Ergodic Exploration of Distributed Information
This work addresses the challenge of efficient target search for autonomous underwater robots, representing an incremental improvement in active search methods.
The paper tackles the problem of autonomous mobile robots searching for static targets in underwater environments using nonlinear measurements and dynamics, and demonstrates that their ergodic exploration algorithm outperforms commonly used information-oriented controllers, especially in the presence of distractions.
This paper presents an active search trajectory synthesis technique for autonomous mobile robots with nonlinear measurements and dynamics. The presented approach uses the ergodicity of a planned trajectory with respect to an expected information density map to close the loop during search. The ergodic control algorithm does not rely on discretization of the search or action spaces, and is well posed for coverage with respect to the expected information density whether the information is diffuse or localized, thus trading off between exploration and exploitation in a single objective function. As a demonstration, we use a robotic electrolocation platform to estimate location and size parameters describing static targets in an underwater environment. Our results demonstrate that the ergodic exploration of distributed information (EEDI) algorithm outperforms commonly used information-oriented controllers, particularly when distractions are present.