An Optimization Algorithm for Second-order Multi-robot Collaborative Object Lifting

摘要

In this paper, we formulate the task of multi-robot object collaborative lifting as a distributed optimization problem. The considered total objective function is the sum of the private objective functions corresponding to each robot, which are used to evaluate their location choices. To avoid the large horizontal component force on the multi-robot system during lifting, a coupled equality constraint is introduced to the formulated distributed optimization problem. The feasibility constraints of the optimal location are also considered in this paper. A novel distributed continuous-time algorithm is proposed to solve the collaborative object lifting task for second-order multi-robot systems. Each robot only needs to access its local evaluation function, local variables, and the information exchanged between neighbors. The proposed algorithm does not require exchanging location variables or velocity variables, which effectively prevents privacy leakage. Moreover, we prove the asymptotic convergence of the proposed algorithm to the exact optimal solution. Finally, the efficacy of the proposed algorithm is verified through the numerical simulation.