Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to locate fire sources in an efficient way. In order to achieve this goal, the robots should cooper...

In this paper we propose a cooperative distributed linear model predictive control strategy applicable to any finite number of subsystems satisfying a stabilizability condition. The control strategy has the following features: hard input constraints are satisfied; terminating the iteration of the distributed controllers prior to convergence retains closed-loop stability; in the limit of iterati...

This paper deals with the decentralized control of an interconnected system, where each subsystem has models of all other subsystems (subject to uncertainty). A decentralized controller is constructed based on a reference centralized controller. It is shown that when a priori knowledge of each subsystem about the other subsystems’ models is exact, then the decentralized closed-loop system can p...

Decentralized control and agent-based modeling techniques are employed to determine optimal spacecraft motion for formations having multifaceted objectives in complex dynamic regimes. Cooperative satellite “agents” share a common objective (high resolution imaging) and simultaneously pursue private goals (minimal fuel usage). An algorithm is developed based on dual decomposition. The solution f...

Scheduling of prosumer flexibility is challenging in finding an optimal allocation energy resources for heterogeneous goals under various forecast uncertainties and operation constraints. This study addresses this challenge by introducing a bottom-up framework cooperative scheduling that relies on decentralized network agents to perform coordinated decision-making select subset households’ net ...

The demand for autonomous UAV swarm operations has been on the rise following success of UAVs in various challenging tasks. Yet conventional control approaches are inadequate coping with scalability, computational requirements, and real-time performance. In this paper, we demonstrate capability emerging multi-agent reinforcement learning (MARL) to successfully efficiently make sequential decisi...