Bio-inspired Sensory Systems in Automata for Hazardous Environments


  • Lucio Canete Universidad de Santiago de Chile


attention level, model, performance, emulation, automata.


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


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