Terrain Perception in a Shape Shifting Rolling-Crawling Robot

Abstract

Terrain perception greatly enhances the performance of robots, providing them with essential information on the nature of terrain being traversed. Several living beings in nature offer interesting inspirations which adopt different gait patterns according to nature of terrain. In this paper, we present a novel terrain perception system for our bioinspired robot, Scorpio, to classify the terrain based on visual features and autonomously choose appropriate locomotion mode. Our Scorpio robot is capable of crawling and rolling locomotion modes, mimicking Cebrenus Rechenburgi, a member of the huntsman spider family. Our terrain perception system uses Speeded Up Robust Feature (SURF) description method along with color information. Feature extraction is followed by Bag of Word method (BoW) and Support Vector Machine (SVM) for terrain classification. Experiments were conducted with our Scorpio robot to establish the efficacy and validity of the proposed approach. In our experiments, we achieved a recognition accuracy of over 90% across four terrain types namely grass, gravel, wooden deck, and concrete.