KEYWORDS: Sensors, Navigation systems, Safety, LIDAR, Operating systems, Open source software, Motion models, Monte Carlo methods, Robotics, Global Positioning System
Pavements used for construction and repair of airfield surfaces must be rigorously tested before use in the field. This testing is typically accomplished by trafficking simulated aircraft landing gear payloads across an experimental pavement test patch and measuring deflection, cracking, and other effects on the pavement and aggregate subbase. The landing gear payload is heavily weighted to simulate the pressures of landing and taxiing, and a large tractor pulls the landing gear repeatedly over the test patch while executing an intricate trafficking pattern to distribute the load on the patch in the desired manner. In conventional testing, a human drives the test vehicle, called a load cart, forward and backward over the experimental patch up to about 1000 times while carefully following a set of closely spaced lane markings. This is a dull job that is ripe for automation. One year ago, at this conference, we presented results of kitting the load cart, consisting of the tractor from a Caterpillar 621G scraper and a custom trailer carrying the landing gear simulacrum, with a custom vehicle interface and bringing it under tele-operation. In this paper, we describe the results of fully automating the load cart pavements test vehicle using the Robot Operating System 2 Navigation Stack. The solutions works without GPS, line following, or external tracking systems and involves minimal modifications to the vehicle. Using lidar and Adaptive Monte Carlo localization, the team achieved better than 6" cross-track accuracy with a lumbering, 300,000-pound vehicle.
The Air Force Civil Engineer Center’s C-17 Load Cart is a large, 150-ton machine based on a modified Caterpillar 621G scraper for testing experimental pavements used in airfield surface construction and repair. Long lasting, durable, preparein- place, minimally resourced pavements represent a critical technology for airfield damage repair, especially in expeditionary settings, and formulations must be tested using realistic loads but without the expense and logistical challenges of using real aircraft. The Load Cart is an articulated vehicle consisting of the 621G tractor and a custom trailer carrying a weighted set of landing gear to simulate the loads exerted during aircraft landing and taxiing. During the test a human driver repetitively traffics the vehicle hundreds of times over an experimental patch of pavement, following an intricate trafficking pattern, to evaluate wear and mechanical properties of the pavement formulation. The job of driving the Load Cart is dull, repetitive, and prone to errors and systematic variation depending on the individual driver. This paper describes the full-stack development of an autonomy kit for the Load Cart, to enable repeatable testing without a driver. Open-source code (Robot Operating System), commercial-off-the-shelf sensors, and a modular design based on open standards are exploited to achieve autonomous operation without the use of GNSS (which is challenged by operation inside a metal test building). The Vehicle Control Unit is a custom interface in PC-104 form factor allowing actuation of the Load Cart via CAN J1939. Operational modes include manual, tele-operation, and autonomous.
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