A Sensory-Motor Control Model of Animal Flight Explains Why Bats Fly Differently in Light Versus Dark

Abstract

Animal flight requires fine motor control. However, it is unknown how flying animals rapidly transform noisy sensory information into adequate motor commands. Here we developed a sensorimotor control model that explains vertebrate flight guidance with high fidelity. This simple model accurately reconstructed complex trajectories of bats flying in the dark. The model implies that in order to apply appropriate motor commands, bats have to estimate not only the angle-to-target, as was previously assumed, but also the angular velocity (“proportional-derivative” controller). Next, we conducted experiments in which bats flew in light conditions. When using vision, bats altered their movements, reducing the flight curvature. This change was explained by the model via reduction in sensory noise under vision versus pure echolocation. These results imply a surprising link between sensory noise and movement dynamics. We propose that this sensory-motor link is fundamental to motion control in rapidly moving animals under different sensory conditions, on land, sea, or air.

Sensorimotor Model of Obstacle Avoidance in Echolocating Bats

“…al. [ 95 ] and Bar et al [ 96 ]. In the case of obstacle avoidance, we consider the proposed obstacle avoidance behaviour to be a robust sensorimotor loop to which both FM and CF bats can fall back on in case less reliable cues are unavailable.…”

Diversity and pattern of nest preference of bat species at bat-dwelling caves in Gombong Karst, Central Java, Indonesia

“…Instead, bats with the inability to pass through a long cave passage selecting a nest in a location close to the entrance cave for easy maneuvers. According to Bar et al (2015), the flying maneuver of bats in the dark room had a correlation with sensory control. Bats should improve sound sensor by integrating sensory information for flight guidance.…”

A fully autonomous terrestrial bat-like acoustic robot

“…In some respects our approach was probably much more simplistic than a bat. For example, the obstacle avoidance algorithm was very simple and a better approach would probably use control-theory to steer the Robat around obstacles [ 37 ]. In terms of mission priority, we used serial processing where the Robat first processes new incoming sensory information; it then performs the urgent low-level task of obstacle avoidance and path planning, and only every several acquisitions, it performs the high-level process of map integration.…”