By Darcy Cook – VP Engineering, JCA Electronics

There has been fast growth in the development of autonomous agricultural machines over the past few years. Most machines that have been promoted publicly are either concept machines developed by OEMs and research organizations, or machines launched by start-ups looking to be the first broad solution in a new market. In addition to this, many major OEMs are developing innovative autonomous machines behind closed doors, with the intention of major product releases in the next few years.

All of these efforts are working towards developing the technologies, applications, and market for autonomous machines in agricultural applications. As more of the work behind the scenes becomes public over the next few years, the scale and impact of the technological shift towards autonomous machines will become increasingly clear.

Many organizations have developed and demonstrated (either publicly or privately) proof-of-concept autonomous machines that provide a confidence in the core technology required for autonomy. This is shifting the thinking of OEMs from asking “Can it be done?” to now answering the question of “How can we make it scale?” 

The shift is one from a research mindset, where technological proof-of-concept demonstration is the goal, to a production-readiness mindset where robustness, reliability, ease-of-use, cost, and safety are critical factors. Traditionally, the transition from proof-of-concept to production systems for operator-driven machines has often been a component-focused process led by OEM procurement groups.

While component reliability is as important as it has ever been, the complexity of autonomous systems means that now a robust system architecture is the most critical consideration when developing a scalable autonomous machine. This paper provides an overview of the subsystems and technologies needed in agricultural autonomous systems, the challenges that are faced by autonomous machine manufacturers in moving from proof-of-concept systems to robust and scalable field-ready systems, and why a cohesive system architecture is key.

Challenge Of Scalability For Autonomous Machine Technologies

In today’s agricultural equipment, operators perform a wide variety of complex tasks that integrate different functions of the system. These include assessment of surroundings, driving the machine, operating the implement functions and adjusting to environmental conditions, and general operation monitoring. These are all tasks that must be automated in one way or another for autonomous machines. This requires a step-change in technological advancement from traditional systems to manage the complexity of these tasks.

Autonomous agricultural systems have only recently become possible because of advances in a wide variety of technology areas including connectivity (IoT), robotics, guidance systems, sensors, and machine learning that serve as the underlying technology that have the capability to perform the complex tasks people currently perform. Many of these technologies are new to the agricultural machine industry and have often originated in other industries that have different interface requirements and environmental constraints. Proof-of-concept autonomous machines have often been developed using existing components repurposed from other industries and/or early dev platforms with new technologies. These are effective in demonstrating that the core functionality needed for autonomy is possible, but unless OEMs plan to ship each machine with a team of engineers that follow it in the field, there needs to be additional effort to design the machine for scalable production.

Scalable production of autonomous systems requires both:

* Components that integrate these new underlying technologies in form factors that are designed for both the environmental conditions and interfaces appropriate for agricultural machines, and

*A robust system architecture that facilities complex interactions between autonomous subsystem functions to provide robust and reliable system interactions.

Read more about developing proof-of-concept autonomous ag machines here