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This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories and assigns tasks for each element of the group in real-time, while also guaranteeing the communication network of the MAS to be robustly connected at all times. Additionally, we provide a data-based methodology to estimate the disturbances sets required by the robust MPC formulation. The results are demonstrated with experiments in two obstacle-filled scenarios.

This work is concerned with the problem of planning trajectories for a group of mobile robots operating in a obstacle-filled environment. The objective of the robots is to cooperatively reach targets sets, avoiding collisions between themselves and with obstacles, while maintaining the robust connectivity of their communication network. Model predictive control with mixed-integer encoding is used to plan the trajectories in a centralized fashion. Two novel constraints are introduced to cope with the robustness connectivity problem. Simulations are provided to evaluate the proposal.

This note concerns the problem of k-hop connectivity in a network of mobile agents, which is achieved if any pair of agents can communicate with each other through a link of k-1 or fewer intermediate nodes. We propose linear constraints involving binary optimization variables to ensure k-hop connectivity. Such constraints are then integrated into a Mixed-Integer Linear Programming (MILP) trajectory planning model. Simulation results illustrate the application of the proposed method and the effect of varying k in the context of a mission involving the visitation of multiple targets.

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