🤖 AI Summary
This paper addresses the challenge of autonomous interception of non-cooperative, dynamic targets by heterogeneous mobile platforms—unmanned aerial vehicles, ground vehicles, and spacecraft—under conditions of no global localization, limited field-of-view, and frequent occlusions. Methodologically, it proposes a monocular vision–driven general interception framework integrating extended Kalman filter–based relative pose estimation, history-conditioned target trajectory prediction, and real-time constrained convex optimization for receding-horizon motion planning. It is the first work to systematically validate cross-platform interception feasibility under weak observability and to establish a unified kinematic adaptation paradigm. Experiments demonstrate sub-0.15 m interception error, >94% success rate, and real-time execution on embedded platforms such as Jetson Orin. The core contribution is the first general-purpose visual interception architecture designed for multiple robot classes, achieving simultaneous robustness against sensing limitations and computational efficiency.
📝 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.