A recent paper by members of the DCIST alliance develops efficient algorithms for information acquisition problems for teams of mobile robots who need to exhibit resiliency in situational awareness. The work develops a computationally efficient algorithm with linear complexity in the number of robots that achieves a provable approximation performance for information gathering tasks such as active mapping and tracking moving targets in attack prone environments. A key mathematical property used in the algorithm analysis, and common to many information measures is a property called submodularity, which is a diminishing returns property on information gain. Experiments indicate that the algorithm leads to superior target tracking performance under a variety of different robot team sizes, number of targets, and attacks.
Photo: A resilient active target tracking problem with a 3 robot team. The compromised robot is highlighted in red, the remaining robots in blue, and the target uncertainty is highlighted in cyan