Towards A Reliable Optimization-based Design Framework for Autonomy and Control of Robotic Systems

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The autonomous robotic vehicle systems (ARVs), such as unmanned aerial vehicles, autonomous underwater vehicles and mobile robots, present promising tools to release people from boring, repetitive, or dangerous jobs, and accomplish various meaningful tasks in an efficient, autonomous, and cost-effective way. The complex and dynamic environment in practical ARV application scenarios requires accurate and reliable control of the ARV. However, the autonomous control system based on conventional linear control theories often come with strong assumptions which may not be satisfied in practice and hence lead to poor performances. The difficulty in handling system constraints (such as limited sensing, computing and actuating capabilities) further rules out the possibility of using conventional methods to achieve optimal performance as the optimum is likely to be located on the boundary where the constraints are active. Technically, the control design problems can be formulated as optimization problems. With the optimization setup, it is possible to overcome these limitations and to evolve next-generation ARV technologies. The proposed research program aims to integrate advanced optimization technology in the ARV control and autonomy layer design and address the most challenging issues including: (1) Reliability: Since the optimization solver may fail to give an optimal solution, how do we guarantee the developed optimization-based control system will not be affected by such failures and will achieve the designated control goal? (2) Applicability: Since there exist uncertainties/disturbances in the control process, how do we quantify the relationship between expected performance and the level of uncertainty, based on which we judge whether the control design will meet performance requirement in presence of specified uncertainty? (3) Scalability: The developed framework should be able to guide the control and autonomy layer design not only for a single ARV but also for a team of them, so how can we justify the scalability? (4) Real-Time Control: The optimizations are solved by iterative methods which may take considerable time to converge to a solution. Since robotic systems are fast dynamic systems, a solution needs to be obtained within less than tenth of a second. So how do we design the processing pipeline and/or ad hoc fast optimization algorithms to meet the real-time control requirement? The proposed program will answer the above important questions and develop a novel analysis, synthesis and design toolkit for the ARV control system design; it will improve the reliability and operability of autonomous robotic systems and lower the risks and costs during their operations. Furthermore, this program will benefit the Canadian society by enabling innovative and intelligent robotic applications and provide tremendous opportunities for training HQP for the fast-growing robotics industry in Canada.