Earthmoving construction automation with military applications: Past, present and future

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Amongst increasing innovations in frontier engineering sciences, the advancements in Robotic and Autonomous Systems (RAS) has brought about a new horizon in construction applications. There is evidence of the increasing interest in RAS technologies in the civil construction sector being reflected in construction efforts of many military forces. In particular, Army or ground-based forces are frequently called upon to conduct construction tasks as part of military operations, tasks which could be partially or fully aided by the employment of RAS technologies. Along with recent advances in the Internet of Things (IoT) and cyber-physical system infrastructure, it is essential to examine the current maturity, technical feasibility, and affordability, as well as the challenges and future directions of the adoption and application of RAS to military construction. This paper presents a comprehensive survey and provides a contemporary and industry-independent overview on the state-of-the-art of earthmoving construction automation used in defence, spanning current world’s best practice through to that which is predicted over the coming years

TOPLEX: Teleoperated Lunar Explorer. Instruments and Operational Concepts for an Unmanned Lunar Rover

A Teleoperated Lunar Explorer, or TOPLEX, consisting of a lunar lander payload in which a small, instrument-carrying lunar surface rover is robotically landed and teleoperated from Earth to perform extended lunar geoscience and resource evaluation traverses is proposed. The rover vehicle would mass about 100 kg and carry approximately 100 kg of analytic instruments. Four instruments are envisioned: (1) a Laser-Induced Breakdown Spectrometer (LIBS) for geochemical analysis at ranges up to 100 m, capable of operating in three different modes; (2) a combined x-ray fluorescence and x-ray diffraction (XRF/XRD) instrument for elemental and mineralogic analysis of acquired samples; (3) a mass spectrometer system for stepwise heating analysis of gases released from acquired samples; and (4) a geophysical instrument package for subsurface mapping of structures such as lava tubes

Automatic estimation of excavator actual and relative cycle times in loading operations

This paper proposes a framework to automatically determine the productivity and operational effectiveness of an excavator. The method estimates the excavator\u27s actual, theoretical, and relative cycle times in the loading operation. Firstly, a supervised learning algorithm is proposed to recognize excavator activities using motion data obtained from four inertial measurement units (IMUs) installed on different moving parts of the machine. The classification algorithm is offline trained using a dataset collected via an excavator operated by two operators with different levels of competence in different operating conditions. Then, an approach is presented to estimate the cycle time based on the sequence of activities detected using the trained classification model. Since operating conditions can significantly influence the cycle time, the actual cycle time cannot solely reveal the machine\u27s performance. Hence, a benchmark or reference is required to analyze the actual cycle time. In the second step, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Furthermore, two schemes are presented to estimate the swing angle and digging depth based on the recognized excavator activities. In the third step, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. Finally, the results of the method are demonstrated by the implementation on two case studies which are operated by inexperienced and experienced operators. The obtained relative cycle time can effectively monitor the performance of an excavator in loading operations. The proposed method can be highly beneficial for worksite managers to monitor the performance of each machine in worksites

Continuous control of an underground loader using deep reinforcement learning

Reinforcement learning control of an underground loader is investigated in simulated environment, using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of the pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point, while avoiding collisions, getting stuck, or losing ground traction. It relies on motion and force sensors, as well as on camera and lidar. Using a soft actor-critic algorithm the agents learn policies for efficient bucket filling over many subsequent loading cycles, with clear ability to adapt to the changing environment. The best results, on average 75% of the max capacity, are obtained when including a penalty for energy usage in the reward.Comment: 9 pages, 7 figure

Lunar surface construction and assembly equipment study: Lunar Base Systems Study (LBSS) task 5.3

A set of construction and assembly tasks required on the lunar surface was developed, different concepts for equipment applicable to the tasks determined, and leading candidate systems identified for future conceptual design. Data on surface construction and assembly equipment systems are necessary to facilitate an integrated review of a complete lunar scenario

Construction machinery : 2007

Current industrial report. Contains annual data for construction machinery products produced in the United State

Construction machinery : 2000

Current industrial report. Contains annual data for construction machinery products produced in the United State

Construction machinery : 1998

Current industrial report. Contains annual data for construction machinery products produced in the United State