The development of autonomous navigation and obstacle avoidance for a robotic mower using machine vision technique

The autonomous driving of agricultural machinery using information from global navigation satellite system (GNSS) information has developed rapidly because it is considered as a labor-saving measure in agriculture. The agricultural machinery is able to locate its position using a GNSS signal allowing it to move in an area autonomously. However, if machinery uses the GNSS signal only to self-locate it may run the risk of colliding with obstacles as it may not accurately sense the surrounding environment. Furthermore, sensors such as radars or lasers cannot distinguish between grass and obstacles; hence they cannot be used for sensing an agricultural environment including the detection of obstacles that are likely to be encountered by the machinery. Autonomous driving cannot be performed in environments such as orchards where the satellite positioning accuracy is low. This paper presents an autonomous driving system that we developed that is able to avoid obstacles and drive without the aid of a GNSS signal. The system uses an object detection system that is based on a stereo camera and deep learning technique i.e. convolutional neural networks as they can be used to recognize an environment and avoid obstacles. The autonomous driving ability of the vehicle was evaluated using real-time kinematic-GNSS to measure the true values through experiments that were conducted in the Tanashi Forest of the University of Tokyo. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.