Aperiodic Communication for MPC in Autonomous Cooperative Landing

In this paper, we focus on the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). These heterogeneous agents with nonlinear dynamics are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on the Distributed Model Predictive Control (MPC). One of our main contributions is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm requires that agents update the rendezvous location only when they are not guaranteed to reach it. Therefore, the exchange of information occurs aperiodically and proposed algorithm improves the communication efficiency. Furthermore, we prove the recursive feasibility of the algorithm. The simulation results show the effectiveness of our algorithm applied to the problem of autonomous cooperative landing.Comment: 7 pages, 6 figures, This work has been submitted to IFAC for possible publication, 7th IFAC Conference on Nonlinear Model Predictive Control 202

Prediction-Based Leader-Follower Rendezvous Model Predictive Control with Robustness to Communication Losses

In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The algorithm is executing DMPC with exchange of trajectories between the agents at a sufficient rate. In the case of loss of communication, and given the sensor setup, agents are predicting the trajectories of other agents based on the available measurements and prior information. The predictions are then used as the reference inputs to DMPC. During the landing, the followers are tasked with avoidance of USV-dependent obstacles and inter-agent collisions. In the proposed distributed algorithm, all agents solve their local optimization problem in parallel and we prove the convergence of the proposed algorithm. Finally, the simulation results support the theoretical findings.Comment: 8 pages, 5 figures, submitted to 62nd IEEE Conference on Decision and Control 202