Bio-inspired Sensory Systems in Automata for Hazardous Environments
Keywords:attention level, model, performance, emulation, automata.
Every automaton in dynamic and complex environments requires sensoryÂ systems with an appropriate level of attention on the hazardous environment. ThisÂ property in any efficient automaton is analogous to that observed in animal sensoryÂ systems. In this context, it is noted that to ensure its viability, the sensory systems ofÂ animals must maintain a continuous state of alertness or attention to the environment.Â However, the state consumes energy so it is impossible to keep a constant level overÂ time. In this regard, biologists have designed models for explaining the variation inÂ the level of surveillance in two vital activities of animals: Work and Rest.Â In an alternating pattern between Work and Rest, the Attention Level V(t) declinesÂ and increases as the animal works and rests respectively along the time. For each ofÂ the two states, there is one relation: dV/dt = âˆ’Î± * V while working and dV/dt =Â Î²*(1-V) while resting. In this model Î± is the loss rate of surveillance that dependsÂ on the difficulty of the work and Î² is the recovery rate which depends on the qualityÂ of rest. In the case of automata, this phenomenon is analogous to that observedÂ in the Animal Kingdom. Even if the automatic machines have relief structures toÂ monitor their environments, they always require that its sensory system recoversÂ the alertness after being hit by the inexorable entropy. If the task is hard (Î± isÂ large), the Attention Level decreases rapidly. Once the level has dropped below aÂ threshold of tolerance, it must be recovered. If rest is poor, the automaton will takeÂ a lot of time to achieve the desired level. Obviously, machines do not rest, but inÂ analogous terms, this phenomenon is emulated in the way of maintenance activities.Â Parameter Î² represents the quality of these maintenances. This model has been testedÂ with computer simulations to study the performance of automatic machines in hostileÂ environments.Â After tests, it was possible to quantify Î± and Î² for each kind of task-environment andÂ each kind of maintenance. The bio-inspired model showed to have explicative andÂ predictive applications to the conquest of hostile scenarios by means of automata.Â Indeed it is an interesting conceptual tool for increasing the performance of machines.
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