Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

Abstract

The transportation of large payloads can be made possible with Multi-Robot Systems (MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the acting robots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.

Multi-Robot Leader Grouping Consistent Formation Control Method Research with Low Convergence Time Based on Nonholonomic Constraints

“…The common formations include the straight line, wedge, column, triangle, double row, etc. [3], as shown in Figure 2. Arrows between robots indicate the flow of information.…”

“…In order to explain the relationship between the directed graph and adjacency matrix, between the diagonal matrix and Laplace matrix, a directed graph of a multi-robot system is assumed as shown in Figure 3. So, its adjoint matrix A, diagonal matrix D and Laplacian matrix L can be shown in Equation (3).…”

“…The information interaction protocol is used to determine and update the state of robots. There are a large number of such examples in the real world [1][2][3], as shown in Figure 1. At the same time, the multi-robot cluster movement also began to develop.…”