Abstract: Safe planning and control of multiple robots performing complex tasks has been a challenging problem. Methods that offer guarantees on safety and mission satisfaction generally do not scale well. On the other hand, more computationally tractable approaches do not provide significant safety guarantees. In this talk, I will present a family of robust and predictive motion planning and control methods that overcome these limitations for a wide variety of task objectives, represented using Signal Temporal Logic (STL). Starting from the given STL specification, we formulate a non-convex optimization problem, which can be efficiently solved to local optimality in both centralized and decentralized manners. We also formulate constraints that result in trajectories that can be tracked near perfectly by off-the-shelf lower-level controllers. The performance and scalability of the methods will be demonstrated through multi-robot simulation studies and experiments on quadrotor aerial robots and non-holonomic ground robots. Finally, I will present ongoing work on extending these methods to systems with partially known dynamics.
Biography: Dr. Yash Vardhan Pant is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, where he leads the Control, Learning and Logic (CL2) group. He received his PhD in Electrical Engineering from the University of Pennsylvania in 2019, and was a postdoctoral fellow at the University of California at Berkeley from 2019-2021, before joining Waterloo in the summer of 2021. His research focuses on decision-making for multi-agent and autonomous systems, drawing on elements of Control Theory, Machine Learning, Formal Methods and Optimization, with application to ground robots, human-robot interaction and swarms of aerial robots. More details about Dr. Pant’s research can be found at link.