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There’s a large soy farm out in the middle of Kansas whose owner often laughs when anyone suggests that soon he’ll be able to control an entire fleet of self-driving vehicles to do the work for him. In his mind, a vehicle will always require a driver when it comes to working the land.

He says the fields are constantly changing and there are always new obstacles. He thinks a vehicle must be manually controlled to account for the day-to-day changes to complete the job.

This is one of the central issues facing the developers of autonomous vehicles. Environments from industrial farms to mining have always presented a challenge due to the constantly changing surface on which the vehicles operate. How do you create self-driving vehicles that can account for surprise obstacles, changing landscapes, and unpredictable weather?

Rather than settling for semi-autonomous vehicles, which would require human intervention to fine tune the machines for the days exact specifications, ASI’s Research and Development team found a solution. The answer, thanks to advancements in artificial intelligence (AI), is known as World Modeling.

World Modeling allows a fleet of vehicles to interact with each other in real time to create a clear and precise view of the terrain on which they operate. Through machine learning, which is the capability of machines to adjust their decision based off past experience, ASI has been able to design and implement AI algorithms that build a picture of the world through the sensors built into the autonomous vehicles.

This model is simultaneously created and shared by the entire fleet, as well as stored in a database so that it can be accessed at any time. The task of getting from one point to another suddenly becomes a whole lot simpler when the vehicle can automatically fine tune itself for the day’s specific conditions and obstacles. This detail-oriented approach results in not only a more efficient work site, but one that is far safer for both the vehicles and the workers themselves.



Through their innovative use of AI and machine learning, ASI has made itself a world leader in vehicle automation. With their algorithms designed to give vehicles and fleets abilities like world modeling, they are allowing key industries like mining and agriculture to become not only more productive than ever, but safer as well. You can learn more about how ASI is furthering AI and machine learning by visiting our research page.






The invention of the tractor in the 19th century unquestionably revolutionized farming. And it wasn’t long before these engine powered vehicles totally replaced animal drawn machines, completely modernizing the entire agriculture industry for centuries to come.

But now, autonomous technology is proving to be a game-changer for the 21st century farmer. Tractors integrated with artificial intelligence and smart hardware are facilitating more efficient, less labor-intensive farming, and totally transforming agriculture processes once again.

A year ago, ASI and CNH Industrial unveiled the autonomous tractor concept, the cabless Case IH Magnum series tractor. This autonomous vehicle has travelled to agriculture trade shows and events around the world, boastfully displaying its cutting-edge technology and assorted features.

ASI, a leader in autonomous technology, is working with CNH Industrial to perfect their autonomous technology which is projected to increase accuracy, improve management of resources, and save on fuel and labor costs. And with continuous innovation and testing, this technology continues to mature and progress for application in the agriculture industry.

In early 2017 AccuGuide for Case IH made its debut. Developed by ASI and CNH Industrial’s Innovation Group, AccuGuide is an automated steering system that utilizes advanced technology to plan end of row turns in the headlands of a field. This technology made an appearance last month at AgQuip 2017 in Australia and the Farm Progress show in Iowa, where it’s host of impressive features turned many heads.

This state of the art system displayed its extraordinary capabilities of achieving year-to-year repeatable accuracy from sub-inch levels and beyond; reducing skips and overlaps; and promoting better seed, fertilizer, and chemical inputs management. With AccuGuide, operations become simplified, and hours can be added to the day during critical operating windows.

CNH Industrial and ASI are also collaborating on New Holland Agriculture’s conceptual, New Holland Drive, a driverless tractor concept engineered to perform a wide range of farming tasks. This unmanned vehicle can be remotely monitored, commanded and controlled via a desktop computer or portable tablet interface, giving a single individual the ability to manage one or more tractors with remarkable precision and reliability.



A path-plotting screen reveals the tractor’s progress while another shows a live camera feed, providing the user with multiple, real-time views. And because the New Holland Drive tractor is able to work 24/7, it can achieve superior levels of productivity compared to conventional methods, all while reducing the risks associated with human error.

Autonomous technology is dramatically upending the agriculture industry, boosting operational efficiencies and increasing harvests, all while driving down prices.

ASI is excited to be working with CNH Industrial to pioneer autonomous tractor technology and the mechanization of agricultural tasks, making farm work smarter. To learn more about the future of farming, watch our videos featuring the autonomous tractor at asirobots.com/farming today.






While autonomous vehicles are gaining usage across numerous industries, the concept of self-navigation raises many questions, and rightfully so. With no human in direct control of the vehicle to navigate where to go, how to get there, or make real-time decisions along the way, how do autonomous vehicles actually determine their paths?

Accurate and reliable detection of obstacles is paramount to collision avoidance, but can be quite challenging as obstructions tend to vary in size and geometry. Some objects, such as dust, can cause a false-positive or false-negative reaction, when in reality, there is no impending collision.

ASI’s advanced Path Planning and Control makes it all possible. By utilizing state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML), ASI has developed precision control algorithms that enable autonomous vehicles to accomplish their tasks safely and efficiently.

 

Path Planning and Control
The basic framework of Path Planning and Control starts with programming an objective for the autonomous vehicle to achieve. To accomplish this task, the machine must choose a path and adjust to obstacles, terrain, and changing conditions in order to reach its destination safely.

By utilizing a primarily LiDAR-based perception system, a cost surface is created as an input to a nonlinear optimization solver. The current vehicle state, desired vehicle state, perceived-cost surface, vehicle dynamics, and vehicle kinematics are vital inputs the solver uses to generate feasible path options for the vehicle.

 

Autonomous Navigation
ASI’s AI algorithms are then used to facilitate safe and reliable navigation of unknown or dangerous terrain to arrive at the desired location. With this terrain model, the vehicle is able to predict future behaviors for hazard avoidance and optimal trajectory selection.



World Modeling
Exteroceptive sensors on the vehicle collect data from the surroundings, which are processed by the AI algorithm for sensor fusion to build a highly accurate model of the environment. This model can be accessed and updated by any autonomous vehicle entering the area.

Leader Follower Operation
Another feature of ASI’s autonomous vehicles is the ability to follow the leader. Whether the leader be a manned vehicle, unmanned vehicle, or even a person on foot, this ASI algorithm enables autonomous vehicles to follow the defined leader while still maintaining their individual collision avoidance and terrain navigation features.

Machine Learning
ASI’s machine learning is another important part of their autonomous vehicle solutions. Here are some of the ML capabilities:

 

  • Auto Tuning – By monitoring and comparing actual and desired behaviors, parameters are automatically adjusted for continuous improvement.
  • Obstacle Identification and Classification – ASI researchers are improving world modeling and object learning that will then be shared between vehicles for collaborative and improved behaviors.
  • Convoy Operations – ASI is also developing convoy technology that will increase the efficiency of vehicle groups through unknown and harsh terrain.

Leader Follower Operation
Another feature of ASI’s autonomous vehicles is the ability to follow the leader. Whether the leader be a manned vehicle, unmanned vehicle, or even a person on foot, this ASI algorithm enables autonomous vehicles to follow the defined leader while still maintaining their individual collision avoidance and terrain navigation features.

Path Planning and Control are essential features for vehicle automation made possible by ASI’s innovative AI and ML technology. As the world leader in vendor independent vehicle automation systems, Autonomous Solutions, Inc. delivers automation solutions to the mining, agriculture, automotive, government, and manufacturing industries with unrivaled performance.

Learn more about our advanced autonomous vehicle solutions today!






Autonomous vehicles are being utilized across numerous industries resulting in dramatic increases in business performance. To operate safely and successfully, these autonomous vehicles must navigate their surroundings without incident. As such, collision avoidance is one of the largest areas of concern when it comes to the implementation of autonomous vehicles.

Accurate and reliable detection of obstacles is paramount to collision avoidance, but can be quite challenging as obstructions tend to vary in size and geometry. Some objects, such as dust, can cause a false-positive or false-negative reaction, when in reality, there is no impending collision.

The problem of obstacle detection can be reduced to a two-state Markov model, which is a model used for randomly changing systems based on stochastic calculus. The transition between these two states, obstacle or non-obstacle, is then governed by a Bayesian inference which takes into account past probability, current measurements, and sensor characteristics. Thus, the most comprehensive way to correctly understand the real obstacles autonomous vehicles encounter is by applying sensor fusion.



By “fusing” LiDAR and Radar measurements into a complete occupancy grid, along with advanced Artificial Intelligence (AI) and Machine Learning (ML), ASI’s Obstacle Detection Sensor Fusion application optimizes obstacle detection for increased collision avoidance. Through the implementation of predictive analytics which account for prior inputs of cell occupancy probability, a labeled 3D point-cloud from the LiDAR unit, the detected objects by the radar, and the associated radar field of view, ASI’s algorithm builds a complete grid system that identifies, classifies, and circumvents obstacles.

The value in each grid cell is computed using the measurements that are classified as either obstacle or non-obstacle. Then, based on adjustments to both true-positive and true-negative rates, the AI algorithm is tuned for each sensor by using the associated confidence level. This method has been show to enhance collision avoidance.

ASI’s Obstacle Detection Sensor Fusion enables autonomous vehicles to traverse unknown terrain and dangerous obstacles to arrive at a desired endpoint safely. The advanced AI and ML algorithm creates an accurate model of the world, identifies and classifies obstacles and non-obstacles, then stores this information in a database which can be accessed by any autonomous vehicle that enters that region, thereby allowing for continuous updating and learning. Teaching a vehicle to learn is just the beginning — ASI’s research and development team is taking this technology further by enabling autonomous vehicles to share information for a collaborative improvement in vehicle behavior.

Learn more about ASI’s collision detection at work in the mining industry and current areas of research.






Elements of driverless technology are being inserted into every industry and sector, from consumer vehicles to commercial transportation to mining fleets.

The military is also actively pursuing ways in which ground vehicle operations can be made safer and more efficient with the integration of autonomous solutions. One way in which this is possible is by transforming manned convoys into unmanned operations.

A Smarter Convoy

The military relies on convoys to move people and supplies, typically along vast and sometimes treacherous terrain. Obstacles, enemy combatants, even inclement weather can pose challenges that might stand in the way of a successful convoy deployment. However, artificial intelligence and advanced robotics allow convoys to create improved, safer, and more reliable paths.

ASI’s Autonomous Convoy System (ACS) uses innovative technology that enables convoy vehicles to communicat with one another, relaying information about obstacles, terrain or other unsafe conditions. While numerous technology companies have made investments in autonomous convoy development for military applications, The United States Army Tank Automotive Research, Development and Engineering Center (TARDEC) turned to ASI to help advance current autonomous convoy technology.

Our research group has engineered breakthroughs for lateral stability control improvements. This allows leading vehicles to pass information back to followers, which the following vehicles use to improve their path following. We’ve also made advancements in sequential stability, multiple vehicle convoy, and world modeling, as convoys often encounter unmapped territories.



Autonomous Advantages

ACS from ASI allows convoys to chart the smartest path to a destination. Autonomous Ground Resupply (AGR) provides another solution for generating better efficiencies. With AGR, troops can go out into the field with autonomous supply vehicles following them. When supplies diminish, the vehicles can return to base and retrieve more of what the field troops need. This saves time and costs, not having to transport human personnel back and forth across possibly dangerous supply lines, allowing them to continue executing their mission.

The advantages ACS yield for military applications can also be transferred to other industries. Convoys of unmanned vehicles can be useful for farming, shipping, and industrial organizations, reducing costs, decreasing personnel, and saving time by using smarter paths. To learn more about ACS and how ASI technology platforms are automating vehicle transportation, visit us at asirobots.com/platforms.






ASI is working on things that no one else is doing today—taking existing vehicles and converting them to autonomous. We can use the same basic kit on a lot of different vehicles: mining equipment, agriculture vehicles, and everything in between.

This makes it possible to reduce driver exposure to hazardous conditions. After all, a robot can’t get a back or neck injury.

The Mobius technology has been developed over the last 17 years to optimize systems, so an operation can get lots of vehicles working together. One person can control 50 vehicles, and the Mobius technology keeps them from hitting each other and enables them to run constantly for 24 hours.

Projects that were never financially viable now become a possibility under an autonomous operation. The ASI system can also interface with vehicles that are not autonomous, which gives operators the ability to work in a mixed traffic environment.

Putting the robot in a vehicle opens the door for many industries to be more innovative and a lot safer. If you can dream it, there are people at ASI who can do it. To see the game-changing capability of ASI’s solutions in action, enjoy the video.








SALT LAKE CITY, JULY 13, 2017 – Autonomous Solutions, Inc. (ASI) has been awarded additional funding through the Department of Defense (DoD) to further apply machine learning and artificial intelligence to improve the mobility and behavior of autonomous vehicles in challenging environments.

Two other ongoing programs with the government include the development of machine learning for obstacle classification, LIDAR-camera fusion, and vehicle auto tuning using artificial intelligence. The developments will enable ASI’s autonomous ground vehicles to adapt in real time to difficult dynamic environments for automotive, agriculture, mining, construction, floor cleaning, security surveillance, and lawn mowing robots.

In order for us to maintain our leadership in the unmanned vehicle space we must continue to push the boundaries of what is possible with machine learning and artificial intelligence,” says CEO and founder of ASI, Mel Torrie. “These programs will ensure that we continue to offer our customers and partners the most advanced safe and simple autonomous solutions.”



The ability for vehicles to learn from past experience and continuously improve as they drive is important. These benefits multiply as the robots share their learning with other vehicles in the area in real time. The use of Machine Learning and AI will enable huge strides in efficiency improvement and maintenance reduction,” says Dr. Jeff Ferrin, Research and Development General Manager.

Mel Torrie will be sharing more about this exciting technology development at the Silicon Valley Innovation and Entrepreneurship Conference in Beijing China on 15 July 2017 and at the Silicon Valley-China AI & Investment Forum on 19 July 2017 in Santa Clara, California in an AI session alongside leading pioneers in this space from companies like Tesla, IBM, Facebook, and Samsung.










In the proving ground environment, car manufacturers engage in extensive durability and misuse testing.

They might jump a vehicle off a ledge, run it into a wall, or even run tests in which the vehicle rolls over—all to improve the safety of the vehicle for consumers.

The challenge is there is a clear limit to what you can test with a driver in the car. Though the ultimate buyer of the vehicle may misuse a car or truck in hundreds of imaginative ways, proving grounds need to keep the health and safety of their drivers in mind when they’re designing tests.



In fact, one of ASI’s customers recently had to shut down a ditch twist test the first day they started running it, after two test drivers endured neck injuries. Since then, they turned to ASI to automate their vehicle. They now run the test all the time, ultimately improving the safety of the vehicle itself.

Putting the robot in a vehicle opens the door for a proving ground to be very innovative in what kinds of tests they can write and run. To learn more about the capability of ASI’s proving ground solution, please enjoy the video.








For automakers, manufacturing vehicles that are safe is the number one priority. In order to evaluate the safety and reliability of cars and trucks, companies test their models on miles of tracks known as proving grounds.

Here, human drivers are often employed to subject automobiles to rigorous handling and varying conditions so engineers can assess the effects of different speeds, driving scenarios, and varying passenger loads.

But the human element of these durability and misuse tests comes with many disadvantages that, when removed, can optimize operations leading to faster results and direct cost savings.

Robotic Advantages

The first advantage of replacing a human driver with robotics programmed with artificial intelligence is it increases safety on the tracks. There’s no individual who can be harmed performing high-risk exercises, decreasing the liability of automakers.

There are government regulations regarding the use of humans behind the wheel on proving grounds which can slow testing; these rules don’t come into play with an autonomous solution.

Humans require breaks and there are stiff safety regulations that dictate only one to three hours behind the wheel every twenty four hour period in many cases. On the other hand, robots can perform for an extended time period only needing to stop to refuel. And one of the most notable benefits of robotics is a more successful rate of repeatability that an individual just can’t match.



A Multi-Vehicle Control Platform

ASI’s Mobius for proving grounds is a multi-vehicle command and control platform that allows a single operator to oversee the operation of an entire fleet of automobiles. To accomplish this, vehicles are fitted with ASI’s Vehicle Automation Kit which includes our VCU (vehicle control unit) that communicates with Mobius.

The platform allows for the creation of custom paths to fit the parameters of desired tests, as vehicles can be programmed to accelerate, decelerate or perform any number of actions at precise locations. By fitting a beacon on non-autonomous vehicles, Mobius will recognize and account for them so both traditional and driverless vehicles can occupy a track at the same time.

Proven Results on Proving Grounds

Proving grounds using ASI’s Mobius technology see dramatically improved efficiency. One customer reported that tests with human drivers took 12 days to complete as a result of higher test failure rate due to human error, the restrictions of heavy safety regulations, and natural human fatigue requiring breaks. These same tests took robot drivers just five days to complete and logged high test ratings due to superior repeatability.

Car companies deploying ASI’s Mobius multi-vehicle autonomous technology for proving grounds enjoy faster, more accurate tests at reduced costs without putting a human driver in harm’s way.




Competitors’ solutions don’t allow the flexibility that Mobius provides in creating custom paths and events, which is why Ford, Toyota, FCA and Hyundai all rely on Mobius and ASI for durability and misuse testing. To learn more about the power and capability of Mobius, visit ASIrobots.com today!