NREC timeline

DARPA robotics challenge

CHIMP is a human-sized robot that, when standing, is 5-foot-2-inches tall and weighs about 400 pounds. Tartan Rescue Team engineers designed CHIMP to work in dangerous, degraded environments that were built for people, not robots. CHIMP operates semi-autonomously and can plan and carry out high-level instructions given by its operator. Its near-human form, strength, precision, and dexterity enable it to perform complex, human-level tasks. CHIMP is not a dynamically balanced walking robot. Instead, it is designed to move on stable, tank-like treads incorporated into its four limbs. When it needs to operate power tools, turn valves, or otherwise use its arms, CHIMP can stand and roll on its leg treads. The robot’s long front arms (almost 5 feet) give it an ape-like appearance.

CHIMP ranked third in the DARPA Robotics Challenge Trials in December 2013.^[9][11] Scoring 18 out of a possible 32 points during the two-day trials, the team demonstrated the system's ability to perform such tasks as removing debris, opening doors, cutting a hole in a wall, and closing a series of valves. The system was selected as one of nine eligible for DARPA funding to prepare for the DARPA Robotics Challenge Finals in 2015.^[10]

Urban challenge

Carnegie Mellon University's Tartan Racing Team^[6][who?] and General Motors built an autonomous SUV that won first place in the 2007 DARPA Urban Challenge.^[5] The Urban Challenge race was held on November 3, 2007, at the Victorville training facility in California. Eleven teams competed against each other to finish a 60-mile city course in less than six hours. Their vehicles had to execute simulated missions in a mock urban area while obeying traffic laws, safely merging into moving traffic, navigating traffic circles, negotiating busy intersections, and avoiding other vehicles – all without human intervention.

Automation and machine learning for agriculture

Vehicle safeguarding: Being able to detect obstacles and terrain hazards significantly increases the safety of both manned and unmanned agricultural vehicles. The project uses machine learning techniques to build a robust obstacle detection system that can be easily adapted to different environments and operating conditions. NREC integrated its add-on perception packages onto a team of three computer-controlled tractors developed by John Deere. These autonomous tractors were used in harvesting operations in a peat bog. The robotic peat harvesting team was tested for a full season, completing over 100 harvesting missions in a working peat bog. Their behavior imitated manual peat harvesting operations while maintaining a safe operating environment.

Strawberry Plant Sorter: Building upon expertise in vision, mechanisms, and manipulation, NREC built an automated strawberry plant sorter that streamlines the harvesting process, improves efficiency, and ensures consistent plant quality. The machine vision system is trained to sort strawberry plants using samples harvested by a human, sorting plants of different varieties and levels of maturity while operating under realistic conditions, where rain and frost change plants' appearance and roots may contain mud and debris. Lassen Canyon Nursery and other growers, representing approximately 85% of California's strawberry plant nursery market, supported this project and plan to use the technology in their operations.

Orchard Spraying: NREC developed a retrofit kit that allows a tractor to operate without a driver. Its software accurately estimated the vehicle’s location and enabled it to autonomously follow a predetermined path. The autonomous tractor sprayed water while following a seven-kilometer-long path through an orange orchard without any human intervention. 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.^{[clarification needed]}

Defense robotics for convoy safety

NREC and Oshkosh Defense are developing autonomous unmanned ground vehicle technologies for logistics tactical wheeled vehicles used by the US Marine Corps. CARGO Unmanned Ground Vehicles (CARGO UGVs or CUGVs) are designed for autonomous use in convoys that combine manned and unmanned vehicles. An operator in another vehicle supervises one or more unmanned vehicles, which drive autonomously in convoy formation day and night, in all weather, and when dust and smoke limit visibility.

Technologies developed under this project are part of Oshkosh Defense’s TerraMax™ UGV kit, which supports unmanned convoy operations.^[12]

Sensabot

Sensabot is a rugged robot designed to safely carry out on-site inspections in hazardous environments, isolated facilities, and other places that are difficult or dangerous for personnel to access. Benefits include reduced risk and improved efficiency of operation.

The system consists of a mobile robotic base equipped with a sensor boom that is fitted with inspection sensors. It is capable of operating in extreme temperatures, as well as explosive and toxic atmospheres. The robot is remotely controlled by a human operator who utilizes the sensors to conduct inspections on pipes, fittings, and valves. Sensabot has been designed to conform to the IECEx Zone 1 standards for explosive environments, as well as the ANSI safety standards for guided industrial vehicles.^[13]

Advanced Robotic Laser Coating Removal System (ARLCRS)

The Air Force Research Laboratory (AFRL), Concurrent Technologies Corporation (CTC), and NREC are developing an environmentally friendly system to remove coatings from U.S. Air Force aircraft through funding from Air Force Materiel Command (AFMC).

The Advanced Robotic Laser Coating Removal System (ARLCRS) uses a powerful laser stripping tool and state-of-the-art mobile robots to automatically remove paint and coatings from aircraft. The complete system is scalable for use from fighters to cargo and tanker aircraft. ARLCRS will reduce hazardous waste, air emissions, maintenance costs, and processing time. CTC is developing the laser coatings removal and particle capture systems. NREC is developing the mobile robots, sensors, and autonomy system.

Operator assistance for underground coal mining

NREC has worked with coal mining industry partners to develop operator assistance technology for longwall mining. This includes a complete navigation system for a continuous mining machine, laser rangefinder-based perception for robot localization, planning for cluttered spaces, and integration and simulation tools. This system was successfully demonstrated in a working mine in West Virginia.^{[citation needed]} Related research and objectives include automated mine surveying, haulage, and multiple-machine interaction.

ARMOR 1: mat sinking system

ARMOR 1 is an automated robotic system for the U.S. Army Corps of Engineers to perform revetment operations along the Mississippi River. Once deployed, ARMOR 1 will replace the old Mat Sinking Unit, originally built in 1948. The goal is to increase the speed of revetment operations and improve the safety and working conditions of the employees who perform this vital work.

When completed, ARMOR 1 will include six, independent robotic cranes. These cranes will pick up the large concrete squares from the supply barge and place them on the "mat deck" of ARMOR 1's manufacturing barge. There, the individual squares will be tied together into one 140 ft wide (and up to 900 ft long) flexible "mat" by an automated tie system. The completed mat will be launched from the barge and will be submerged along the banks of the Mississippi River, while more mats are continuously being assembled on the deck.