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Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species

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Document pages: 31 pages

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 hunts 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 is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easier to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the travelling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and the 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 smoothly 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. In the second place, performance in a global task (foraging) depends strongly on local parameters like visual direction-tuning, position of the eyes and paddles, etc. Behaviour and habitat mould 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.

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