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Sensors 2019, 19(6), 1292; https://doi.org/10.3390/s19061292

Learning the Cost Function for Foothold Selection in a Quadruped Robot

1
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
2
State Key Laboratory of Robotics and System (HIT), Harbin 150080, China
3
Shenzhen Academy of Aerospace Technology, Shenzhen 518057, China
*
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in “Li, X.; Li, J.; Guo, Y. Foothold selection for quadruped robot based on learning from expert. In Proceedings of the IEEE International Conference on Advanced Robotics and Mechatronics, Hefei, China, 27–31 August 2017; pp. 223–228”.
Received: 13 February 2019 / Revised: 8 March 2019 / Accepted: 8 March 2019 / Published: 14 March 2019
(This article belongs to the Special Issue Semantic Representations for Behavior Analysis in Robotic system)
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Abstract

This paper is focused on designing a cost function of selecting a foothold for a physical quadruped robot walking on rough terrain. The quadruped robot is modeled with Denavit–Hartenberg (DH) parameters, and then a default foothold is defined based on the model. Time of Flight (TOF) camera is used to perceive terrain information and construct a 2.5D elevation map, on which the terrain features are detected. The cost function is defined as the weighted sum of several elements including terrain features and some features on the relative pose between the default foothold and other candidates. It is nearly impossible to hand-code the weight vector of the function, so the weights are learned using Supporting Vector Machine (SVM) techniques, and the training data set is generated from the 2.5D elevation map of a real terrain under the guidance of experts. Four candidate footholds around the default foothold are randomly sampled, and the expert gives the order of such four candidates by rotating and scaling the view for seeing clearly. Lastly, the learned cost function is used to select a suitable foothold and drive the quadruped robot to walk autonomously across the rough terrain with wooden steps. Comparing to the approach with the original standard static gait, the proposed cost function shows better performance. View Full-Text
Keywords: quadruped robot; foothold selection; TOF camera; 2.5D elevation map; supporting vector machine quadruped robot; foothold selection; TOF camera; 2.5D elevation map; supporting vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, X.; Gao, H.; Zha, F.; Li, J.; Wang, Y.; Guo, Y.; Wang, X. Learning the Cost Function for Foothold Selection in a Quadruped Robot. Sensors 2019, 19, 1292.

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