DiLeBaCo · Distributed Learning-Based Control for Multi-Agent Systems

Distributed Learning-Based Control for Multi-Agent Systems

Multi-agent systems offer a tremendous potential to improve the quality of modern society life. For instance, robotic networks will increase food production, or engage in search and rescue missions. Fleets of autonomous cars will reduce traffic congestion and fuel consumption while increasing road safety. With 45% of all freight being transported by road, transportation makes up 26% of the total EU energy consumption and accounts for 18% of the greenhouse gas emissions. Fuel reduction in this area will have a significant impact on the environment. This will be achieved through platooning where heavy-duty vehicles drive close to each other in groups, which reduces their aerodynamic drag and thus increases their fuel efficiency. The challenge in controlling autonomous systems is their increasing complexity. This is due to interactions between multiple autonomous agents in a system, and by the complex dynamic environments they are operated in. Therefore, state-of-the-art classical control methods are either overly conservative leading to a poor control performance, or they cannot guarantee safety. In particular, since these classical methods are based on analytical models they might even be impossible to be used. The objective of the research project therefore is to fuse methods from machine learning with control approaches in order to guarantee both performance and safety of the controlled systems. In particular, previously seen data (from experience) or simulated data (rollouts) are used in order to learn missing information arising from the complexities of the systems, i.e., about the optimal control policies, about the dynamical model of the systems, or about the complex and dynamic environment they are navigated in. The specific goals of the project are to 1) develop novel control algorithms by fusing methods from machine learning with control approaches 2) to guarantee safety and performance of the developed algorithms 3) to focus on data-efficiency, scalability and computational efficiency of these methods, such that they can be applied online in real-time, and for complex multi-agent systems. The project has shown that in the area of complex systems (dealing with multiple coupled agents, dynamic environments and safety-critical systems) the combination of machine learning methods with the framework of model predictive control has a great potential to immensely increase the performance of classical control algorithms, while at the same time providing safety guarantees. This direction should further be exploited in order to bring high performing and safe algorithms to relevant real-world applications in areas such as heavy-duty platooning, autonomous driving or robotic networks.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз