Seminar: Long-Term Autonomy for Solar-Powered Robotic Systems

Many robotic systems will benefit from prolonged operational time when performing long-duration missions, such as environmental monitoring, search and rescue, and communications. A solar-powered robot that harvests energy from the environment and charges storage batteries as backup is intended to extend the endurance time or realize persistent operation in ideal scenarios. Due to the complex system dynamics and mission constraints, it is challenging to realize full autonomy of solar-powered robotic systems in long-term operations, especially under dynamic environments where real-time motion planning is required.

This talk will present our work on developing sophisticated modeling and motion planning strategies for high-level autonomy of solar-powered robotic systems and establishing experimental facilities for verification of proposed strategies. Two types of representative problems will be discussed. One is integrated energy harvesting and power management of solar-powered ground vehicles in time-optimal travel missions. The other is design, control, and motion planning of solar-powered fixed-wing and quadrotor aircraft. Computationally efficient models for these problems are generated with significantly reduced design variables, compact mathematical representations, simplified dynamics, and/or relaxed constraints. The optimization-based motion planning schemes, including cascaded heuristic search and iterative semidefinite programming algorithms, aim to overcome the computational bottleneck using the new modeling paradigms. The overall objective is to realize high-level autonomy of solar-powered ground and aerial vehicles in energy harvesting and utilization under operational and mission constraints. Some results obtained in virtual simulation are verified in real-world experimental platforms, including customized ground and aerial vehicles, leading to synthesized theoretical and experimental framework for analyzing efficiency of solar-powered robotic systems.

About the Speaker

Ran Dai is an Assistant Professor and Black & Veatch faculty fellow in the Aerospace Engineering Department at Iowa State University. She received her B.S. degree from Beihang University and her M.S. and Ph.D. degrees in Aerospace Engineering from Auburn University. After graduation, she worked as an engineer in an automotive technology company, Dynamic Research, Inc., and conducted research and consulting in the areas of semi-autonomous vehicle guidance and control. From 2010 to 2012, Dr. Dai joined the Robotics, Aerospace, and Information Networks Lab at University of Washington as a postdoctoral fellow, where she involved in an energy management project with application to the next generation of Boeing 787 aircraft power systems. Dr. Dai’s research focuses on motion planning of autonomous systems, computational optimization, and networked control systems. She is a recipient of the National Science Foundation CAREER Award and a Senior Member of AIAA.

Hosted by Professor Jim Gregory.