Automated Curb Recognition and Negotiation for Robotic Wheelchairs

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.

[1]  Vicente Feliú Batlle,et al.  Trajectory Planning for a Stair-Climbing Mobility System Using Laser Distance Sensors , 2016, IEEE Systems Journal.

[2]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[3]  Shuro Nakajima A New Personal Mobility Vehicle for Daily Life: Improvements on a New RT-Mover that Enable Greater Mobility are Showcased at the Cybathlon , 2017, IEEE Robotics & Automation Magazine.

[4]  Robin Sibson,et al.  SLINK: An Optimally Efficient Algorithm for the Single-Link Cluster Method , 1973, Comput. J..

[5]  Fernando Santos Osório,et al.  Robust curb detection and vehicle localization in urban environments , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[6]  Xiaonian Wang,et al.  Road-Segmentation-Based Curb Detection Method for Self-Driving via a 3D-LiDAR Sensor , 2018, IEEE Transactions on Intelligent Transportation Systems.

[7]  Jiwon Seo,et al.  Low-Cost Curb Detection and Localization System Using Multiple Ultrasonic Sensors , 2019, Sensors.

[8]  Line Simplification Algorithms , 2004 .

[9]  Caixia Deng,et al.  An Improved Canny Edge Detection Algorithm , 2015 .

[10]  Konstantinos G. Derpanis,et al.  Overview of the RANSAC Algorithm , 2005 .

[11]  Sergiu Nedevschi,et al.  Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[12]  Rory A Cooper,et al.  Step-Climbing Power Wheelchairs: A Literature Review. , 2017, Topics in spinal cord injury rehabilitation.

[13]  Rory A Cooper,et al.  Kinematics and Stability Analysis of a Novel Power Wheelchair When Traversing Architectural Barriers. , 2017, Topics in spinal cord injury rehabilitation.

[14]  Sergiu Nedevschi,et al.  Curb detection in urban traffic scenarios using LiDARs point cloud and semantically segmented color images , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[15]  R. Simpson,et al.  How many people would benefit from a smart wheelchair? , 2008, Journal of rehabilitation research and development.

[16]  Stanislav Panev,et al.  Road Curb Detection and Localization With Monocular Forward-View Vehicle Camera , 2019, IEEE Transactions on Intelligent Transportation Systems.

[18]  Rory A Cooper,et al.  A Heuristic Approach to Overcome Architectural Barriers Using a Robotic Wheelchair , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Claude Vincent,et al.  Development of an obstacle course assessment of wheelchair user performance (OCAWUP): A content validity study , 2004 .

[20]  Gail Powell-Cope,et al.  Wheelchair‐related Falls: Current Evidence and Directions for Improved Quality Care , 2005, Journal of nursing care quality.

[21]  Measurement and Analysis of Depth Resolution Using Active Stereo Cameras , 2021, IEEE Sensors Journal.

[22]  Hrishikesh D. Vinod Mathematica Integer Programming and the Theory of Grouping , 1969 .

[23]  Rory A. Cooper,et al.  Trends and Issues in Wheelchair Technologies , 2008, Assistive technology : the official journal of RESNA.

[24]  Lynn A. Worobey,et al.  Type and frequency of wheelchair repairs and resulting adverse consequences among veteran wheelchair users , 2020, Disability and rehabilitation. Assistive technology.

[25]  Kara Edwards,et al.  A survey of adult power wheelchair and scooter users , 2010, Disability and rehabilitation. Assistive technology.

[26]  Reed Johnson Autonomous Navigation On Urban Sidewalks Under Winter Conditions , 2020 .

[27]  J. Guerrero,et al.  Road Curb Detection: A Historical Survey , 2021, Sensors.

[28]  Bin Dai,et al.  Velodyne-based curb detection up to 50 meters away , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[29]  Zhidong Deng,et al.  Road curb detection using 3D lidar and integral laser points for intelligent vehicles , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[30]  Rory A Cooper,et al.  Usability Evaluation of a Novel Robotic Power Wheelchair for Indoor and Outdoor Navigation. , 2019, Archives of physical medicine and rehabilitation.

[31]  Josef Kittler,et al.  The Adaptive Hough Transform , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Øyvind Stavdahl,et al.  Perception-Driven Obstacle-Aided Locomotion for Snake Robots: The State of the Art, Challenges and Possibilities † , 2017 .

[33]  Dennis Hong,et al.  Analysis and Noise Modeling of the Intel RealSense D435 for Mobile Robots , 2019, 2019 16th International Conference on Ubiquitous Robots (UR).

[34]  Ella Atkins,et al.  Polylidar3D-Fast Polygon Extraction from 3D Data , 2020, Sensors.

[35]  Ahmet M. Kondoz,et al.  Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots , 2017, Sensors.

[36]  Derek D. Lichti,et al.  DETECTION OF ROAD CURB FROM MOBILE TERRESTRIAL LASER SCANNER POINT CLOUD , 2012 .

[37]  Aashto,et al.  A Policy on geometric desing of highways and streets. , 1984 .

[38]  Sean Bennett,et al.  Wheelchair accessibility: Descriptive survey of curb ramps in an urban area , 2009, Disability and rehabilitation. Assistive technology.

[39]  Sergiu Nedevschi,et al.  Fusing semantic labeled camera images and 3D LiDAR data for the detection of urban curbs , 2018, 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP).