APPLICATION
The Problem
Navigating complex terrain at speed and with minimal human supervision has always been a major challenge for UGVs. The need to recognize obstacles requires a dramatic improvement in perception capability. Also, the continued likelihood of running into obstacles requires a vehicle that is rugged enough to continue operating after sustaining tolerable damage in collisions.

The Solution
Building on successful results from PerceptOR, the UPI program’s perception and automation systems are being extended to improve automation capabilities at higher speeds.
As part of UPI, NREC designed a new vehicle, Crusher, which features a new highly durable hull, increased travel suspensions, and leverage off many development and improvements from the Spinner vehicle.
Enhanced perception capabilities include new "learning” technologies that enable the vehicle to learn from terrain data. It can then navigate new, highly varied terrains with increasing levels of autonomy. The team is also applying machine learning techniques to improve Crusher ’s localization estimate in the absence of GPS.
Payload development, integration and testing are scheduled to continue through 2008. UPI will bring together technologies and people to produce autonomous vehicle platforms capable of conducting missions with minimal intervention.

DESCRIPTION
With the addition of two new vehicles, the program will be able to conduct three parallel field testing agendas in varied terrain sites:

• Building on successful results from PerceptOR, the perception and automation systems will be extended to provide improved automation capabilities at higher speeds. Increased focus will be placed on the use of prior overhead data as well as learning technologies which will allow the vehicle to move effectively through previously untraveled terrains. Technologies that can supervise or support off-road navigation in highly varying terrain will be a priority.
• Vehicle field testing will continue, allowing for continuous improvement of obstacle capability, resilience, endurance, and payload fraction (key goals for UGVs). Vehicle performance will be analyzed, modified, and tested continuously to maximize the inherent terrainability of the Crusher platform.
• Payload development, integration and testing will prove out the intended mission scenarios. Leveraging upon unmanned vehicle capabilities and Crusner's unique terrain capabilities, these field tests will help demonstrate and influence the use of autonomous vehicles in the future.

APPLICATION
The 60-mile Urban Challenge course wound through an urban area with crowded streets, buildings, traffic, road signs, lane markers, and stop lights. The exact route was unknown until the morning of the race. Each vehicle attempted to complete a series of three missions within a six-hour time limit. No human intervention whatsoever was allowed during the race. Each vehicle used its on-board sensing and reasoning capabilities to drive safely in traffic, plan routes through busy streets, negotiate intersections and traffic circles, obey speed limits and other traffic laws, and avoid stationary and moving obstacles – including other Urban Challenge competitors.
Tartan Racing entered the Urban Challenge to bring intelligent autonomous driving from the pages of science fiction to the streets of your town. The technologies developed for this race will lay the groundwork for safer, more efficient, and more accessible transportation for everyone.
An aging population and infrastructure and rising traffic volumes put motorists at risk. Without technological innovation, auto accidents will become the third-leading cause of death by 2020. Integrated, autonomous driver assistance systems and related safety technologies will prevent accidents and injuries and save lives. They will also help people retain their freedom of movement and independence as they grow older.
Autonomous driving technologies can also be used to improve workplace safety and productivity. Assistance systems for heavy machinery and trucks will allow them to operate more efficiently and with less risk to drivers and bystanders.

DESCRIPTION
Tartan Racing took a multi-pronged approach to the daunting challenge of navigating the dynamic environment of a city:

• Organize and orchestrate concurrent software components to determine sequences of tasks, process sensor data, and control the vehicle. Response times had to be less than a second! Other software components continuously monitored the status of individual tasks to find out whether they were
  successfully completed.
• Sense, differentiate and localize objects (such as buildings and cars) and environmental features (such as lane markings, curbs, and sidewalks), both fixed and moving. The vehicle used radar, ladar, and video sensors to perceive its environment and GPS and IMUs to establish its position.
• Control actuators to ensure safe and efficient driving in intersections, parking lots, traffic circles, and similar city features.
• Plan and replan the most efficient routes through a network of streets, taking into account constantly-changing conditions.
• Retrofit two stock Chevy Tahoe SUVs to enable computer control of their steering, speed and gears.
• Achieve robustness by applying well-known principles of systems engineering and testing, using both simulation and live tests. The goal was to make the vehicles as reliable as possible for the race.

NREC faculty and staff took on key leadership roles in conquering these technical challenges.

APPLICATION
The Problem
Until the scientists and engineers at NREC developed the AMTS solution, companies had limited options to relying on driver-operated forklifts and tug vehicles for transporting materials and stacking pallets in factories and warehouses.
Current automated guided vehicles (AGVs) are limited by their inability to "see” their surroundings. And, in order to function at all, they required complex setup and costly changes to facility infrastructure. For example, companies using conventional AGVs have to install special sensors, jigs and attachments for an automated forklift to pick up a pallet of materials.

The Solution
NREC scientists and engineers devised a computer vision system that can be used with any mobile robot application. Today’s cost-effective AMTS solution works effectively around the clock, with lights out in many cases and with less damage to vehicles than humans cause. There is typically no need to retrofit the facility infrastructure to accommodate the AGVs. These AMTS-equipped automated vehicles — robotic forklifts and tugs — find their way around by virtue of a low-cost, high-speed positioning system developed at NREC.
NREC equips each vehicle with a combination of cameras and laser rangefinders for navigation and control. With a downward-looking camera mounted to the bottom of the forklift, the robot captures visual cues and matches them to a pre-stored database of floor imagery that becomes its map for navigating the floor.
Using a forward-looking camera system, the forklift images the side of the trailer to find pallets for transfer to tug vehicle wagons. The forks are inserted into the pallet holes, and the forklift lifts the pallet. While it is backing out of the trailer, the robotic forklift relies on its laser rangefinder to safety remove the tight-fitting pallet from the trailer. The robotic tug vehicle uses the same downward vision technology to move around and position its wagons for loading and unloading.

DESCRIPTION
NREC scientists and engineers developed four novel vision systems and associated visual servoing control systems, as well as factory-level vehicle traffic coordination software.
Development of the AMTS solution began with prototypes of both the position-estimation technology and pallet-acquisition vision system. After integrating NASA technology into these systems, NREC provided a demonstration of simplified, automated trailer loading/unloading and automated pallet stacking.
Subsequently, during an AGV pilot program at an automotive assembly plant, several tug AGVs used the AMTS downward vision technology and proved its viability.

Today, the AMTS is available as a pragmatic solution for efficient, cost-effective materials transport in manufacturing facilities, industrial plants and storage warehouses. Because it requires no changes to facility infrastructure, it makes automated materials handling more practical and affordable than ever before.

APPLICATION
Black Knight can be used day or night for missions that are too risky for a manned ground vehicle (including forward scouting, reconnaissance surveillance and target acquisition, (RSTA), intelligence gathering, and investigating hazardous areas) and can be integrated with existing manned and unmanned systems. It enables operators to acquire situational data from unmanned forward positions and verify mission plans by using map data to confirm terrain assumptions.
Black Knight demonstrates the advanced capabilities that are available to unmanned ground combat vehicles (UGCVs) using current technology. Its 300 hp diesel engine gives it the power to reach speeds of up to 48 mph, with off-road autonomous and teleoperation speeds up to 15 mph. Its band-tracked drive makes it highly mobile in extreme off-road terrain while reducing its acoustic and thermal signatures. The 12-ton Black Knight can be transported within a C-130 cargo plane and makes extensive use of components from the Bradley Combat Systems program to reduce costs and simplify maintenance.
Black Knight can be teleoperated from within another vehicle (for example, from the commander's station of a Bradley Fighting Vehicle) or by dismounted Soldiers. Its Robotic Operator Control Station (ROCS) provides an easy-to-use interface for teleoperating the vehicle. Black Knight's autonomous and semi-autonomous capabilities help its operators to plan efficient paths, avoid obstacles and terrain hazards, and navigate from waypoint to waypoint. Assisted teleoperation combines human driving with autonomous safeguarding.
Black Knight was extensively tested both off-road and on-road in the Air Assault Expeditionary Force (AAEF) Spiral D field exercises in 2007, where it successfully performed forward observation missions and other tasks. Black Knight gave Soldiers a major advantage during both day and night operations. The vehicle did not miss a single day of operation in over 200 hours of constant usage.

DESCRIPTION
NREC developed Black Knight's vehicle controller, tele-operation, perception and safety systems.
Black Knight's perception and control module includes Laser Radar (LADAR), high-sensitivity stereo video cameras, FLIR thermal imaging camera, and GPS. With its wireless data link, the sensor suite supports both fully-autonomous and assisted (or semi-autonomous) driving.
Black Knight's autonomous navigation features include fully-automated route planning and mission planning capabilities. It can plan routes between waypoints – either direct, straight-line paths or paths with the lowest terrain cost (that is, the lowest risk to the vehicle). Black Knight's perception system fuses LADAR range data and camera images to detect both positive and negative obstacles in its surroundings, enabling its autonomous navigation system to avoid them.
These autonomy capabilities can also assist Black Knight's driver during teleoperation. Black Knight can plan paths to be manually driven by its operator. In “guarded teleoperation” mode, objects that are detected by the perception system are overlaid on the driving map, enabling drivers to maneuver around them. The vehicle also stops when it detects lethal obstacles in its path. Black Knight is driven from the Robotic Operator Control Station (ROCS), located within another vehicle. It can also be driven off-board via its safety controller. The ROCS displays images from the vehicle's color and FLIR driving cameras and includes a hand controller for steering the vehicle and operating its sensors. It also allows the driver to control and view the status of the various vehicle and sensor systems. Map and route displays help the driver to navigate through unfamiliar terrain.
The ROCS also allows operators to control the Commander's Independent View (CIV) sensor suite. The CIV is used for remote surveillance and target acquisition (RSTA) and includes color video and FLIR cameras.

APPLICATION
The Problem
Surface mining of metals, quarrying of rock, and construction of highways requires the efficient removal of massive quantities of soil, ore, and rock. Human-operated excavators load the material into trucks. Each truckload typically requires several passes, each of which in turn takes 15–20 seconds. The operator’s performance peaks early in the work shift but wears down with fatigue. Scheduled idle times, such as lunch and other breaks, also diminish production across a shift.
Safety is another important consideration. Excavator operators are most likely to be injured when mounting or dismounting the machine. Operators tend to focus on the task at hand and may fail to notice other site personnel or equipment entering the loading zone.

The Solution
Automating the excavation and loading process would increase productivity and improve safety by removing the operator from the machine and by providing complete sensor coverage to watch for potential hazards entering the work area.
Recognizing this opportunity, NREC scientists and engineers developed a system that completely automates the truck loading process.

DESCRIPTION
In designing the ALS and conducting experimental trials, the ALS team used a combination of hardware, software and algorithms for perception, planning and control.
The ALS hardware subsystem consists of the servo-controlled excavator, on-board computing system, perception sensors and associated electronics. During development of the system, the NREC team developed a laser-based scanning system that would be able to penetrate a reasonable amount of dust and smoke in the air. Additionally, the team developed two different time-of-flight scanning ladar systems that are impervious to ambient dust conditions.
The NREC team designed the software subsystem with several modules to process sensor data, recognize the truck, select digging and dumping locations, move the excavator’s joints, and guard against collision.
Planning and control algorithms decide how to work the dig face, deposit material in the truck, and move the bucket between the two. Perception algorithms process the sensor data and provide information about the work environment to the system’s planning algorithms.
Expert operator knowledge was encoded into templates called scripts, which were adjusted using simple kinematic and dynamic rules to generate very fast machine motions. The system was fully implemented and demonstrated on a 25-ton hydraulic excavator and succeeded in loading trucks at about 80% of the speed of an expert human operator.

APPLICATION
The goal of the LAGR program is to develop a new generation of learned perception and control algorithms that will address the shortcomings of current robotic ground vehicle autonomous navigation systems through an emphasis on learned autonomous navigation. DARPA wanted the ten independent research teams they selected to immediately focus on algorithm development rather than be consumed early in the project with getting a baseline robotic platform working. DARPA also wanted a common platform so that software could be easily shared between teams and so that the government could make an objective evaluation of team results.
In just seven months, NREC designed and then built 12 LAGR robots which allowed DARPA to hold the LAGR kick-off meeting on time and to provide each research team with a fully functional autonomous platform for development.
Teams were given 4 hours of training at kick-off and were able to program basic obstacle avoidance capability the same day. Developers were able to focus immediately on learning algorithm research because all basic autonomy capabilities along with well documented APIs were provided with delivery.
Careful configuration control for all platforms enables developers to develop software at their home station, load their software on a memory stick, and ship the memory stick to DARPA, which then runs the software on their LAGR robot.

DESCRIPTION
The LAGR robot includes three 2.0 GHz Pentium-M computers, stereo cameras, IR rangefinders, GPS, IMU, encoders, wireless communications link, and operator control unit. NREC ported its PerceptOR software to the platform to provide baseline autonomous capability.
Communications tools include Gigabit Ethernet for on-board communication; a wireless (802.11b) Ethernet communications link; remote monitoring software that can be run on a laptop; and a standalone radio frequency remote.
The user can log data on the robot in three different modes: teleoperation using the RF remote; teleoperation from the onboard computer system (OCS); and during autonomous operation.
With each robot, NREC ships a comprehensive user manual that documents robot capability, baseline autonomy software, and APIs (with examples) that enable developers to easily interface robot sensor data to their perception and planning algorithms.

APPLICATION
The Problem
Crop spraying is inherently hazardous for the operators that drive spraying equipment. Removing the driver from the machine would lead to increased safety and reductions in health insurance costs. Furthermore, if a system can support nighttime operations less chemical needs to be sprayed for the same effect, due to increased bug activity. This results in higher quality crops and reduced spraying expenses.

The Solution
The NREC developed an unmanned tractor that can be used for several agricultural operations, including spraying. The system uses a GPS receiver, wheel encoders, a ground speed radar unit and an inertial measurement unit (IMU) in order to precisely record and track a path through a field or an orchard. The NREC team mounted two color cameras on the vehicle, to enable the use of color and range based obstacle detection.
The teach/playback system was tested in a Florida orange grove, and it sprayed autonomously while following a path of 7km at speeds ranging between 5 and 8 km/h.

DESCRIPTION
The initial focus of the project was the design of the retrofit kit for converting the 6410 tractor to an autonomous vehicle. One of the key requirements was that after the retrofit the vehicle is still drivable by a human like a normal tractor, in order to facilitate the path recording process. Since the vehicle was not drive-by-wire, the NREC developed actuators for braking, steering and speed control.
To achieve the path teach/playback capability, NREC developed a positioning system that uses an extended Kalman Filter for fusing the odometry, the GPS information and the IMU measurements. The path following system is based on the Pure Pursuit algorithm. More information about the performance of the system can be found in our "Autonomous Robots” paper.

APPLICATION
Most constructive and virtual simulations have very simplified representations of robotic systems, particularly with respect to mobility, target acquisition, interaction, and collaboration. Military simulation planning algorithms often treat robotic vehicles as manned entities with reduced speeds and sensing capabilities. Models seldom incorporate representations for such aspects as autonomous planning, perception, and coordination. Scenarios for examining future applications of ground and air robotic systems tend to focus on manned system missions, with minimal development of uniquely robotic capabilities.
By connecting field-proven NREC robotics technology directly to the simulators, analysts can get higher fidelity simulations of robotic system behavior. This allows for better understanding of the utility and best directions for improvement of those systems. And by closing the loop between robotic system developers like NREC and the users of those systems much faster than ever before, enhancements can be made earlier in the development cycle, and therefore at a lower cost.

DESCRIPTION
In the project’s first phase, NREC developed a highly reusable Robotic Simulation Support module to interface the Field D* planner with the Janus Force on Force simulator. Because the base resolution of the Janus terrain was lower than is necessary to accurately simulate robotics behavior, we used a Fractal Terrain Generator to add the appropriate roughness for each terrain type.
To ensure the additions produced by the generator accurately reflected the true difficulty of the terrain; we also developed an easy-to-use GUI-based tool which allows the RAND analysts to adjust the Terrain Generator’s input parameters, ensuring the validity of the simulation. Following development, successful integration tests on relevant simulation scenarios were run at RAND’s facilities.
In the second phase, NREC adapted the software module to connect to the JCATS simulator as well. Again, NREC and RAND successfully tested the integration on relevant simulation scenarios. NREC also began designing a system to bring new cooperative robotic behaviors to the simulator.
Currently, we are working to develop and integrate those behaviors with RAND’s simulators.

APPLICATION
The Problem
Today’s unmanned ground vehicles (UGVs) require constant human oversight and extensive communications resources particularly when traversing complex, cross country terrains. UGVs cannot support tactical military operations in a large scale way until they are able to navigate safely on their own and without constant human supervision. Off all classes of obstacles, UGVs are particularly vulnerable to "negative obstacles" like a hole or a ditch, which are difficult for a ground vehicle to sense due to the limited range and height of on-board sensors.

The Solution
The NREC-led team developed an innovative PerceptOR "Blitz” concept — an integrated air/ground vehicle system that incorporates significant autonomous perception, reasoning and planning for unmanned ground vehicles.
The autonomous UGV included LADAR, three stereo camera pairs, intra- and inter-vehicle sensor fusion, terrain classification, obstacle avoidance, waypoint navigation and dynamic path planning. The unmanned air vehicle — the Flying Eye — views the terrain from above, an optimal vantage point for detecting obstacles such as ruts, ditches and cul de sacs.
The team successfully demonstrated the UGV and Flying Eye working collaboratively to improve navigation performance. The UGV planned its initial route based on all available data and transmitted the route to the Flying Eye. The Flying Eye flew toward a point on this route ahead of the UGV. As the Flying Eye maneuvered, its downward looking sensor detects obstacles on the ground. The location of these obstacles was transmitted back to the UGV in relation to the UGV’s position. The UGV replans its intended path to avoid the obstacles and directs the Flying Eye to scout the new path.
The improved obstacle sensing capabilities (due to dual, well-separated views) and the optimized route planning (enabled by the Flying Eye's reconnaissance) increase the UGV’s autonomous speed by decreasing the risk of the vehicle being disabled or trapped, and by reducing the need for operator intervention and communications system bandwidth.

DESCRIPTION
Working in collaboration with its subcontractors, NREC developed the PerceptOR Blitz solution in a three-phase program.
In Phase I, the team developed a vehicle perception system prototype that included three sensing modalities, sensor fusion, terrain classification software, waypoint navigation and path planning software. A commercial ATV, retrofitted for computer control, served as the perception system platform.
In Phase II, the team validated the PerceptOR prototype on unrehearsed courses at test sites spanning four distinctly different types of terrain: sparse woods in Virginia; desert scrub with washes, gullies and ledges in Arizona; mountain slopes with pine forests in California; and dense woods with tall grass and other vegetation in Louisiana. During the test runs, the team demonstrated fully integrated unmanned, air/ground sensing that was used to detect and avoid negative obstacles and other hazards. They also negotiated complex terrain using only passive sensing. Additionally, they classified difficult terrain types (ground cover, meter-high vegetation, desert scrub) by fusing geometric and color sensor data.
In Phase III, the NREC team continues to improve the performance and reliability of the perception system with additional development and field trials. The team advanced UGV autonomous capabilities for operating in sub-optimal conditions, such as with obscurants (dust, smoke or rain), degraded GPS coverage, and reduced communications bandwidth.

APPLICATION
The Problem
Golf courses require constant maintenance and routinely bear high labor costs for teams of semi-skilled operators to mow fairways, frequently at peak golfing times. Mower operators must avoid golfers, maintain a neat appearance, combat fatigue and operate the mower safely.

The Solution
NREC’s autonomous mower system meets these demands by providing a system that requires a minimal amount of supervision that can be operated at night and during other off-peak hours.
The autonomous mower has a highly reliable obstacle detection and localization system. NREC developed an obstacle detection system that includes a sweeping laser rangefinder, which builds a 3D map of the area in front of the mower. It "learns” and uses this map to detect obstacles along the way. The robotic mower’s localization system combines GPS and inertial data to provide a position estimate that is accurate and robust.

DESCRIPTION
To achieve complete automation on golf courses and sports fields, NREC scientists and engineers developed capabilities for reliable obstacle detection, precise navigation and effective coverage.

Reliable obstacle detection:

• NREC designed the system so that it recognizes true obstacles, as small as a golf ball, while not generating false positives, to keep the vehicle safe.
• NREC engineers continue to refine the system so that it will discriminate true obstacles from tall grass.

Precise navigation:

• NREC’s autonomous mower operates with centimeter-level precision to create the cross-hatch patterns seen on premier golf courses.
• NREC robotics engineers continue to refine the system to improve its reliability in areas of minimal GPS coverage.

Effective coverage:

• NREC designed the robotic mower to follow patterns that enable it cover the entire fairway efficiently.
• NREC's software engineers continue to refine the system's interface for enhanced ease-of-use.

• A "cruise control” feature to automatically steer, drive and control the harvesting header, thereby allowing allow the operator to focus on other in-cab controls and harvest conditions.
• A GPS-based "teach/playback” system to enable the harvester to "learn” a field and then repeat a given path, thereby allowing one operator to remotely control several harvesters.
• A fully autonomous harvester using vision perception to completely harvest a field with no human supervision.

APPLICATION
The Problem
Farmers struggle constantly to keep costs down and productivity up. Mechanical harvesters and many other agricultural machines require expert drivers to work effectively. Labor costs and operator fatigue, however, increase expenses and limit the productivity of these machines.

The Solution
In partnership with project sponsors NASA and New Holland, Inc., NREC built the a robotic harvester, which harvests crops to an accuracy of 10cm by using a combination of a software-based teach/playback system and GPS-based satellite positioning techniques. Capable of operating day and night, the robot can harvest crops consistently at speeds and quality exceeding what a human operator can maintain.
Tangible results from extensive field tests conducted in El Centro, California demonstrated that an automated harvester would increase efficiency; reduce cost and produce better crop yield with less effort.

DESCRIPTION
For robot positioning and navigation, NREC implemented a differential GPS-based teach/playback system. Differential GPS involves the cooperation of two receivers, one that's stationary and another that's roving around making position measurements. The stationary receiver is the key. It ties all the satellite measurements into a solid local reference.
With the teach/playback system, the Windrower "learns" the field it is cutting, store the path in memory and then is programmed to repeat the path on its own.
Early in the project, the NREC team used color segmentation to determine the cut line for machine servoing. The approach differentiates the percentage of green representing the standing crop and the brown stubble of the cut crop. The system’s computer scans the cut line to determine machine direction. The Windrower is guided at speeds of 4–8 mph to about a 3-inch variance from this crop line.
Other guidance and safety instruments include an inclinometer to protect the machine from rollover and tipover and a gyroscope for redundant guidance.

APPLICATION
Peat moss is commonly used in gardening and plant growing. It is accumulated, partially decayed plant material that is found in bogs. An active peat bog is divided into smaller, rectangular fields that are surrounded by drainage ditches on three sides. When the top layer of peat is dry, the fields are ready to be harvested. Harvesting is done daily, weather permitting.
Peat is harvested with tractor-pulled vacuum harvesters. The vacuum harvesters suck up the top layer of dry peat as they’re pulled across a peat field. When the harvester is full, its operator dumps the harvested peat onto storage piles. The stored peat is later hauled away to be processed and packaged.

Peat moss harvesting is a good candidate for automation for several reasons:

• Peat fields have a well-defined, structured environment.
• Peat bogs are largely free of obstacles and vegetation.
• The manual harvesting process lends itself well to automation.
• Peat bogs are located in remote areas, where there are often shortages of qualified operators. This provides an incentive to automate the harvesting process.

DESCRIPTION
NREC’s add-on perception system performs three tasks that are important for safe autonomous operation.

Detecting Peat Storage Piles
Before it can dump the harvested peat onto a storage pile, the robot needs to find the edge of the pile. However, it cannot rely on GPS because the storage piles change shape, size and location as harvested peat is added to them. To locate the edges of storage piles, the perception system finds contiguous areas of high slope in the sensed 3D ground surface. A probabilistic spatial model of the ground surface generates smoothed estimates of ground height and handles sensor noise.

Detecting Obstacles
Although peat fields are generally free of obstacles, the harvesters must detect the presence of obstacles such as people, other harvesters, and other vehicles) to ensure safe unmanned operation. To detect different types of objects, the perception system uses a combination of algorithms that make use of 3D ladar data to find dense regions, tall objects, and hot regions above the ground surface.

Detecting Ditches
Ditch locations are mapped with GPS. However, as an added safety precaution, they are also detected by the perception system. The perception system searches for ditch shapes in the smoothed estimate of ground height.

APPLICATION
RVCA provides the following benefits to FCS:

• Verifies how ANS functions on a UGV platform
• Assesses UGV control with representative FCS hardware and software components operating within FCS network constraints
• Provides feedback that influences the development of FCS Battle Command software
• Reduces force integration risk for FCS
• Expedites the delivery of unmanned systems to soldiers in the field

DESCRIPTION
RVCA consists of the following:

• A UGV platform with integrated ANS hardware and software. Currently, RVCA is using the rugged, highly-mobile Crusher UGV platform. Later in the program, RVCA technology will be integrated onto the APD platform.
• Integrated System of System Common Operating Environment (SOSCOE) in both manned and unmanned vehicle platforms Integrated Vehicle Management System (VMS) and Integrated Computer System (ICS) to control a UGV
• Supporting data for operation of a UGV using these components in a networked environment

Engineering evaluations in the field focus on capabilities such as waypoint following, teleoperation, the system’s overall performance with ANS and other software components, and its use by soldiers in the field. The program concludes in 2010 with a Soldier Operational Experiment.