Are you working for a heavy equipment manufacturer or supplier? Or, like me, do you have a job related to the heavy equipment industry?
If so, you might be as excited as I am by the release of the Monarch new jewel: the world’s first fully autonomous electric tractor. The Monarch tractor combines electrification, automation, machine learning, and data analysis to enhance farmer’s operations, and increase labor productivity and safety. As Praveen Penmetsa, Monarch Tractor co-founder and CEO, summarized: “Monarch Tractor is ushering in the digital transformation of farming with unprecedented intelligence, technology, and safety features.” In this context, a strong autonomous heavy equipment simulation platform is mandatory.
The objective of this article is to give insights into how a simulation-driven approach can support the development of heavy equipment autonomous operation systems. An approach going from sensors design to control system verification and validation.
The Rise of Autonomous Operations in the Heavy Equipment Industry: The heavy equipment customer’s main objective is to increase their machine efficiency in the field by producing more with less while keeping a strict eye on operator safety. But with machines that become more and more specific, more and more complex, guaranteeing people safety and machine integrity also depends a lot on the operator’s skills. Partly or fully automated machine operation is a solution that most of the industry stakeholders are already investigating to increase safety and improve operability. Consequently, they can lead the digital revolution we face today.
Therefore, in this article, I will deliver some insights on the following key pillars to build a strong comprehensive digital framework to support autonomous heavy equipment development.
- The role of simulation and of the digital twin
- Natural environment simulation
- Physical sensor modeling
- Heavy equipment vehicle modeling
- Automate and accelerate the verification workflow
The Role of Simulation and of The Digital Twin: Model-based systems engineering (MBSE) is an approach that fits perfectly with the development of autonomous vehicles – many companies have been using it for years. Today, we’ve entered a period of intense innovation, and manufacturers are under tremendous pressure to reduce program costs. We see more system autonomy to deliver new mission capabilities leading to more interactions between the thousands of systems, interfaces, and components on a single heavy machine.
Autonomous Heavy Equipment Natural Environment Simulation: When it comes to the natural environment modeling, the simulation solution should be open and flexible enough to allow the import of any type of:
- Off-road vehicle geometric models
- Geometric terrain with a relevant drop, slopes, and obstacles like rocks, trees, pedestrians, other vehicles
- Harsh conditions implied by the natural environment seasonality (rain, fog, dirt, day & night, sunset or sunrise, etc.)
This allows to perfectly match with conditions regularly encountered by heavy-equipment vehicles.
Physical Sensor Modeling: When it comes to the replacement of the operator senses, one question summarizes the engineering challenges of implementing sensors like cameras, radars, LiDARs: How can you predict what the machine will or will not sense?
Consider that a physical sensor modeling solution is mandatory for a simulation platform dedicated to autonomous operations development. Indeed, optimization of the sensor design and its configuration can be done virtually to a large extent.
Heavy Equipment Vehicle Modeling: A skilled operator has hours of machine operations background experience. In order to improve the performance of the autonomous operations of the vehicle, it is mandatory to include accurate dynamics within the control virtual training, verification, and validation process. From soil and tire model to the vehicle dynamics, the more physics you implement the more robust your control will be, improving operability and safety.
The automation of the machine positively impacts other attributes. Indeed, it enables to better control the energy distribution, or yet powertrain actuation resulting in improved powertrain durability, keeping the machine within the safety zone of loads.
Automate and Accelerate The Verification Workflow: A key element leading to a significant reduction of the cost of autonomous heavy equipment simulation is the automation of the algorithm verification and validation workflow. Indeed, automating the validation of algorithms performance under various weather or lighting conditions, or simply making sure that the workflow covers all possible scenarios, allows an improved coverage of your perception algorithm validation, not talking about development time and cost-saving associated with this automation.
Sensor and vehicle configuration, sensor mounting, and vehicle design exploration can also be automated. In the example above, Simcenter HEED, Siemens DISW design space exploration, and optimization software package allow this performance verification automation.
The objective is the deployment of the virtual verification framework of ADAS and autonomous vehicle systems, allowing easy scenario generation, efficient critical case identification, and systematic virtual verification of requirements.
Conclusion: To conclude, I would like to pass along the message that Siemens DISW with its comprehensive Xcelerator portfolio of software, services, and applications can support the development of your autonomous machine and its operations, and can also virtually provide you with a simulation platform to validate your machine performances.
Continue reading in detail about A Simulation Approach for Autonomous Heavy Equipment Safety. OR Learn more about our Simcenter solution.