Rolls-Royce and ASI Mining have agreed to ensure compatibility of MTU engines and ASI’s Mobius command and control software for autonomous vehicles. The two partners have signed a Memorandum of Understanding enabling Rolls-Royce to offer autonomous-compatible, Mobius-ready MTU engine solutions for equipment in a wide range of mining applications.
With its brand MTU, Rolls-Royce business unit Power Systems is a leading provider of advanced power solutions for a wide variety of applications, including mining equipment. ASI Mining is an industry leader in the development and sales of high-tech autonomous solutions for mining equipment and other machinery in a wide range of applications.
The companies plan to leverage their extensive experiences to offer customers engine solutions that are compatible with ASI’s vehicle automation software to help optimize vehicle power performance and efficiency, thus enabling more environmentally friendly and safer mining operations.
One potential benefit to customers of Rolls-Royce and ASI Mining may be the ability to retrofit the power system on existing haul trucks and convert them to autonomous operation. The companies are interested in exploring the value customers would receive by modernizing their trucks with more efficient MTU engines along with implementation of ASI’s industry-leading autonomous mining solutions. The customers would thus save on operating costs and further benefit from the increased performance of the autonomously optimized MTU engines.
MTU diesel engines have been setting the standards for performance and fuel-efficiency in mining applications around the globe for decades. They power vehicles for underground and surface mining, including loading vehicles such as excavators and wheel loaders, transport vehicles such as haul trucks or blast hole drilling rigs, and other mining machines—diesel-mechanic, diesel-electric or diesel-hydraulic.
For these applications, MTU engines provide high performance, reliability and availability as well as a maintenance-friendly construction. Long service intervals and an efficient use of fuel provide for exceptionally low operating costs of machines powered with MTU engines.
Autonomous Solutions, Inc. (ASI) is a world leader in industrial vehicle automation. ASI serves clients across the world in the mining, agriculture, automotive, government, and manufacturing industries with remote control, teleoperation, and fully automated solutions from its headquarters and 100-acre proving ground in northern Utah.
Occlusion mapping. In November 2019, ASI reported developing an improved algorithm for autonomous vehicles to detect drop-offs and other large negative obstacles often found in the environments in which automated off-road vehicles operate.
For safe navigation through an environment, autonomous ground vehicles rely on sensor data representing 3D space surrounding the vehicle. Often this data is obscured by objects or terrain, producing gaps in the sensor field of view. These gaps, or occlusions, can indicate the presence of obstacles, negative obstacles, or rough terrain.
Occlusions can be defined as a blockage which prevents a sensor from gathering data in a location. For example, occlusions can be seen as shadows in LiDAR data.
Because sensors receive no data in these occlusions, sensor data provides no explicit information about what might be found in the occluded areas. Information about the occlusions must be inferred from using an occlusion mapping algorithm to provide the navigation system with a more complete model of the environment.
Application of this new technology can be useful in settings with dump edges at mine sites, steep road edges, canals, ditches, hills or stairs for indoor or urban environments.
The occlusion mapping algorithm has three main components:
A sensor field of view (FOV) model that describes what obstacles the sensors are expected to detect. This component is designed for point cloud sensors such as 3D LiDAR, Flash LiDAR, Structured Light, and Stereo Cameras.
An occlusion map is maintained and updated using the sensor FOV model and current sensor data to provide a probabilistic estimate on areas that have not been detected within the sensor FOV.
The integration of the occlusion map into an autonomous vehicle navigation system. It is designed to work with and complement existing obstacle detection and avoidance systems.