Design and optimisation of bio-inspired robotic stochastic search strategy Online publication date: Fri, 18-Sep-2020
by Farhad Maroofkhani; Amir Ali Forough Nassiraei; Kazuo Ishii
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 12, No. 3, 2020
Abstract: An autonomous robot's search strategy is the set of rules that it employs while looking for targets in its environment. Biological systems (e.g., foraging animals) provide useful inspirations for designing optimal stochastic search algorithms for autonomous robots. Due to the complexity of interaction between the robot and its environment, optimisation must be performed in high-dimensional parameter space. We analyse the dependence of search efficiency on environmental parameters and robot characteristics using response surface methodology (RSM), a technique originally developed for experimental design. In this study, the efficiency of a strategy focuses on Lévy walk search strategies on two-dimensional landscapes with clumped resource distributions. We show how RSM techniques can be used to identify optimal parameter values and to describe how sensitive is the efficiency to the changes in these values.
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