Coverage Control in Multi-Robot Systems via Graph Neural Networks

This paper develops a decentralized approach to mobile sensor coverage by a multi-robot system. We consider a scenario where a team of robots with limited sensing range must position itself to effectively detect events of interest in a region characterized by areas of varying importance. Towards this end, we develop a decentralized control policy for the robots -- realized via a Graph Neural Network -- which uses inter-robot communication to leverage non-local information for control decisions. By explicitly sharing information between multi-hop neighbors, the decentralized controller achieves a higher quality of coverage when compared to classical approaches that do not communicate and leverage only local information available to each robot. Simulated experiments demonstrate the efficacy of multi-hop communication for multi-robot coverage and evaluate the scalability and transferability of the learning-based controllers.

Evaluating Robust, Perception Based Control with Quadrotors

Traditionally, controllers and state estimators in robotic systems are designed independently. Controllers are often designed assuming perfect state estimation. However, state estimation methods such as Visual Inertial Odometry (VIO) drift over time and can cause the system to misbehave. While state estimation error can be corrected with the aid of GPS or motion capture, these complementary sensors are not always available or reliable. Recent work has shown that this issue can be dealt with by synthesizing robust controllers using a data-driven characterization of the perception error, and can bound the system's response to state estimation error using a robustness constraint. We investigate the application of this robust perception-based approach to a quadrotor model using VIO for state estimation and demonstrate the benefits and drawbacks of using this technique in simulation and hardware. Additionally, to make tuning easier, we introduce a new cost function to use in the control synthesis which allows one to take an existing controller and "robustify" it. To the best of our knowledge, this is the first robust perception-based controller implemented in real hardware, as well as one utilizing a data-driven perception model. We believe this as an important step towards safe, robust robots that explicitly account for the inherent dependence between perception and control.