PhD Comprehensive Exam | Mohammad Aali, Safety-critical Motion Control with an Application in Multi-body Mobile Robots

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Candidate

Mohammad Aali | Applied Mathematics, University of Waterloo

Title

Safety-critical Motion Control with an Application in Multi-body Mobile Robots

Abstract

Nowadays, we face an era in which robots are becoming more popular day by day. The application of artificial intelligence (AI) is not limited to computers and smartphones anymore, and the robots are not restricted to specific industrial purposes. The hardware development and advanced AI techniques are integrated together in the recent decades, which has led to a breakthrough in robotics. These developments show us that robots are getting better in doing tasks that we thought can be done only by humans. Therefore, they are becoming closer to our daily routine, and we will be in touch with them sooner than we thought. However, one common concern regarding this rapid advancement needs to be answered, which is summarized in one question: are we safe when we delegate a task to robots?

Control barrier functions (CBFs) recently introduced a systematic way to guarantee the system's safety through set invariance. Together with a nominal control method, it establishes a safety-critical control mechanism. The resulting safety 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. In this work, we introduce a multiple CBFs scheme which enforces several safety constraints with high relative degrees. This control structure is essential in many challenging robotic applications that need to meet several safety criteria simultaneously.

In order to illustrate the capabilities of the proposed method, we have addressed the problem of reactive obstacle avoidance for a class of tractor-trailer systems. One of the fundamental issues in autonomous tractor-trailer systems design is safety. The lack of fast response due to poor maneuverability makes reactive obstacle avoidance difficult for these systems. We develop a control structure based on a multiple CBFs scheme for a multi-steering tractor-trailer system to ensure a collision-free maneuver for both the tractor and trailer in the presence of several obstacles. Model predictive control is selected as the nominal tracking controller, and the proposed control strategy is tested in several challenging scenarios.