Adaptive robot coordination using interference metrics

One key issue facing robotic teams is effective coordination mechanisms. Many robotic groups operate within domains where restrictions such as limiting areas of operation are liable to cause spatial conflicts between robots. Our previous work proposed a measure of coordination, interference, that measured the total time robots dealt with resolving such conflicts. We found that a robotic group's productivity was negatively correlated with interference: Effective coordination techniques minimized interference and thus achieved higher productivity. This paper uses this result to create adaptive coordination techniques that are able to dynamically adjust the efforts spent on coordination to match the number of perceived coordination conflicts in a group. Our robots independently calculate a projected level of interference they will encounter. By using this metric as a guide, we are able to create adaptive coordination methods that can quickly and effectively adjust to changing conditions within their environment. We present an adaptation heuristic that is completely distributed and requires no communication between robots. Using thousands of simulated trials, we found that groups using this approach achieved a statistically significant improvement in productivity over non-Adaptive coordination methods.