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.
Highlights
Living beings in nature possess innate abilities to adapt their gaits in response to the nature of terrain being traversed which could vary from smooth, flat ground to bumpy, slippery regions posing serious hazard
We present a novel terrain perception system for our bioinspired robot, Scorpio, to classify the terrain based on visual features and autonomously switch between rolling or crawling morphology locomotion modes in relation to perceived terrain
We presented a new approach for vision based terrain classification, which is based on support vector machines, for a rolling-crawling self-reconfigurable robot using a low resolution camera
Summary
Living beings in nature possess innate abilities to adapt their gaits in response to the nature of terrain being traversed which could vary from smooth, flat ground to bumpy, slippery regions posing serious hazard. In [2], Graeme Best proposed a terrain perception method for legged robots by attaching force sensors on the legs This approach helped to analyze how the robot interacted with the terrain. In another study [5], a terrain perception system using a laser stripe-based structured light sensor for autonomous ground vehicles was discussed This system was reported to have terrain classification accuracy over 90%. We propose a novel biomimetic robot, Scorpio, which is capable of switching between rolling and crawling locomotion morphology for traversing different terrains by visually recognizing the type of terrain. We present a novel terrain perception system for our bioinspired robot, Scorpio, to classify the terrain based on visual features and autonomously switch between rolling or crawling morphology locomotion modes in relation to perceived terrain. Surface texture identification using local feature extraction is preferred in our case because local features are invariant to rotation, shadows, viewpoints, and brightness among others
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