Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets

• URL: http://arxiv.org/abs/2503.14184v1
• Date: Tue, 18 Mar 2025 11:59:24 GMT
• Title: Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets
• Authors: Atharva Ghotavadekar, František Nekovář, Martin Saska, Jan Faigl,
• Abstract summary: 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.
• Score: 6.0967385124149756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agile trajectory planning can improve the efficiency of multi-rotor Uncrewed Aerial Vehicles (UAVs) in scenarios with combined task-oriented and kinematic trajectory planning, such as monitoring spatio-temporal phenomena or intercepting dynamic targets. Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly computationally demanding. That reduces the prediction horizon length, leading to a decrease in solution quality. Besides, the fixed time-step length limits the utilization of the available UAV dynamics in the target neighborhood. In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length. A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the generation of feasible trajectories in the flat output space. Based on the presented evaluation results and experimentally validated deployment, the proposed method increases the solution quality by enabling planning for long flight segments but allowing tightly sampled maneuvering.

Related papers

• Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion [62.91968752955649]
  This paper tackles a novel problem, extendable long-horizon planning-enabling agents to plan trajectories longer than those in training data without compounding errors. We propose an augmentation method that iteratively generates longer trajectories by stitching shorter ones. HM-Diffuser trains on these extended trajectories using a hierarchical structure, efficiently handling tasks across multiple temporal scales.
  arXiv  Detail & Related papers  (2025-03-25T22:52:46Z)
• Real-Time Sampling-based Online Planning for Drone Interception [18.340019191662957]
  We propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization.<n>The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions.
  arXiv  Detail & Related papers  (2025-02-20T03:48:38Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.<n>The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.<n>We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)
• 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)
• Biologically Inspired Swarm Dynamic Target Tracking and Obstacle Avoidance [0.0]
  This study proposes a novel artificial intelligence (AI) driven flight computer to track dynamic targets using a distributed drone swarm for military applications. The controller integrates a fuzzy interface, a neural network enabling rapid adaption, predictive capability and multi-agent solving.
  arXiv  Detail & Related papers  (2024-10-15T03:47:09Z)
• Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion [40.76697924496143]
  We propose an adaptive generative planning approach that adjusts replanning frequency based on the uncertainty of action predictions. Our method minimizes the need for frequent, computationally expensive, and redundant replanning while maintaining robust collision avoidance performance.
  arXiv  Detail & Related papers  (2024-09-25T14:03:58Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Adaptive Planning with Generative Models under Uncertainty [20.922248169620783]
  Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains. While continuous replanning at each timestep might seem intuitive because it allows decisions to be made based on the most recent environmental observations, it results in substantial computational challenges. Our work addresses this challenge by introducing a simple adaptive planning policy that leverages the generative model's ability to predict long-horizon state trajectories.
  arXiv  Detail & Related papers  (2024-08-02T18:07:53Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• 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)
• Bootstrap Motion Forecasting With Self-Consistent Constraints [52.88100002373369]
  We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints. The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. We show that our proposed scheme consistently improves the prediction performance of several existing methods.
  arXiv  Detail & Related papers  (2022-04-12T14:59:48Z)
• Time-Optimal Planning for Quadrotor Waypoint Flight [50.016821506107455]
  Planning time-optimal trajectories at the actuation limit of a quadrotor is an open problem. We propose a solution while exploiting the full quadrotor's actuator potential. We validate our method in real-world flights in one of the world's largest motion-capture systems.
  arXiv  Detail & Related papers  (2021-08-10T09:26:43Z)

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.