Institute for Systems Research

Odometry information alone isnot sufficient for minimizing path length if the terrain is uneven orif it includes obstacles. We generalize existing results on a simplecontrol law, also known as "local pursuit," which is appropriate inthe context of formations and which requires limited interactionbetween vehicles.

Our algorithm is iterative and converges to alocally optimal path. We include simulations and experimentsillustrating the performance of the proposed strategy.

An important feature of the facility we describe is that it uses severaltypes of sensing modalities including position sensing, tactile sensingand more conventional vision sensing. It can interact with objectsof different complexity and is subject to communication constraints arising in a completelynatural and generic way. In constructing it we have used off-the-shelfcomponents wherever possible and made choices with an eye towardflexibility and reliability.

To aid in the calibration process, computer vision techniques such asgradient calculation and the Hough Transform were used to extract the calibration points from a series of images. These points, alongwith their corresponding world coordinates, were then used in Roger Tsai'scamera model to calibrate the camera.

The intrinsic and extrinsic camera parameters were then used to find the vector of the sound source in an image. Again, vision processing was used to extract the sound source from an image using red as a detectable feature. The largest red region was iolated, and the centroid of that region was used to mark the location of the sound source.

Finally, Tsai's model was used in reverse to find the vector in the world along which the camera lies.

In this work we report on experimentalresults obtained with a deformable tactile sensor whose properties arewell-suited to manipulation. The results presented here show that thesensor described provides a rich set of tactile data.