Location
MC 6460
Candidate
Mohammad Aali | Applied Mathematics, University of Waterloo
Title
Safety-critical Motion Control with an Application in Multi-body Mobile Robots
Abstract
Control theory is one of the key ingredients of the remarkable rise in robotics. Due to technological advancements, the use of automated robots, which was once primarily limited to industrial and manufacturing settings, has now expanded to impact many different parts of everyday life. Various control strategies have been developed to satisfy a wide range of performance criteria arising from recent applications. These strategies have different characteristics depending on the problem they solve. But, they all have to guarantee stability before satisfying any performance-driven criteria. However, as robotic technologies become increasingly integrated into everyday life, they introduce safety concerns. For autonomous systems to be trusted by the public, they must guarantee safety. In recent years, the concept of set invariance has been incorporated into modern control strategies to enable systematic safety guarantees. In this thesis, we aim to develop safety-critical control methods that can guarantee safety while satisfying performance-driven requirements. In the proposed strategies, we considered formal safety guarantees, robustness to uncertainty, and computational efficiency as the highest design priorities. Each of them introduces new challenges which are addressed with theoretical contributions. We selected motion control in mobile robots as a use case for proposed controllers which is an active area of research integrating safety, stability, and performance in various scenarios. In particular, we focused on multi-body mobile robots, an area with limited research on safe operation. We provide a comprehensive survey of the recent methods that formalize safety for the dynamical systems via set invariance. A discussion on the strengths and limitations of each method demonstrates the capabilities of control barrier functions (CBFs) as a systematic tool for safety assurance in motion control. A safety filter module is also introduced as a tool to enforce safety. CBF constraints can be enforced as hard constraints in quadratic programming (QP) optimization, which rectifies the nominal control law based on the set of safe inputs.