Multi-robot systems operate under stringent safety requirements such as avoiding collisions and respecting mutual distances. In recent years, control barrier functions (CBFs) have emerged as a gold standard to satisfy such constraints in real time, by ensuring set invariance at all times (i.e., the robots' state remains inside a safe set) with a small computation overhead. One limitation of CBF is that it is usually difficult to design it, which often leads to conservative choices that reduce performance.
In this thesis, you will explore a learning-based approach to improve the performance of CBF for multi-robot systems, focusing on collision avoidance between robots [1]. If time and progress permit, nonideal elements such as sensor noise and delays will be factored in to make the problem more realistic. If desired, simulations can be performed with ROS and Gazebo.
This thesis may involve a – possibly remote – collaboration with the National University of Singapore.
[1] L. Ballotta and R. Talak, “Safe Distributed Control of Multi-Robot Systems With Communication Delays,” IEEE Transactions on Vehicular Technology, 2025. Available at https://arxiv.org/abs/2402.09382