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

2012 Alabama Lunabotics Systems Engineering Paper

Excavation will hold a key role for future lunar missions. NASA has stated that "advances in lunar regolith mining have the potential to significantly contribute to our nation's space vision and NASA space exploration operations." [1]. The Lunabotics Mining Competition is an event hosted by NASA that is meant to encourage "the development of innovative lunar excavation concepts from universities which may result in clever ideas and solutions which could be applied to an actual lunar excavation device or payload." [2]. Teams entering the competition must "design and build a remote controlled or autonomous excavator, called a lunabot, that can collect and deposit a minimum of 10 kilograms of lunar simulant within 10 minutes." [2]. While excavation will play an important part in lunar missions, there will still be many other tasks that would benefit from robotic assistance. An excavator might not be as well suited for these tasks as other types of robots might be. For example a lightweight rover would do well with reconnaissance, and a mobile gripper arm would be fit for manipulation, while an excavator would be comparatively clumsy and slow in both cases. Even within the realm of excavation it would be beneficial to have different types of excavators for different tasks, as there are on Earth. The Alabama Lunabotics Team at the University of Alabama has made it their goal to not only design and build a robot that could compete in the Lunabotics Mining Competition, but would also be a multipurpose tool for future NASA missions. The 2010-2011 resulting robot was named the Modular Omnidirectional Lunar Excavator (MOLE). Using the Systems Engineering process and building off of two years of Lunabotics experience, the 20ll-2012 Alabama Lunabotics team (Team NASACAR) has improved the MOLE 1.0 design and optimized it for the 2012 Lunabotics Competition rules [I]. A CAD model of MOLE 2.0 can be seen below in Fig. 1

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Abstract There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p

Modelling and Remote Control of an Excavator

This paper reposts the results of an on-going project and investigates modelling and remote control issues of an industry excavator. The details of modelling, communication and control of a remotely controllable excavator are studied. The paper mainly focuses on trajectory tracking control of the excavator base and robust control of the excavator arm. These will provide the fundamental base for our next research step. In addition, extensive simulation results for trajectory tracking of the excavator base and robust control of the excavator arm are given. Finally, conclusions and further work have been identified

Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

The Montana ALE (Autonomous Lunar Excavator) Systems Engineering Report

On May 2 1-26, 20 12, the third annual NASA Lunabotics Mining Competition will be held at the Kennedy Space Center in Florida. This event brings together student teams from universities around the world to compete in an engineering challenge. Each team must design, build and operate a robotic excavator that can collect artificial lunar soil and deposit it at a target location. Montana State University, Bozeman, is one of the institutions selected to field a team this year. This paper will summarize the goals of MSU's lunar excavator project, known as the Autonomous Lunar Explorer (ALE), along with the engineering process that the MSU team is using to fulfill these goals, according to NASA's systems engineering guidelines

A stochastic method for representation, modelling and fusion of excavated material in mining

The ability to safely and economically extract raw materials such as iron ore from a greater number of remote, isolated and possibly dangerous locations will become more pressing over the coming decades as easily accessible deposits become depleted. An autonomous mining system has the potential to make the mining process more efficient, predictable and safe under these changing conditions. One of the key parts of the mining process is the estimation and tracking of bulk material through the mining production chain. Current state-of-the-art tracking and estimation systems use a deterministic representation for bulk material. This is problematic for wide-scale automation of mine processes as there is no measurement of the uncertainty in the estimates provided. A probabilistic representation is critical for autonomous systems to correctly interpret and fuse the available data in order to make the most informed decision given the available information without human intervention. This thesis investigates whether bulk material properties can be represented probabilistically through a mining production chain to provide statistically consistent estimates of the material at each stage of the production chain. Experiments and methods within this thesis focus on the load-haul-dump cycle. The development of a representation of bulk material using lumped masses is presented. A method for tracking and estimation of these lumped masses within the mining production chain using an 'Augmented State Kalman Filter' (ASKF) is developed. The method ensures that the fusion of new information at different stages will provide statistically consistent estimates of the lumped mass. There is a particular focus on the feasibility and practicality of implementing a solution on a production mine site given the current sensing technology available and how it can be adapted for use within the developed estimation system (with particular focus on remote sensing and volume estimation)