Preliminary Study of LIDAR Scanner-Based Collision Avoidance and Automated Guided Systems for Autonomous Power Equipment Products 2018-01-0032
Technology is continuously being developed to prevent self-driving vehicles from crashing. That technology could also be considered for other autonomous products. Collision avoidance in automated, guided systems using a light detection and ranging (LIDAR) scanner has been studied for application in low-speed autonomous Honda Power Equipment products, such as self-driving lawn mowers. The automotive application of a LIDAR scanner for autonomous driving is used for obstacle detection and offline local area. Such delineations do not exist in areas where power equipment is used, such as grass fields; therefore, identifying object height and distance, is a relatively new area. For this study, a small LIDAR scanner with a resolution of 0.01 m and a measurement range of 0.05 m to 40.00 m was used on a Honda self-driving lawn mower. The measurement distance data was directly processed in the scanner, enabling the drive unit to obtain distance information during actual operation. Based on real-time data, collision avoidance and automated operation guidance could be achieved. Simplified object detection and an automated guided decision-making system were developed. System parameters were considered to optimize collision avoidance and the structure of the automated guided system. Field testing was performed at a dedicated test field facility, and the test condition was determined. Fences, tall grass, and ground-levels were successfully classified during testing operation. Collision avoidance, running, and stop modes were identified by on-board LIDAR scan data. Based on the field test analysis, a developed autonomous system structure is suitable for lawn, snow, and future power equipment applications.
Citation: Hasegawa, T. and Wians, J., "Preliminary Study of LIDAR Scanner-Based Collision Avoidance and Automated Guided Systems for Autonomous Power Equipment Products," SAE Technical Paper 2018-01-0032, 2018, https://doi.org/10.4271/2018-01-0032. Download Citation
Toshiyuki Hasegawa, Jeff Wians
Honda R & D Americas, Inc., Honda R&D Americas, Inc.