Abstract
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 letter, a Decentralized Ability-Aware Adaptive Control (DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C) 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's 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's 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.
Highlights
C OLLABORATION with other agents can often be beneficial
The main contributions of this letter are summarised as follows: 1) A nominal task ellipsoid is defined based on the common manipulation task, and it is used to optimize the force capability of each manipulator
In order to track the desired trajectory of the object during decentralized multi-robot collaborative manipulation, we propose an ability-aware adaptive controller in which the force capability of each robot is considered
Summary
C OLLABORATION with other agents can often be beneficial. For example, a multi-robot team like the one shown in Fig. 1 is more dexterous and robust in heavy and large object manipulation tasks [1] than a single robot. We investigate multi-robot collaborative manipulation tasks, where a decentralized robot team needs to achieve a common objective, while each robot has different motion and force capabilities. We propose a method to maximize the force capability of each robot while designing a decentralized adaptive controller Using this framework, we achieve the shared manipulation task under modelling uncertainties, input constraints and band-limited communication. Considering a multi-robot collaborative manipulation task, a decentralized ability-aware adaptive control (DA3C ) framework (shown in Fig. 2) is proposed. The main contributions of this letter are summarised as follows: 1) A nominal task ellipsoid is defined based on the common manipulation task, and it is used to optimize the force capability of each manipulator. 3) Different heterogeneous multi-robot systems with input and communication constraints realize collaborative manipulation tasks using the proposed decentralized ability-aware adaptive control that guarantees stability and convergence.
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