Recent research has focused on understanding how "perception" should be formally incorporated into feedback control in such a way that control signals are determined by a single unified awareness derived from a combination of sensory processes together with past experience. This talk will be focused on two aspects of perception-enabled control. First, I will discuss the analysis of large data sets of bat trajectories and our work to interpret and design feedback control laws that synthetically replicate the motions of Myotis velifer bats around trees and other natural obstacles in rural Texas. Bats are not blind, and in fact, vision may be an essential tool for their navigation. Other agile flyers almost certainly are guided by optical flow, and both laboratory experiments and field observations indicate that they are attracted to locations where the the visual environment is rich with features. The talk will discuss optical feature salience and propose control paradigms for using fleeting glimpses of salient features to guide high-speed motions. This is joint work with Zhaodan Kong..

 

John Baillieul's research deals with robotics, the control of mechanical systems, and mathematical system theory. His PhD dissertation, completed at Harvard University under the direction of R.W. Brockett in 1975, was an early work dealing with connections between optimal control theory and what came to be called “sub-Riemannian geometry.”

 

After publishing a number of papers developing geometric methods for nonlinear optimal control problems, he turned his attention to problems in the control of nonlinear systems modeled by homogeneous polynomial differential equations. Such systems describe, for example, the controlled dynamics of a rigid body. His main controllability theorem applied the concept of finiteness embodied in the Hilbert basis theorem to develop a controllability condition that could be verified by checking the rank of an explicit finite dimensional operator.

 

Baillieul’s current research is aimed at understanding decision making and novel ways to communicate in mixed teams of humans and intelligent automata.

 

John Baillieul is a Fellow of IFAC, a Fellow of the IEEE and a Fellow of SIAM.