This paper presents a novel solution for UAV control in cooperative multi-robot systems, which can be used in various scenarios such as leader-following, landing on a moving base, or specific relative motion with a target. Unlike classical methods that tackle UAV control in the world frame, we directly control the UAV in the target coordinate frame, without making motion assumptions about the target. In detail, we formulate a non-linear model predictive controller of a UAV, referred to as the agent, within a non-inertial frame (i.e., the target frame). The system requires the relative states (pose and velocity), the angular velocity and the accelerations of the target, which can be obtained by relative localization methods and ubiquitous MEMS IMU sensors, respectively. This framework eliminates dependencies that are vital in classical solutions, such as accurate state estimation for both the agent and target, prior knowledge of the target motion model, and continuous trajectory re-planning for some complex tasks. We have performed extensive simulations to investigate the control performance with varying motion characteristics of the target. Furthermore, we conducted real robot experiments, employing either simulated relative pose estimation from motion capture systems indoors or directly from our previous relative pose estimation devices outdoors, to validate the applicability and feasibility of the proposed approach.

翻译：本文提出了一种面向协同多机器人系统中无人机控制的新方案，可应用于领航-跟随、移动平台降落以及与目标保持特定相对运动等多种场景。与在全局坐标系中处理无人机控制的经典方法不同，本方法无需对目标运动做任何假设，直接在目标坐标系中控制无人机。具体而言，我们在非惯性系（即目标坐标系）中构建了无人机的非线性模型预测控制器（此处称为智能体）。该系统需要目标的相对状态（位姿与速度）、角速度及加速度，这些参数可分别通过相对定位方法和通用MEMS惯性测量单元传感器获取。本框架消除了经典解决方案中至关重要的依赖性，例如智能体与目标的精确状态估计、目标运动模型的先验知识，以及某些复杂任务所需的连续轨迹重规划。我们通过大量仿真研究了不同目标运动特性下的控制性能。此外，我们开展了真实机器人实验，分别利用室内运动捕捉系统提供的模拟相对位姿估计值，以及户外基于前期开发的相对位姿估计设备直接获取的数据，验证了所提方法的适用性与可行性。