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Robotic exploration is valuable in many practical applications. Even something as simple for a human as lawn-mowing is a nontrivial challenge for robotic controllers. It is possible to build a safe obstacle-edging reflex based on range sensor filtering. However, in some situations, a reflex is insufficient. Animals such as cockroaches depend on exploration, and the complexity of their strategies may inspire robotic approaches. After many hours of cockroach trials with and without goals (darkened shelters) and further experiments in analysis, we extracted a state-based algorithm that makes these types of decisions stochastically and also captures the shelter seeking bias in the path length of cockroaches. We call this algorithm RAMBLER, Randomized Algorithm Mimicking Biased Lone Exploration in Roaches. Further we find that this algorithm can be extended to predict behavior of cockroaches in arenas with clear barriers between the entrance and the goal. For robotics, this algorithm could add some variability to robot paths in situations where heuristics fail. For biology, this algorithm is a model that may help us better understand the decision-making process in the cockroach brain. |
