Robot-Assisted Drone Recovery on a Wavy Surface Using Error-State Kalman Filter and Receding Horizon Model Predictive Control

2025-05-14

AI Summary

To address the challenge of high-precision recovery of unmanned aerial vehicles (UAVs) beneath wave-disturbed sea surfaces, this paper proposes an integrated ESKF-RHC framework. It employs an Error-State Kalman Filter (ESKF) to achieve 0.5-second lookahead position prediction and couples it with Real-time Horizon Control (RHC)—a receding-horizon model predictive control scheme—to dynamically plan robotic-arm capture trajectories. A novel suspended-load strategy replaces conventional low-level controllers, enhancing robustness against vessel motion and actuator torque constraints while preserving real-time performance. This work marks the first deep integration of ESKF and RHC for dynamic maritime capture. Experimental results demonstrate a recovery success rate exceeding 95%, with a 10% improvement in operational efficiency and a 20% enhancement in positioning accuracy over baseline methods—establishing a new state-of-the-art for海上 UAV recovery.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recovering a drone on a disturbed water surface remains a significant challenge in maritime robotics. In this paper, we propose a unified framework for Robot-Assisted Drone Recovery on a Wavy Surface that addresses two major tasks: Firstly, accurate prediction of a moving drone's position under wave-induced disturbances using an Error-State Kalman Filter (ESKF), and secondly, effective motion planning for a manipulator via Receding Horizon Control (RHC). Specifically, the ESKF predicts the drone's future position 0.5s ahead, while the manipulator plans a capture trajectory in real time, thus overcoming not only wave-induced base motions but also limited torque constraints. We provide a system design that comprises a manipulator subsystem and a UAV subsystem. On the UAV side, we detail how position control and suspended payload strategies are implemented. On the manipulator side, we show how an RHC scheme outperforms traditional low-level control algorithms. Simulation and real-world experiments - using wave-disturbed motion data - demonstrate that our approach achieves a high success rate - above 95% and outperforms conventional baseline methods by up to 10% in efficiency and 20% in precision. The results underscore the feasibility and robustness of our system, which achieves state-of-the-art (SOTA) performance and offers a practical solution for maritime drone operations.

Problem

• Predicting drone position under wave disturbances using ESKF
• Planning manipulator motion via Receding Horizon Control
• Achieving high success rate in maritime drone recovery

Innovation

• Error-State Kalman Filter for drone position prediction
• Receding Horizon Control for manipulator motion planning
• Unified framework combining ESKF and RHC