Preliminary Study of LIDAR Scanner-Based Collision Avoidance in Automated Guided Systems for Autonomous Power Equipment Products

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 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 onboard LIDAR scan data. Based on the field test analysis, a developed autonomous system structure is suitable for lawn, snow, and future power equipment applications.