Bat swarming as an inspiration for multi-agent systems: predation success, active sensing, and collision avoidance

Many species of bats primarily use echolocation, a type of active sensing wherein bats emit ultrasonic pulses and listen to echoes, for guidance and navigation. Swarms of such bats are a unique type of multi-agent systems that feature bats's echolocation and flight behaviors. In the work of this dissertation, we used bat swarming as an inspiration for multi-agent systems to study various topics which include predation success, active sensing, and collision avoidance. To investigate the predation success, we modeled a group of bats hunting a number of collectively behaving prey. The modeling results demonstrated the benefit of localized grouping of prey in avoiding predation by bats. In the topics regarding active sensing and collision avoidance, we studied individual behavior in swarms as bats could potentially benefit from information sharing while suffering from frequency jamming, i.e., bats having difficulty in distinguishing between self and peers's information. We conducted field experiments in a cave and found that individual bat increased biosonar output as swarm size increased. The experimental finding indicated that individual bat acquired more sensory information in larger swarms even though there could be frequency jamming risk. In a simulation wherein we modeled bats flying through a tunnel, we showed the increasing collision risk in larger swarms for bats either sharing information or flying independently. Thus, we hypothesized that individual bat increased pulse emissions for more sensory information for collision avoidance while possibly taking advantage of information sharing and coping with frequency jamming during swarming.