Mechanical and Civil Engineering Seminar

As robotic and autonomous systems become more ubiquitous and their applications more expansive, the problems we look to solve are often best characterized by desirable statistics or distributions. Automating search and exploration for mobile robots, for example, involves being able to make decisions based on distributed, probabilistic, and potentially sporadic information. I will discuss my work in developing optimal control techniques that allow such problems to be formulated directly in terms of spatial statistics using principles from ergodic theory (a trajectory's distance from ergodicity, or its statistical distance from a distribution, can be used to define a metric suitable for optimal control). I will present experimental results using ergodic optimal control to automate active sensing tasks using a bio-inspired underwater robot, as well as future avenues of work with applications to precision agriculture, assisted surgery and rehabilitation.