![\"autonomous](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2021\/01\/Picture-1.png\")
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.<\/p>\n\n\n\n
The rise of autonomous operations in the heavy equipment industry<\/strong><\/h2>\n\n\n\nThe heavy equipment customer\u2019s 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\u2019s skills.<\/p>\n\n\n\n
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. <\/p>\n\n\n\n
With an increasing number of Advanced Driver Assistance Systems (ADAS) features to improve operator safety, such as remote-control driving\/actuation or fully autonomous driving, the interactions between the operator and the vehicle are being reshaped. Nevertheless, working on heavy equipment autonomous operations means partially or fully replacing a skilled operator. These operators bring four main elements to their position: his\/her senses, his\/her brain, his\/her experience, and the environment they interact with. <\/p>\n\n\n\n
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. <\/p>\n\n\n\n
- The role of simulation and of the digital twin<\/strong><\/a><\/li>
- Natural environment simulation<\/a><\/strong><\/li>
- Physical sensor modeling<\/a><\/strong><\/li>
- Heavy equipment vehicle modeling<\/a><\/strong><\/li>
- Automate and accelerate the verification workflow<\/a><\/strong><\/li><\/ul>\n\n\n\n
The role of simulation and of the digital twin<\/strong><\/h2>\n\n\n\nModel-based systems engineering (MBSE) is an approach that fits perfectly with the development of autonomous vehicles \u2013 many companies have been using it for years. Today, we\u2019ve 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.<\/p>\n\n\n\n
When it comes to product design and performance engineering, the MBSE approach brings the so-called \u2018Digital Twin\u2019 of the product\/vehicle as well as its environment. It allows engineers to virtually explore the autonomous vehicle designs, as well as accelerate advanced control verification and validation, which can be tough for vehicle durability and expensive if pursued using a classical physical testing approach. Even if you work with the state-of-the-art data acquisition solution dedicated to ADAS<\/a>. <\/p>\n\n\n\nAutonomous heavy equipment natural environment simulation<\/strong><\/h2>\n\n\n\nWhen it comes to the natural environment modeling, the simulation solution should be open and flexible enough to allow the import of any type of: <\/p>\n\n\n\n
- off-road vehicle geometric models,<\/li>
- geometric terrain with a relevant drop, slopes, and obstacles like rocks, trees, pedestrians, other vehicles, <\/li>
- harsh conditions implied by the natural environment seasonality (rain, fog, dirt, day & night, sunset or sunrise, etc.)<\/li><\/ul>\n\n\n\n
This allows to perfectly match with conditions regularly encountered by heavy-equipment vehicles.<\/p>\n\n\n\n
To create a good virtual digital twin of the field environment, Simcenter Prescan<\/a>, the solution developed by Siemens Digital Industries Software (DISW), ensures objects have a good enough geometric description and material property description by considering what is important for each sensor modality: camera, LiDAR, radar.<\/p>\n\n\n\n![\"\"](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2021\/01\/Picture-3.jpg\")
<\/figure><\/li>![\"\"](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2021\/01\/Picture-2.jpg\")
<\/figure><\/li><\/ul>Figure: Examples of agricultural and construction environments, both including natural obstacles, crossing pedestrians, and moving machines.<\/em><\/figcaption><\/figure>\n\n\n\n<\/div>\n\n\n\nAdditionally, Siemens DISW teams have been working in the past years on the coupling between its environment and its system simulation software, as well as investing in improved ground modeling technology. The Simcenter Amesim<\/a> Track generator feature is an example. This feature allows the implementation of bumpy soil, with a certain penetration coefficient to consider the impact of the soil on autonomy sensor performance at vehicle low or high speed. The interactions between vehicle dynamics, traction on soft soil, and the powertrain are crucial.<\/p>\n\n\n\n
The role of simulation and of the digital twin<\/strong><\/h2>\n\n\n\nModel-based systems engineering (MBSE) is an approach that fits perfectly with the development of autonomous vehicles \u2013 many companies have been using it for years. Today, we\u2019ve 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.<\/p>\n\n\n\n
When it comes to product design and performance engineering, the MBSE approach brings the so-called \u2018Digital Twin\u2019 of the product\/vehicle as well as its environment. It allows engineers to virtually explore the autonomous vehicle designs, as well as accelerate advanced control verification and validation, which can be tough for vehicle durability and expensive if pursued using a classical physical testing approach. Even if you work with the state-of-the-art data acquisition solution dedicated to ADAS<\/a>. <\/p>\n\n\n\nAutonomous heavy equipment natural environment simulation<\/strong><\/h2>\n\n\n\nWhen it comes to the natural environment modeling, the simulation solution should be open and flexible enough to allow the import of any type of: <\/p>\n\n\n\n
- off-road vehicle geometric models,<\/li>
- geometric terrain with a relevant drop, slopes, and obstacles like rocks, trees, pedestrians, other vehicles, <\/li>
- harsh conditions implied by the natural environment seasonality (rain, fog, dirt, day & night, sunset or sunrise, etc.)<\/li><\/ul>\n\n\n\n
This allows to perfectly match with conditions regularly encountered by heavy-equipment vehicles.<\/p>\n\n\n\n
To create a good virtual digital twin of the field environment, Simcenter Prescan<\/a>, the solution developed by Siemens Digital Industries Software (DISW), ensures objects have a good enough geometric description and material property description by considering what is important for each sensor modality: camera, LiDAR, radar.<\/p>\n\n\n\n<\/figure><\/li><\/figure><\/li><\/ul>Figure: Examples of agricultural and construction environments, both including natural obstacles, crossing pedestrians, and moving machines.<\/em><\/figcaption><\/figure>\n\n\n\n<\/div>\n\n\n\nAdditionally, Siemens DISW teams have been working in the past years on the coupling between its environment and its system simulation software, as well as investing in improved ground modeling technology. The Simcenter Amesim<\/a> Track generator feature is an example. This feature allows the implementation of bumpy soil, with a certain penetration coefficient to consider the impact of the soil on autonomy sensor performance at vehicle low or high speed. The interactions between vehicle dynamics, traction on soft soil, and the powertrain are crucial.<\/p>\n\n\n\n
Autonomous heavy equipment natural environment simulation<\/strong><\/h2>\n\n\n\nWhen it comes to the natural environment modeling, the simulation solution should be open and flexible enough to allow the import of any type of: <\/p>\n\n\n\n
- off-road vehicle geometric models,<\/li>
- geometric terrain with a relevant drop, slopes, and obstacles like rocks, trees, pedestrians, other vehicles, <\/li>
- harsh conditions implied by the natural environment seasonality (rain, fog, dirt, day & night, sunset or sunrise, etc.)<\/li><\/ul>\n\n\n\n
This allows to perfectly match with conditions regularly encountered by heavy-equipment vehicles.<\/p>\n\n\n\n
To create a good virtual digital twin of the field environment, Simcenter Prescan<\/a>, the solution developed by Siemens Digital Industries Software (DISW), ensures objects have a good enough geometric description and material property description by considering what is important for each sensor modality: camera, LiDAR, radar.<\/p>\n\n\n\n<\/figure><\/li><\/figure><\/li><\/ul>Figure: Examples of agricultural and construction environments, both including natural obstacles, crossing pedestrians, and moving machines.<\/em><\/figcaption><\/figure>\n\n\n\n<\/div>\n\n\n\nAdditionally, Siemens DISW teams have been working in the past years on the coupling between its environment and its system simulation software, as well as investing in improved ground modeling technology. The Simcenter Amesim<\/a> Track generator feature is an example. This feature allows the implementation of bumpy soil, with a certain penetration coefficient to consider the impact of the soil on autonomy sensor performance at vehicle low or high speed. The interactions between vehicle dynamics, traction on soft soil, and the powertrain are crucial.<\/p>\n\n\n\n
This allows to perfectly match with conditions regularly encountered by heavy-equipment vehicles.<\/p>\n\n\n\n
To create a good virtual digital twin of the field environment, Simcenter Prescan<\/a>, the solution developed by Siemens Digital Industries Software (DISW), ensures objects have a good enough geometric description and material property description by considering what is important for each sensor modality: camera, LiDAR, radar.<\/p>\n\n\n\n Additionally, Siemens DISW teams have been working in the past years on the coupling between its environment and its system simulation software, as well as investing in improved ground modeling technology. The Simcenter Amesim<\/a> Track generator feature is an example. This feature allows the implementation of bumpy soil, with a certain penetration coefficient to consider the impact of the soil on autonomy sensor performance at vehicle low or high speed. The interactions between vehicle dynamics, traction on soft soil, and the powertrain are crucial.<\/p>\n\n\n\n<\/figure><\/li><\/figure><\/li><\/ul>