In this paper, we introduce a novel approach of reducing the communication load in distributed model predictive control (DMPC) for mobile robots. The key idea is to project the predicted state trajectory onto a grid resulting in an occupancy grid prediction. This approach has the advantage of utilizing continuous optimization methods while only quantized information is exchanged. We consider non-holonomic mobile robots to numerically and experimentally investigate the proposed method.

The research topic of autonomous cars and the communication among them has attained much attention in the last years and is developing quickly. Among others, this research area spans fields such as image recognition, mathematical control theory, communication networks, and sensor fusion. We consider an intersection scenario where we divide the shared road space in different cells. These cells form a grid. The cars are modelled as an autonomous multi-agent system based on the Distributed Model Predictive Control algorithm (DMPC). We prove that the overall system reaches stability using Optimal Control for each multi-agent and demonstrate that by numerical results.