Sensing and Estimation in Networks of Sensors and Robots

March 31, 2003 - 10:15am to 11:15am
Location: 
EE/CS 3-125
Host: 
N/A
Recent trends in sensor technologies have expanded our sensing
capabilities in terms of scale, quality and content of information. The
availability of large and diverse amounts of sensory data has introduced
a set of unprecedented challenges when attempting to infer causal
relationships or model the behavior of complex systems. Autonomous
robots represent an exemplary case of such systems.

Numerous robotic applications require that robots work in close
collaboration. In these cases each robot needs to know its position both
in absolute coordinates and with respect to its teammates. Communication
and sensor sharing among groups of robots can significantly increase the
accuracy and robustness of the position estimation (localization) task.
To this end we have developed a distributed Kalman filter estimator that
can optimally combine information from all the robots in the group with
minimal communication requirements. Furthermore, we have derived
analytical expressions for the maximum expected uncertainty as a
function of the number of robots in the team and the various sources of
noise and uncertainty in the acquired measurements.

During my presentation I will also discuss two exemplary cases of sensor
based probabilistic algorithms for autonomous vehicle navigation: (i)
autonomous stair climbing, and (ii) safe and precise planetary landing.