Robots are having significant impact in society, business, and industry. They are solving increasingly difficult tasks, moving out of highly structured environments (e.g., factories) into the real world. Robots must move reliably and safely in order to solve real world tasks. Efficient algorithms are needed to compute safe, reliable motion plans in complex environments.
My research interests are centered on integrating control with computational and algorithmic approaches to motion and task planning. Control theory is essential for addressing the dynamics of autonomous systems; computer algorithms excel at handling geometric problem constraints and searching in high-dimensional continuous spaces. Bringing the two together involves using control knowledge to enhance the efficiency of planning algorithms, and utilizing algorithmic approaches to compute feedback laws with guaranteed convergence and obstacle avoidance properties.
Several past and present research projects are listed below.
Resolution complete planning under differential constraints
Fundamental sampling considerations for motion planning