说明：  为了提高四足机器人在复杂环境下的适应性,重点研究了采用飞行时间(TOF)原理相机的四足机器人环境感知策略并改进了地形识别及路径规划算法.首先采用高斯过程回归(GPR)模型对TOF相机的距离数据进行误差校正,解决了采用传统多项式或三角函数模型进行误差修正时模型阶次过高及函数组合复杂的问题.基于得到的环境深度信息,采用数字高程模型(DEM)进行地形描述,并通过计算各栅格的坡度、粗糙度、起伏度对地形进行识别.粗糙度由该栅格所处的坡度平面与其8邻域高程点的离散程度进行计算,避免了采用高程方差计算时对粗糙度的误检测.依靠地形信息,提出了滑动窗+增量式A*(IA*)算法的路径规划方法. IA*算法通过寻找当前路径与目标投影点的最优路径进行增量式路径规划,该方法解决了采用A*算法进行重规划时效率低的问题.仿真和实验结果验证了本文方法的有效性和可靠性(文章被EI期刊收录)
(In order to improve the adaptability of the quadruped robot in complex environments, the environment percep- tion 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 digi- tal 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 in)