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 capability to rapidly augment airbases with bio-concrete infrastructure to support parking, loading, unloading, rearming, and refueling operations is of interest to the Air Force, because it requires transportation of fewer raw materials to remote sites. Automation of the bio-cement delivery further reduces logistical requirements and mitigates hazards to personnel, especially in contested or austere environments. In this paper we discuss the full-stack development and integration of a robotic applique for a commercial tractor and present the test results for autonomous delivery of bio-cement bacteria, feed stock, and water for stabilization of a sandy test area. The tractor autonomously navigates, sprays, and avoids obstacles using robust and economical off-the-shelf components and software. For this first phase of the project, we employ GNSS for localization and automotive lidar for obstacle detection. We report on the design of the robotic applique, including the mechanical, electrical, and software components, which are mostly commercial-off-the-shelf or open source. We discuss the results of testing and calibration including tests of towing capacity, calibration of steering, measurement of liquid spray distribution, measurement of tracking errors, and determination of repeatability of positioning for refilling of the reservoir. We also examine higher order behaviors and chart a path forward for future development, which includes GNSS-denied navigation.
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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