In this paper, we propose a cooperative long-term task execution (LTTE) algorithm for protecting a moving target into the interior of an convex hull by a team of robots resiliently in the changing environments. Particularly, by designing target-approaching and sensing-neighbor collision-free subtasks, and incorporating these subtasks into the constraints rather than the traditional cost function in an online constraint-based optimization framework, the proposed LTTE can systematically guarantee target convoying under in the -dimensional Euclidean space. Then, the introduction of slack variables allows for the constraint violation of different subtasks; i.e., the attraction from target-approaching constraints and the repulsion from collision-avoidance constraints, which results in the desired formation with arbitrary spatial ordering sequences. Rigorous analysis is provided to guarantee asymptotical convergence with challenging nonlinear couplings induced by collision-free constraints. Finally, 2D experiments using three autonomous mobile robots (AMRs) are conducted to validate the effectiveness of the proposed algorithm, and 3D simulations tackling changing environmental elements, such as different initial positions, some robots suddenly breakdown and static obstacles are presented to demonstrate the multi-dimensional adaptability, robustness and the ability of obstacle avoidance of the proposed method.

中文翻译：

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使用长期任务执行进行移动目标护送的灵活排序多机器人协调

在本文中，我们提出了一种协作式长期任务执行（LTTE）算法，用于由一组机器人在不断变化的环境中弹性地保护移动目标进入凸包内部。特别是，通过设计目标接近和感知邻居无碰撞子任务，并将这些子任务纳入约束而不是基于在线约束的优化框架中的传统成本函数，所提出的LTTE可以系统地保证在以下条件下的目标护送：维欧几里得空间。然后，引入松弛变量允许不同子任务的约束违反；即，来自目标接近约束的吸引和来自碰撞避免约束的排斥，这导致具有任意空间排序序列的期望编队。提供严格的分析来保证渐近收敛与由无碰撞约束引起的具有挑战性的非线性耦合。最后，使用三个自主移动机器人（AMR）进行了 2D 实验来验证所提出算法的有效性，并针对不断变化的环境因素（例如不同的初始位置、一些机器人突然故障和静态障碍物）进行了 3D 模拟，以证明多-所提出方法的维度适应性、鲁棒性和避障能力。