Advances in vehicle electronics and satellite navigation technologies have resulted in automatic guidance systems in agriculture that enable increased productivity and convenience. Systems such as John Deere’s AutoTrac provide closed-loop control of a vehicle to a straight line or curved path via steering control. Electrohydraulic controllers provide steer-by-wire integration with the vehicle. Key components of these systems include a global positioning satellite (GPS) receiver, a method of steering actuation and a user interface.
Automatic guidance systems increase productivity through accurate operation at higher speeds for most tasks, and by providing the ability to perform night operations. Other benefits of these systems include reducing the skill level needed and the human fatigue experienced in operating the machine.
The level of precision needed to perform agricultural operations depends on the task. It ranges from meter-level precision of GPS for mapping of information such as harvest data, decimeter-level accuracy for guidance for tillage and spraying, and centimeter levels of control to manage the needs of each plant. Fusion of sensor information provides further accuracy: sensors are used to detect the cut-uncut edge of the crop as well as mechanical feelers or advanced noncontact methods such as perception sensing with cameras or lidar.
Agricultural vehicle guidance requires consideration of the kinematics of the vehicle system, the pose of the vehicle regarding the terrain, and the position of the vehicle with respect to growing crops. The lever-arm effect experienced by the GPS receiver is compensated by sensor fusion with an inertial measurement unit (IMU) that measures the pose so it can be corrected geometrically. Figure 1 shows lever-arm correction of the GPS sensor through sensor fusion.
The advances in automatic guidance have combined with other technology advances such as access to information and other forms of machine system automation to enable machine system productivity. Advances include automation of machine functions, coordination of machine-to-machine operations, and increased levels of machine system automation.
Communications between vehicles and from the vehicle to data storage and management systems is expanding productivity by allowing information to be documented more accurately and quickly and enabling coordination between vehicles and information from season to season.
Telematics, which provides communication from the vehicle to an office, gives information on the use of the machine system and allows the farmer to make decisions that will improve productivity.
Communication between machines enables the coordination of activities on machines, resulting in increased performance. MachineSync, shown in Figure 2, uses Global Navigation Satellite System (GNSS) technology on two machine systems to coordinate machine operations between a combine and a tractor-grain cart so that unloading can occur while the systems move through the field.
Machine systems can also be optimized by systems that generate machine path plans across the landscape. In this way, the guidance pattern can be optimized as the machine traverses a field. Machine operations can be integrated with these plans to manage the vehicle/implement actions according to location within the path plan.
All of these technologies are part of a progression of increasing the machine productivity of human-machine systems. However, the system focus around the machine can only partially achieve the productivity optimization across the production system and worksite. Expanding the focus to the worksite ecosystem will further improve productivity and convenience.
Integrated worksite solutions are the next generation of tools to increase agricultural productivity. They enable increased machine systems automation, optimization of various interacting systems, including ergonomic performance, and optimization of the worksite through information management and decision support. These solutions consist of products and services for the worksites that also interact with the broader industrial ecosystem. Applications include management of inputs such as seed and fertilizer, and outputs such as crop yield.
Other sources of autonomy in production systems include automating discrete machine-systems, automating the data infrastructure used to manage the worksite, and modular approaches to autonomous systems that conduct clean-sheet redesign of the machines and tasks required. Beyond machine autonomy, robust communication in the field and data infrastructure is required for the development of sophisticated autonomous systems and can also benefit manned operations.
Innovations in machine systems automation continue to increase productivity and convenience in farm production. These innovations will progress into integrated worksite solutions, which will transform agriculture production systems.
This article is based on SAE technical paper 2016-01-8006 authored by John Reid, Stewart Moorehead, and Julian Sanchez of Deere & Company; and Alex Foessel of John Deere Brasil SA. The paper will be presented at the 2016 SAE Commercial Vehicle Engineering Congress.