Publications Details
Collective search by mobile robots using alpha-beta coordination
One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. Mapping mine fields, extraterrestrial and undersea exploration, the location of chemical and biological weapons, and the location of explosive devices are just a few potential applications. Teams of robotic bloodhounds have a simple common goal; to converge on the location of the source phenomenon, confirm its intensity, and to remain aggregated around it until directed to take some other action. In cases where human intervention through teleoperation is not possible, the robot team must be deployed in a territory without supervision, requiring an autonomous decentralized coordination strategy. This paper presents the alpha beta coordination strategy, a family of collective search algorithms that are based on dynamic partitioning of the robotic team into two complementary social roles according to a sensor based status measure. Robots in the alpha role are risk takers, motivated to improve their status by exploring new regions of the search space. Robots in the beta role are motivated to improve but are conservative, and tend to remain aggregated and stationary until the alpha robots have identified better regions of the search space. Roles are determined dynamically by each member of the team based on the status of the individual robot relative to the current state of the collective. Partitioning the robot team into alpha and beta roles results in a balance between exploration and exploitation, and can yield collective energy savings and improved resistance to sensor noise and defectors. Alpha robots waste energy exploring new territory, and are more sensitive to the effects of ambient noise and to defectors reporting inflated status. Beta robots conserve energy by moving in a direct path to regions of confirmed high status.