The benefits of insect-swarm hunting in echolocating bats, and its influence on the evolution of bat echolocation signals

Predation on swarms of prey, especially using visual information, has drawn much interest in studies of collective movement. Surprisingly, in the field of biosonar this aspect of prey detection, which is probably very common, has received little to no attention. Here, we combine computer simulations and actual echo measurements to accurately estimate the echo intensity of insect swarms of different size and density. We show that swarm echo intensity increases with 3dB for every doubling of insect number, irrespective of swarm density. Thus swarms will be much easier to detect than single insects. Many of the insects bats eat are so small that they are only detectable by echolocation at very short distances. By focusing on detection of swarms of insects, a bat may increase its operating range and diversify its diet. Interestingly, interference between the sound waves reflected from a swarm of insects can sometimes result in echoes that are much much weaker than echoes from single insects. We show that bats can reduce this problem by increasing the bandwidth of their echolocation calls. Specifically, a bandwidth of 3-8 kHz would guarantee receiving loud echoes from any angle relative to the swarm. Indeed, many bat species, and specifically bats hunting in open spaces, where swarms are abundant, use echolocation signals with a bandwidth of several kHz. Our results might also explain how the first echolocating bats that probably had limited echolocation abilities, could detect insects through swarm hunting.

Control of Stochastic Boundary Coverage by Multirobot Systems

This technical brief summarizes and extends our recently introduced control framework for stochastically allocating a swarm of robots among boundaries of circular regions. As in the previous work, a macroscopic model of the swarm population dynamics is used to synthesize robot control policies that establish and maintain stable predictable team sizes around region boundaries. However, this extension shows that the control strategy can be implemented with no robot-to-robot communication. Moreover, target team sizes can vary across different types of regions, where a region's type is a subjective characteristic that only needs to be detectable by each individual robot. Thus, regions of one type may have a higher equilibrium team size than regions of another type. In other work that predicts and controls stochastic swarm behaviors using macroscopic models, the equilibrium allocations of the swarm are sensitive to changes in the mean robot encounter rates with objects in the environment. Thus, in those works, as the swarm density or number of objects changes, the control policies on each robot must be retuned to achieve the desired allocations. However, our approach is insensitive to changes in encounter rate and therefore requires no retuning as the environment changes. In this extension, we validate these claims and show how the convergence rate to the target equilibrium allocations can be controlled in swarms with a sufficiently large free-robot population. Furthermore, we demonstrate how our framework can be used to experimentally measure the rates of robot encounters with occupied and unoccupied sections of region boundaries. Thus, our method can be viewed both as an encounter-rate-independent allocation strategy as well as a tool for accurately measuring encounter rates when using other swarm control strategies that depend on them.