At FRPG, multi-robot systems form the backbone of most of our research. We study both centralized and distributed perception and control of robots in a team performing cooperative tasks. We especially focus on the intertwining of perception and control in this context

Formation control: To achieve a required degree of quality in perception, usually quantified as estimation uncertainty, a team of robots/agents need to coordinate their actions and share independently-obtained information with their teammates. To achieve this, we develop classical (model predictive control) and AI-based methods (reinforcement learning) that explicitly account for perceptual gain in their objective function or rewards, respectively. At the same time the formation control methods must account for environmental and task-related constraints such as collision avoidance and angular configuration maintenance. To this end, we have introduced a novel force function-based approach that keeps the formation objective convex. In other methods, we have focused on heterogeneous robot teams.

Cooperative Perception: Fusion of information among robots/agents in a team is fundamental for a coherent understanding of the environment and, consequently, for reliable decision making as a team. In this regard, we investigate and develop a range of methods for cooperative target tracking, cooperative localization of robots as well as unified methods for performing tracking and localization simultaneously. Furthermore, we also investigate cooperative human and animal pose estimation methods. Our methods include both parametric (EKF) and non-parametric (Particle Filters) approaches.