| 注册
首页|期刊导航|机器人|四足机器人地形识别与路径规划算法

四足机器人地形识别与路径规划算法

张慧 荣学文 李贻斌 李彬 丁超 张俊文 张勤

机器人2015,Vol.37Issue(5):546-556,11.
机器人2015,Vol.37Issue(5):546-556,11.DOI:10.13973/j.cnki.robot.2015.0546

四足机器人地形识别与路径规划算法

Terrain Recognition and Path Planning for Quadruped Robot

张慧 1荣学文 1李贻斌 1李彬 1丁超 1张俊文 1张勤2

作者信息

  • 1. 山东大学控制科学与工程学院,山东 济南 250061
  • 2. 济南大学自动化与电气工程学院,山东 济南 250022
  • 折叠

摘要

Abstract

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 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.

关键词

四足机器人/地形识别/路径规划/TOF相机/高斯过程回归

Key words

quadruped robot/terrain recognition/path planning/TOF camera/Gaussian process regression

分类

信息技术与安全科学

引用本文复制引用

张慧,荣学文,李贻斌,李彬,丁超,张俊文,张勤..四足机器人地形识别与路径规划算法[J].机器人,2015,37(5):546-556,11.

基金项目

国家863计划(2015AA042201) (2015AA042201)

国家自然科学基金(61233014,61305130) (61233014,61305130)

山东省自然科学基金(ZR2013FQ003,ZR2013EEM027, ZR2014FP009) (ZR2013FQ003,ZR2013EEM027, ZR2014FP009)

中国博士后科学基金(2013M541912). (2013M541912)

机器人

OA北大核心CSCDCSTPCD

1002-0446

访问量4
|
下载量0
段落导航相关论文