Integration of Learning Classifier Systems with simultaneous localisation and mapping for autonomous robotics

Abstract

A cognitive mobile robot must be able to autonomously solve the three complex problems of navigating: where it is, where it is going and how it is going to get there. The first is addressed by techniques for simultaneous localization and mapping (SLAM). The next stage of navigating is to plan a path to a goal, which is often achieved by learning techniques due to the scale of search required. Commonly, the localisation and mapping stage is separated from path planning stage, with the function not of interest being considered ideal in order to simplify the problem (similarly, the goal is often predetermined by an external agent, such as a human operator specifying a location to reach). This work integrates the planning with the localisation and mapping in order to investigate the benefits of considering these aspects together (rather than as a separate functions as is often assumed). Firstly, experiments on real-robots show decreased localisation error in this approach (1.8 mm ±0.41 mm to 1.2 mm ±0.26 mm). Secondly, the number of steps to goal has concurrently been reduced (13.4 steps to 11.8 steps). This work is novel in the integration of evolutionary computation planning techniques with SLAM. It also has enabled the opportunity for rule-sharing between heterogeneous robots and the inclusion of action policies in SLAM filter updates.