Robot Space Exploration Using Peano Paths Generated by Self-Organizing Maps

Abstract

Autonomous exploration by a team of robots has many important applications in rescue operations, clearing of mine fields and other military applications, and even space exploration. With limited range of sensors robots have to divide exploration tasks among themselves working under multiple constraints. An optimal covering of two-dimensional area by robot trajectories requires formation of space-filling Peano curves. This may be achieved using Self-Organizing Feature Map (SOFM) algorithm. There are two steps involved in the proposed approach: first optimal trajectories are defined generating Peano curves for space of arbitrary shape using the SOFM algorithm, and second, robots are deployed for exploration based on selection of start/end nodes and radius of robot sensors. The same approach may be used to direct people or teams exploring some area in rescue operations. Tests simulations show that this approach achieves better coverage and faster exploration than competing algorithms.