Abstract
Terrain recognition technology plays a key role in enhancing autonomous mobility for Quadruped robot in off-road environments. However, feature extraction and classification algorithm are the key to accuracy and efficiency of the terrain recognition. Regarding the characteristics of different terrain surface properties and structures, it gets low-dimensional and high dimensional characteristics by texture features and wavelet transform and uses them as training features of classifier. Then its efficiency is not high and convergence speed is slow for traditional learning algorithm, which is difficult to meet the requirements. So the extreme learning machine is used to classify the terrain pictures collected by robot in real time. Experimental results show that the accuracy of extreme learning machine terrain classification is higher than the traditional neural network algorithm and the support vector machine, and algorithm efficiency is raised more than several times for the sample size of 6000, which meet...
Highlights
Terrain recognition ability in complex unstructured environment is a key factor to improve the motion efficiency of quadruped robots
Terrain recognition technology can be used to select different access areas according to the different terrains, select different gaits according to different terrain environments,[1] and implement different motion control strategies
In order to improve the terrain recognition ability of quadruped robot, it is necessary to select the appropriate methods of terrain feature extraction
Summary
Terrain recognition ability in complex unstructured environment is a key factor to improve the motion efficiency of quadruped robots. Terrain recognition technology can be used to select different access areas according to the different terrains (such as grassland, highway, stone road), select different gaits according to different terrain environments,[1] and implement different motion control strategies In this process, terrain feature extraction method and classification algorithm are important. Compared with the general BP, RBF neural network, and SVM, the parameters selection is easy and the learning speed is fast, and generalization performance is good.[10,13] To sum up, in order to improve the terrain recognition ability of quadruped robot, texture feature and wavelet feature are used as the classification feature vector, and a new neural network algorithm ELM is introduced to classify the terrain image collected in the field.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
