Sensing and Control for Robotic Fish Locomotion
Richard Mason, Kristi Morgansen, Joel Burdick

We are studying issues in fluid mechanics, nonlinear control, and sensing that are necessary for the development of self-propelled robot fish. (full report)

Distributed Manipulation
Todd Murphey, Joel W. Burdick

This research analyzes the stability of distributed manipulation control schemes. A commonly proposed method for designing a distributed actuator array control scheme assumes that the system's control action can be approximated by a continuous vector force field. The continuous control vector field idealization must then be adapted to the physical actuator array. However, we have shown that when one takes into account the discreteness of actuator arrays and realistic models of the actuator/object contact mechanics, the controls designed by the continuous approximation approach can be unstable. For this analysis we introduce and use a ``power dissipation'' method that captures the contact mechanics in a general but tractable way. We show that the quasi-static contact equations have the form of a multi-model hybrid system. We introduce a discontinuous feedback law can produce stability which is robust with respect to variations in contact state. (full report)

The Neuroethology of Sensory-Based Behavior
Dr. Malcolm MacIver, Prof. Joel Burdick

We have begun research that addresses the interrelationship between animal sensing and the mechanics of animal movement. There are two interrelated thrusts to this work. The first is optimal sensing and movement strategies for far-field targets, such as distant resources that must be detected and acquired. The second thrust is optimal sensing and movement strategies for near-field locomotion-directed signals, such as needed for sensing flow velocity near a constriction in a streambed that requires a fish to increase its thrust.
(full report)

Weighted Feature matching Algorithms for Mobile Robot Displacement Estimation
Dr. Joel W. Burdick, Dr. Stergios Roumeliotis, Kristo Kriechbaum, Sam Pfister

Sensor based motion planning is an integral part of mobile robotics. It incorporates sensor information, reflecting the current state of the environment, into a robot's planning process, as opposed to classical planning , where full knowledge of the world's geometry is assumed to be known prior to the planning event. Sensor based planning is important because: (1) the robot often has no a priori knowledge of the world; (2) the robot may have only a coarse knowledge of the world because of limited memory; (3) the world model is bound to contain inaccuracies which can be overcome with sensor based planning strategies; and (4) the world is subject to unexpected occurrences or rapidly changing situations.
(full report)