Phototaxic Foraging of the Archaepaddler, a Hypothetical Deep-Sea Species

Abstract

An autonomous agent (animat, hypothetical animal), called the (archae) paddler, is simulated in sufficient detail to regard its simulated aquatic locomotion (paddling) as physically possible. The paddler is supposed to be a model of an animal that might exist, although it is perfectly possible to view it as a model of a robot that might be built. The agent is assumed to navigate in a simulated deep-sea environment, where it forages for autoluminescent prey. It uses a biologically inspired phototaxic foraging strategy, while paddling in a layer just above the bottom. The advantage of this living space is that the navigation problem—and hence our model—is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easy to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the traveling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and body geometry (e.g., placement, direction and directional selectivity of the eyes) on foraging success. Adequate performance proves to require a specific body geometry adapted to the habitat characteristics. In general, performance degrades gracefully for modest changes of the geometric and habitat parameters, indicating that we work in a stable region of “design space.” The parameters have to strike a compromise between, on the one hand, the ability to “fixate” an attractive target, and on the other hand, to “see” as many targets at the same time as possible. One important conclusion is that simple reflex-based navigation can be surprisingly efficient. Additionally, performance in a global task (foraging) depends strongly on local parameters such as visual direction tuning, position of the eyes and paddles, and so forth. Behavior and habitat “mold” the body, and the body geometry strongly influences performance. The resulting platform enables further testing of foraging strategies or vision and locomotion theories stemming either from biology or from robotics.