Tuning fuzzy-based hybrid navigation systems using calibration maps

We present a novel approach for the tuning and assessment of a cascade of fuzzy logic systems, working cohesively for robot soccer navigation. We generate calibration maps to comprehensively examine the performance of the cascades, allowing for both the visualisation and quantification of the overall system performance. The experiments demonstrate how the proposed method captures the aggregate effect on system's efficiency of even the slightest changes to the fuzzy rules. It also provides feedback on the mechanics of the fuzzy systems that could be held responsible for any shortcomings. Interestingly, without the aid of the proposed techniques, these minute changes are very difficult, if not impossible to identify through human visual inspection per se. Although the example provided in the paper reflects navigation in the Mirosot league robot soccer scope, the proposed calibration method lends itself amenable to other problem domains where target pursuit and obstacle avoidance behaviours are a necessity. It is also worth-noting that the calibration method can be utilised as a fitness function to a Genetic Algorithm or other optimisation techniques, for a fully-automated calibration. Lastly, we discuss how the calibrated cascade of fuzzy systems neatly integrate with the A*algorithm to produce a hybrid system for near-optimal navigation.