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.

翻译：本文提出了一种通用的自主视觉框架，用于拦截动态非合作目标，并在三种不同的移动平台上进行了验证：无人机、四轮地面漫游车和空气推进器航天器测试平台。该方法仅依赖带有基准标记的单目相机进行目标跟踪，完全在局部观测者坐标系中运行，无需全局信息。本工作的核心贡献在于提出了一种简洁通用的自主拦截方法，可适配于具有不同动力学的机器人，并对有限可观测性且无全局定位条件下异构移动系统中的机器人拦截问题进行了全面研究。我们的方法整合了：(1) 用于间歇测量条件下相对位姿估计的扩展卡尔曼滤波器，(2) 用于动态目标轨迹预测的历史条件运动预测器，以及(3) 通过实时求解约束凸优化问题的滚动时域规划器，以确保时间高效且运动学可行的拦截路径。我们的工作假设可观测性受到部分视场、传感器数据丢失和目标遮挡的限制。实验在这些条件下进行，包括无人机在动态目标上的自主着陆、漫游车交会与领航-跟随任务，以及航天器近距离操作。仿真和物理实验结果表明，该方法具有鲁棒的性能，拦截误差低（包括在位置保持期间和场景完成时），在确定性和随机性目标运动模式下均具有高成功率，并能在Jetson Orin、VOXL2和Raspberry Pi 5等嵌入式处理器上实时运行。这些结果凸显了该框架的通用性、鲁棒性和计算效率。