Multi-robot information gathering teams typically require communication for data fusion and cooperative decision making. However, when communication takes place over wireless networks, stringent bandwidth limits apply. These limits raise the need for efficient utilisation of available communication resources in a manner that balances information gathering utility with communication costs or limits. In our previous work, we introduced the dynamic information flow (DIF) problem as a general formulation of this trade-off. We introduced two variants of the problem addressing the issue of communication efficiency for data fusion only. In this paper, we extend one of the variants to address communication efficiency for both data fusion and cooperative decision making in a synergistic manner. We present a solution to this new variant that integrates a multi-cast routing algorithm with information structure optimisation. This solution allows teams that involve high-data-rate sensors and tight coordination to respect bandwidth limits. Through several simulations we verify that our solution significantly reduces bandwidth usage of such teams while maintaining information gathering performance.

Advantages of decentralised decision making systems for multi-agent robotic tasks are limited by the heavy demand they impose on communication. This paper presents an approach to control communication for the LQ team problem, namely a team of agents with linear dynamics and quadratic team cost. Communication costs are added to the objective of the LQ optimal control linear matrix inequality formulation, allowing for a well-defined balancing of communication costs and team performance. Results show a reduction in communication consistent with the specified cost and in a manner that upholds team performance relative to the reduced communication footprint. The applicability of the approach has also been extended to information gathering tasks through local LQ approximations along the agents' paths. Simulation testing on a sample two-agent problem shows a 40% reduction in communication with negligible impact on performance.

This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.