Terrain Recognition and Path Planning for Quadruped Robot

In order to improve the adaptability of the quadruped robot in complex environments, the environment perception strategy based on time of flight (TOF) camera is investigated, and the terrain recognition and path planning algorithms are improved.Firstly, the Gaussian process regression (GPR) model is used to calibrate the range error of the TOF camera, and with this model, the high-order computation and complex function composition caused by polynomial or trigonometric function models are avoided.Based on the depth information of the environment, the terrain is represented with the digital elevation model (DEM) and recognized by the slope, roughness and step height of the grid.The roughness is obtained through the dispersion between the slope plane in which the grid locates and its 8-neighbor height points, which avoids the detection error when using the variance of the terrain height.Based on the terrain information, a path planning algorithm using sliding window and incremental A^* (IA^*) is proposed.When IA^* is used in route planning, the optimal route from the current route to the new goal projection is searched out incrementally.Compared with A^*, the IA^* algorithm enables the robot to replan a path more efficiently.The simulation and experiment results illustrate the feasibility and effectiveness of the proposed algorithms.