminates computationally expensive likelihood evaluations and avoids ODE solving during optimization.
arXiv Detail & Related papers (2025-06-05T16:59:53Z) - Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets [6.0967385124149756]
Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly demanding.<n>In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length.<n>A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the trajectory generation of feasible trajectories in the flat output space.
arXiv Detail & Related papers (2025-03-18T11:59:24Z) - RFPPO: Motion Dynamic RRT based Fluid Field - PPO for Dynamic TF/TA Routing Planning [2.299967328525874]
This paper proposes the Motion Dynamic RRT based Fluid Field - PPO for dynamic TF/TA routing planning.<n>A reward function is designed to encourage strategies for obstacle avoidance, terrain following, terrain avoidance, and safe flight.<n> Experimental results on real DEM data demonstrate that our algorithm can complete long-distance flight tasks through collision-free trajectory planning.
arXiv Detail & Related papers (2024-12-28T09:42:02Z) - Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice [28.37162791852146]
Inevitable tracking error necessitates robustification of the nominal plan to ensure safety.
In this work, we present a novel method leveraging massively parallel simulation to learn a dynamic tube representation.
The resulting Dynamic MPC Tube is applied to the 3D hopping robot ARCHER.
arXiv Detail & Related papers (2024-11-22T21:22:51Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - DDPEN: Trajectory Optimisation With Sub Goal Generation Model [70.36888514074022]
In this paper, we produce a novel Differential Dynamic Programming with Escape Network (DDPEN)
We propose to utilize a deep model that takes as an input map of the environment in the form of a costmap together with the desired position.
The model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions.
arXiv Detail & Related papers (2023-01-18T11:02:06Z) - Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models [76.32775745488073]
We present a novel dataset and modeling framework designed to study motion planning in understructured environments.<n>We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset.
arXiv Detail & Related papers (2022-09-19T05:06:57Z)
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.