A cooperative optimization strategy for distributed multi-robot manipulation with obstacle avoidance and internal performance maximization. (June 2021) - He, Yanhao; Wu, Min; Liu, Steven
Abstract: Reactive multi-robot manipulation can be of great assistance in many applications. In this article a distributed cooperation strategy for object manipulation with multiple robots in un-modelled environments is proposed to tackle workpiece transportation and dynamic obstacle avoidance simultaneously. A decentralised optimisation process will run first to generate robot pose references and then a joint-level control is responsible for tracking. Since in multi-arm manipulation systems the available workspace for each robot is more restricted, necessary internal performance optimisation is also included in the planning phase. A velocity-level optimum will be solved and then transformed to a position-level control reference so that it can be combined with many compliant interactive controllers such as impedance control. Although no torque or force sensor is required and each robot only has access to local information, when avoiding obstacles the coordination strategy can generate synchronised group motion so that internal forces are avoided. Only task-space variables will be communicated, therefore it is easy to team up different types of robots, making the system more flexible. Simulation and experiments with two redundant manipulators have been done to validate the proposed design, and data show that the online computation speed is fast. Highlights: A distributed cooperative manipulation control for multiple robots is presented. Whole-body dynamic obstacle avoidance and internal performance maximisation is achieved in real-time. The optimal solution is obtained by fast iterative quadratic programming. An efficient coordination mechanism helps to avoid conflicting motion. The coordination achieved by negotiation suppresses internal forces.