Decentralized Ability-Aware Adaptive Control for Multi-robot Collaborative Manipulation
This work aims to improve the robustness and efficiency of multi-robot collaborative manipulation for industries requiring complex object handling, particularly for heterogeneous robot teams.
This paper proposes a Decentralized Ability-Aware Adaptive Control framework for multi-robot collaborative manipulation. It addresses challenges like heterogeneous robot capabilities and limited communication by optimizing robot configurations to maximize force capability and designing a decentralized adaptive controller that is Lyapunov stable despite actuation constraints and uncertain parameters. The framework achieves decentralized coordination and load distribution without communication, only broadcasting control deficiency if robots reach force limits, which then modifies the object reference trajectory to ensure stable interaction.
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.