Our work considers the problem of localizing the source of a physical signal of interest, such as magnetic force, heat, radio or chemical concentration, using a robot team. We proposed distributed control strategies for source seeking specific to two scenarios: one in which the robots have a noisy model of the signal formation process and one in which a signal model is not available. In the model-free scenario, the robot team follows a stochastic gradient of the signal field. Our approach is robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the robots to a neighborhood of a local maximum of the field. The performance of the model-free algorithm is demonstrated in the video using a single robot to localize the source of a wireless radio signal. In the model-based scenario, the robots follow a stochastic gradient of the mutual information between predicted signal measurements and the predicted source location. In contrast with existing work which insists on improving the quality of the gradient estimate as much as possible, we show that good performance can be achieved by using only a few predicted signal measurements.
@article{Atanasov_StochasticSourceSeeking_JDSMC14,
author = {N. Atanasov and J. Le Ny and G. Pappas},
title = {Distributed Algorithms for Stochastic Source Seeking with Mobile Robot Networks},
journal = {ASME Journal of Dynamic Systems, Measurement, and Control (JDSMC)},
year = {2015},
volume = {137},
number = {3},
pages = {031011-031011-9},
doi = {http://www.doi.org/10.1115/1.4027892}
}
@inproceedings{Atanasov_StochasticSourceSeeking_ICRA12,
author = {N. Atanasov and J. Le Ny and N. Michael and G. Pappas},
title = {Stochastic Source Seeking in Complex Environments},
booktitle = {IEEE Int. Conf. on Robotics and Automation (ICRA)},
year = {2012},
pages = {3013-3018},
doi = {http://www.doi.org/10.1109/ICRA.2012.6225289}
}