Two Adaptive Communication Methods for Multi-Robot Collision Avoidance

Designers of robotic groups are faced with the formidable task of creating effective coordination architectures that can deal with collisions due to changing environment conditions and hardware failures. Communication between robots is a mechanism that can at times be helpful in such systems, but can also create a time, energy, or computation overhead that reduces performance. In dealing with this issue, different communication schemes have been proposed ranging from those without any explicit communication, localized algorithms, and centralized or global communicative methods. Finding the optimal communication act is typically an intractable problem in real-world problems. As a result, we argue that at times group designers should use computationally bounded team communication approaches. We propose two such approaches: an algorithm selection approach to communication whereby robots choose between a known group of communication schemes and a parameterized communication framework whereby robots can reason about how large a communication radius is needed for a given problem. Both solutions use a novel coordination cost measure, combined coordination costs, to find the appropriate level of communication within such groups. Robots can then use this measure to create adaptive communication approaches that select between communication approaches as needed during task execution. We validated this approach through conducting extensive experiments in a canonical robotic foraging domain and found that robotic groups using these adaptive methods were able to significantly increase their productivity compared to teams that used only one type of communication scheme.