通过改进运动学模型与加权预测控制，提升无人机机械臂的精准操控能力。
Predictive Kinematic Coordinate Control for Aerial Manipulators based on Modified Kinematics Learning
- 引入闭环动态特性与在线残差学习，修正传统运动学模型
- 加权预测控制使无人机与机械臂协同运动精度提升59.6%
- 适用于复杂轨迹与移动目标跟踪任务，适合高精度空中操作场景
高精度操作始终是无人机机械臂的发展目标。本文研究了无人机机械臂的运动学坐标控制问题，提出一种基于改进运动学学习的预测性运动学坐标控制方法，包含学习型改进运动学模型和基于权重分配的模型预测控制（MPC）方案。相比现有方法，本方法具有显著优势：首先，运动学模型融合闭环动力学特性与在线残差学习，相比未考虑闭环动态与残差的方法，精度提升59.6%；其次，提出的考虑权重分配的MPC方案可协调四旋翼与机械臂的运动策略，相比不考虑权重分配的方法，能支持更多类型任务。通过复杂轨迹跟踪与移动目标跟踪实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
High-precision manipulation has always been a developmental goal for aerial manipulators. This paper investigates the kinematic coordinate control issue in aerial manipulators. We propose a predictive kinematic coordinate control method, which includes a learning-based modified kinematic model and a model predictive control (MPC) scheme based on weight allocation. Compared to existing methods, our proposed approach offers several attractive features. First, the kinematic model incorporates closed-loop dynamics characteristics and online residual learning. Compared to methods that do not consider closed-loop dynamics and residuals, our proposed method has improved accuracy by 59.6$\%$. Second, a MPC scheme that considers weight allocation has been proposed, which can coordinate the motion strategies of quadcopters and manipulators. Compared to methods that do not consider weight allocation, the proposed method can meet the requirements of more tasks. The proposed approach is verified through complex trajectory tracking and moving target tracking experiments. The results validate the effectiveness of the proposed method.
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