Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics capabilities of the robots, the limited communication between them, and the uncertainty of the system parameters. In this paper, a Decentralized Ability-Aware Adaptive Control is proposed to address these challenges based on two key features. Firstly, the common manipulation task is represented by the proposed nominal task ellipsoid, which is used to maximize each robot force capability online via optimizing its configuration. Secondly, a decentralized adaptive controller is designed to be Lyapunov stable in spite of heterogeneous actuation constraints of the robots and uncertain physical parameters of the object and environment. In the proposed framework, decentralized coordination and load distribution between the robots is achieved without communication, while only the control deficiency is broadcast if any of the robots reaches its force limits. In this case, the object reference trajectory is modified in a decentralized manner to guarantee stable interaction. Finally, we perform several numerical and physical simulations to analyse and verify the proposed method with heterogeneous multi-robot teams in collaborative manipulation tasks.

翻译：多机器人团队可以比一个机器人实现更宽度、复杂和更重的有效有效载荷任务,但需要有效协作。多机器人团队由于机器人的动力和动态能力不同、彼此之间的交流有限以及系统参数的不确定性,因此极具挑战性。在本文件中,根据两个关键特点,提议采用分散式能力-软件适应控制来应对这些挑战。首先,共同操作任务由拟用的名义任务ELLEXID代表,该任务通过优化机器人的配置来最大限度地实现每个机器人的在线能力。第二,一个分散式适应控制器的设计是稳定的Lyapunov。在拟议框架中,机器人之间实现分散式协调和负载分配,没有通信,而如果任何机器人达到其功率极限,则只播放控制缺陷。在本案中,对对象参考轨进行了分散式的修改,以保障稳定互动。最后,我们进行了数项数字和物理模拟,以分析和核查拟议方法,在协作操控中采用混合多机器人团队。