A General Purpose Method for Robotic Interception of Non-Cooperative Dynamic Targets
- URL: http://arxiv.org/abs/2512.20769v1
- Date: Tue, 23 Dec 2025 21:14:03 GMT
- Title: A General Purpose Method for Robotic Interception of Non-Cooperative Dynamic Targets
- Abstract summary: This paper presents a general purpose framework for autonomous, vision-based interception of dynamic, non-cooperative targets.<n>The approach relies solely on a monocular camera with fiducials for target tracking and operates entirely in the local observer frame.<n>Results from simulated and physical experiments demonstrate robust performance with low interception errors.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a general purpose framework for autonomous, vision-based interception of dynamic, non-cooperative targets, validated across three distinct mobility platforms: an unmanned aerial vehicle (UAV), a four-wheeled ground rover, and an air-thruster spacecraft testbed. The approach relies solely on a monocular camera with fiducials for target tracking and operates entirely in the local observer frame without the need for global information. The core contribution of this work is a streamlined and general approach to autonomous interception that can be adapted across robots with varying dynamics, as well as our comprehensive study of the robot interception problem across heterogenous mobility systems under limited observability and no global localization. Our method integrates (1) an Extended Kalman Filter for relative pose estimation amid intermittent measurements, (2) a history-conditioned motion predictor for dynamic target trajectory propagation, and (3) a receding-horizon planner solving a constrained convex program in real time to ensure time-efficient and kinematically feasible interception paths. Our operating regime assumes that observability is restricted by partial fields of view, sensor dropouts, and target occlusions. Experiments are performed in these conditions and include autonomous UAV landing on dynamic targets, rover rendezvous and leader-follower tasks, and spacecraft proximity operations. Results from simulated and physical experiments demonstrate robust performance with low interception errors (both during station-keeping and upon scenario completion), high success rates under deterministic and stochastic target motion profiles, and real-time execution on embedded processors such as the Jetson Orin, VOXL2, and Raspberry Pi 5. These results highlight the framework's generalizability, robustness, and computational efficiency.
Related papers
- Finite-Time Curvature-Constrained Vector Field for Saturation-Free Motion Planning of Nonholonomic Robots [32.96414771882991]
Accurately steering a robot to a target configuration is fundamental in engineering, yet remains challenging for nonholonomic mobile robots.<n>Most existing VF-based methods cannot explicitly generate trajectories satisfying curvature constraints.<n>We propose a general motion planning and control framework consisting of a finite-time curvature-constrained vector field (FT-C2VF) and a saturation-free control law.
arXiv Detail & Related papers (2026-07-20T04:36:20Z) - Learning Smooth and Robust Space Robotic Manipulation of Dynamic Target via Inter-frame Correlation [12.529803316934176]
In microgravity environments, objects are typically free-floating, lacking the support and frictional constraints found on Earth.<n>This letter proposes a data-driven space robotic manipulation approach that integrates historical temporal information and inter-frame correlation mechanisms.
arXiv Detail & Related papers (2026-03-29T06:27:30Z) - RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation [68.7948300643741]
We propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention.<n>RC-NF decouples the processing of task-aware robot and object states within the normalizing flow.<n>It achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks.
arXiv Detail & Related papers (2026-03-11T10:14:37Z) - Dual-Agent Multiple-Model Reinforcement Learning for Event-Triggered Human-Robot Co-Adaptation in Decoupled Task Spaces [3.349003999623489]
This paper presents a shared-control rehabilitation policy for a custom 6-degree-of-freedom upper-limb robot.<n>The patient governs the primary reaching direction using binary commands, while the robot autonomously manages corrective motions.
arXiv Detail & Related papers (2026-03-06T11:15:10Z) - Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons [69.87766750714945]
General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations.<n>We introduce Robometer, a scalable reward modeling framework that combines intra-trajectory progress supervision with inter-trajectory preference supervision.<n>Robometer is trained with a dual objective: a frame-level progress loss that anchors reward magnitude on expert data, and a trajectory-comparison preference loss that imposes global ordering constraints.
arXiv Detail & Related papers (2026-03-02T17:38:58Z) - Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction [57.252599851031505]
We introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI.<n>Pro-HOI is a generalizable framework for robust humanoid loco-manipulation.<n> Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness.
arXiv Detail & Related papers (2026-03-01T14:19:59Z) - Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments [9.325443556436436]
Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments.<n>This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization.<n>To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot.
arXiv Detail & Related papers (2026-02-25T22:49:54Z) - Multi-UAV Trajectory Optimization for Bearing-Only Localization in GPS Denied Environments [0.0]
This work presents a new trajectory optimization framework that enables cooperative target localization using UAVs with fixed, non-gimballed cameras.<n>Results demonstrate that coordinated UAVs with fixed, non-gimballed cameras achieve localization accuracy that meets or exceeds that of single gimballed systems.
arXiv Detail & Related papers (2026-02-11T18:29:05Z) - Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees [39.146761527401424]
We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically.<n>The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities.
arXiv Detail & Related papers (2026-01-06T15:30:02Z) - NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation [54.87964060934928]
Vision-Language-Action (VLA) models confront critical barriers to real-world deployment, most notably catastrophic forgetting.<n>We propose the Narrowing of Trajectory VLA framework: a novel approach that narrows its focus to sparse trajectories.<n>NoTVLA achieves superior performance and generalization compared to pi0 while operating under two critical constraints.
arXiv Detail & Related papers (2025-10-04T18:26:55Z) - NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
arXiv Detail & Related papers (2025-06-23T14:28:30Z) - Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
arXiv Detail & Related papers (2024-12-01T09:37:46Z) - A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with
Dynamic Obstacle Trajectory Prediction and Its Application with LLMs [6.747468447244154]
This paper proposes a vision-based planning system that combines tracking and trajectory prediction of dynamic obstacles to achieve efficient and reliable autonomous flight.
We conduct experiments in both simulation and real-world environments, and the results indicate that our approach can successfully detect and avoid obstacles in dynamic environments in real-time.
arXiv Detail & Related papers (2023-11-21T08:09:00Z) - SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal.
We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints.
recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution.
A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z)
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.