A system for semi-autonomous tractor operations
Tractors are the workhorses of the modern farm. By automating these machines, we can increase the productivity, improve safety, and reduce costs for many agricultural operations. Many researchers have tested computer-controlled machines for farming, but few have investigated the larger issues such as how humans can supervise machines and work amongst them. In this paper, we present a system for tractor automation. A human programs a task by driving the relevant routes. The task is divided into subtasks and assigned to a fleet of tractors that drive portions of the routes. Each tractor uses on-board sensors to detect people, animals, and other vehicles in the path of the machine, stopping for such obstacles until it receives advice from a supervisor over a wireless link. A first version of the system was implemented on a single tractor. Several features of the system were validated, including accurate path tracking, the detection of obstacles based on both geometric and non-geometric properties, and self-monitoring to determine when human intervention is required. Additionally, the complete system was tested in a Florida orange grove, where it autonomously drove seven kilometers.
path tracking agricultural robots position estimation sensor fusion obstacle detection