Aerial robot plans and catches uncooperative moving targets fast
Aerial GRIPPER: A Gradient-based Real-time Inverse-game Predictor and Planner
Robotics
Summary
Capturing moving objects that don't want to be caught is a very hard problem. The authors present a robotic system that flies and grabs such targets by predicting their actions and planning the best way to catch them. Their method learns the target’s behavior quickly and updates its plan in real time, running more than 50 times per second using efficient math tricks. They also built a controller to keep the drone steady despite disturbances, ensuring accurate captures. Tests in simulations and real life show their system works reliably in challenging situations.
What this means in practice
- •For drone delivery teams: Capture and transport moving objects that try to evade, improving logistics in unstable environments.
- •For security drone operators: Track and apprehend evasive targets using a drone that predicts and adapts to target behavior in real time.
Authors
Zeshuai Chen, Meng Wang, Jindou Jia, Xiang Yu, Lei Guo
Abstract
Accurate capture of non-cooperative targets is critical. In an attempt to tackle this intractable challenge, an aerial gripper system integrated with a Gradient-based Real-time Inverse-game Predictor and PlannER (GRIPPER) framework is proposed. The interaction is formulated as a general-sum pursuit-evasion game under incomplete information. Specifically, underlying cost parameters of the target are inferred online, and the open-loop Nash equilibrium (OLNE) strategy is iteratively refined within a receding-horizon loop. To ensure high-frequency execution, a computationally friendly gradient-based inverse-game solver is developed. Without explicit computation of the Hessian inverse, the optimized solution is updated (> 50 Hz) based on implicit differentiation and fast Hessian-vector products. Meanwhile, an anti-disturbance controller is developed to overcome disturbances of uncertain payload and gripper actuation, enabling precise tracking of the planned trajectory and accurate grasping of the target. Simulations and real-world experiments illustrate the superior computational efficiency and task performance of GRIPPER. The task of capturing and delivering a non-cooperative target is accomplished, highlighting the robustness, adaptability, and real-time performance of the framework in highly adversarial scenarios.