School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.
US Department of Veterans Affairs, Pittsburgh, PA 15206, USA.
Sensors (Basel). 2021 Nov 24;21(23):7810. doi: 10.3390/s21237810.
Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user's speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.
普通电动轮椅无法安全地通过建筑障碍物(如路缘),这可能会伤害使用者并损坏轮椅。为了解决这个问题,已经开发出了机器人轮椅;然而,在通过路缘之前,使用者需要进行适当的对准。身体和/或感官有障碍的用户可能会发现很难通过这些障碍物。因此,开发了一种路缘识别和通过(CRN)系统,以提高用户在通过路缘时的速度和安全性。本文介绍了 CRN 系统,该系统结合了现有的移动增强机器人(MEBot)的路缘通过应用程序和一种名为 Polylidar3D 的平面提取算法,以识别路缘特征,并自动接近和通过路缘。在受控条件下,评估了 CRN 系统对具有已知高度和 15 个起始位置的工程路缘的检测准确性和可靠性。CRN 系统在 15 个起始位置中的 14 个位置成功识别出路缘,并正确确定了 MEBot 向路缘行驶的高度和距离。虽然 MEBot 的路缘对准角度为 1.5 ± 4.4°,但路缘上升是安全执行的。这些发现为实施机器人轮椅提供了支持,以提高通过路缘时的速度和减少人为错误,并提高可达性。