于乃功, 谢秋生, 李洪政. 基于改进区域生长的仿人机器人楼梯参数估计方法研究[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.05.10.001
引用本文: 于乃功, 谢秋生, 李洪政. 基于改进区域生长的仿人机器人楼梯参数估计方法研究[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.05.10.001
Research on staircase parameter estimation method of humanoid robot based on improved regional growth[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.05.10.001
Citation: Research on staircase parameter estimation method of humanoid robot based on improved regional growth[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.05.10.001

基于改进区域生长的仿人机器人楼梯参数估计方法研究

Research on staircase parameter estimation method of humanoid robot based on improved regional growth

  • 摘要: 环境感知对仿人机器人自主导航和运动规划具有重要研究意义,是仿人机器人在复杂环境中进行自主移动进而完成特定任务的前提。而特殊的楼梯场景成为仿人机器人环境感知的难点之一。针对楼梯障碍物破坏阶梯平面特征,导致仿人机器人获取不准确的楼梯参数而出现踏空、摔跤等问题,本文结合区域生长和平面构造方法识别和剔除楼梯障碍物点云,基于剔除障碍物后的楼梯进行三维参数估计。首先利用相邻点的投影之和最小原理准确完成对楼梯水平面的提取;其次根据区域生长算法判定楼梯障碍物聚类情况,构造平面并分析平面内点数以完成对障碍物点云的快速识别与剔除工作;最后对有障碍物楼梯与剔除障碍物楼梯进行楼梯三维感知实验。实验结果表明,本文剔除楼梯障碍物的平均精度为92.43%,且剔除后的楼梯参数感知误差仅为有障碍物时的0.5倍。总体表明所提算法能提高机器人在复杂楼梯环境中的楼梯参数估计精度,能够有效提高仿人机器人在复杂楼梯环境下的感知能力。

     

    Abstract: The perception of the environment holds significant research importance for humanoid robots' autonomous navigation and motion planning. It serves as a prerequisite for them to move autonomously and accomplish specific tasks in complex environments. The specialized scenario of staircases has emerged as a formidable challenge in the environment perception process for humanoid robots. Addressing the problem related to the degradation of staircase plane features due to staircase obstacle interference, leading to inaccuracies in staircase parameter acquisition and resulting in complications such as missteps and wrestling, this study combines region growing and plane fitting methods to first identify and remove point cloud obstacles on the stairs, and then performs three-dimensional parameter estimation based on the cleared stairs. Initially, depth cameras are used to capture point clouds of the stair environment and undergo downsampling. Then, the KD-Tree algorithm establishes the topological structure of point cloud data, utilizing the principle of minimizing the sum of projections of neighboring points to accurately extract the horizontal plane of stairs. Subsequently, the region growing algorithm determines stair obstacle clustering, directly removing individually clustered obstacles based on clustering results, and eliminating non-individually clustered obstacles based on plane construction and analysis of point numbers within the plane. Finally, experiments on obstacle removal are conducted using multiple sets of stair data containing obstacles. The results indicate that this method can accurately recognize and eliminate various types of stair obstacles, with an average elimination accuracy of 92.43%. Additionally, the experiments elimination that the presence of obstacles primarily affects the humanoid robot's acquisition of stair height and depth information, with height errors reaching 30% and an overall average relative error of 16%. However, after obstacle elimination, the average relative error of stair parameter perception is approximately 7%. In general, the proposed algorithm improves the accuracy of stair parameter estimation for robots and effectively enhances the perception capabilities of humanoid robots in complex stair environments.

     

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