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The accurate recognition of unknown terrain by robots plays a vital role in completing walking tasks. Haptic-based terrain recognition methods have their own advantages over visual, audio, and vibration-based terrain recognition methods. With the increasing complexity of robot application scenarios, there are still many technical difficulties in using tactile systems to recognize terrain. For real-time terrain detection technology, how to improve the accuracy of terrain recognition and shorten recognition time based on tactile systems is an urgent problem to be solved. In this paper, the digital elevation model (DEM) geomorphic factor terrain recognition algorithm using probabilistic neural networks (DGFTRA-PNN) based on tactile force-feedback system is proposed to solve this problem. First, the tactile force-feedback system is mounted on the foot ends of the hexapod robot to collect terrain data. Second, the feasibility of using DEM geomorphic factors as terrain features to identify terrain was demonstrated. Third, DGFTRA-PNNs are established based on the different walking gait of the hexapod robot, achieving terrain recognition based on DEM feature values. Finally, the accuracy and speed of DGFTRA-PNN were verified through field experiments. Compared with the terrain recognition algorithm based on back propagation neural network (BP) and extreme learning machine (ELM) algorithm, the DGFTRA-PNN has advantages in terms of terrain recognition accuracy and recognition time: the average recognition accuracy for various types of terrain is 96%, and the calculation time is 0.04 seconds.
Xing et al. (Fri,) studied this question.