In this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given 3D blueprint. Constructing mounds around landing pads will provide physical shielding from debris during launch landing. Burying a human habitat modules under 0.5 m of lunar regolith is expected to provide both radiation shielding and maintain temperatures of -25 @math C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it doesn't require a team of astronauts for excavation and it requires minimal supervision. The robot teams are shown to autonomously interpret blueprints, excavate and prepare sites for a lunar base. Because little pre-programmed knowledge is provided, the controllers discover creative techniques. ANT evolves techniques such as slot-dozing that would otherwise require excavation experts. This is critical in making an excavation mission feasible when it is prohibitively expensive to send astronauts. The controllers evolve elaborate negotiation behaviors to work in close quarters. These and other techniques such as concurrent evolution of the controller and team size are shown to tackle problem of antagonism, when too many robots interfere reducing the overall efficiency or worse, resulting in gridlock. While many challenges remain with this technology our work shows a compelling pathway for field testing this approach.
These scripts are developed with input from an expert human operator and model vehicle specific limitations such as load handling capacity and latency. These systems incorporate adaptive coarse and refined planners to sequence digging operations @cite_44 . Other works used coarse and refined planner containing a neural network approach to learn soil conditions and excavator capabilities during operation @cite_34 . Such systems are comparable in efficiency to human operators. Other approaches are devoted to modeling kinematics and or dynamics of the excavation vehicles and simulating their performance @cite_12 . These techniques are designed for specific vehicle platforms and do not include scripts for every possible scenario in the task space thus requiring close monitoring and intervention by a human operator. This makes the approach unsuitable for fully autonomous operation of multiple robots on the moon.
@cite_44 @cite_34 @cite_12
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Planetary excavator robots face unique and extreme engineering constraints relative to terrestrial counterparts. In space missions mass is always at a premium because it is the main driver behind launch costs. Lightweight operation, due to low mass and reduced gravity, hinders excavation and mobility by reducing the forces a robot can effect on its environment. This work shows that there is a quantifiable, non-dimensional threshold that distinguishes the regimes of lightweight and nominal excavation. This threshold is crossed at lower weights for continuous excavators e.g. bucket-wheels than discrete scrapers. This research introduces novel experimentation that for the first time subjects excavators to gravity offload a cable pulls up on the robot with five-sixths its weight, to simulate lunar gravity while they dig. A 300 kg excavator robot offloaded to 1 6 g successfully collects 0.5 kg s using a bucket-wheel, with no discernible effect on mobility. For a discrete scraper of the same weight, production rapidly declines as rising excavation resistance stalls the robot. These experiments suggest caution in interpreting low-gravity performance predictions based solely on testing in Earth gravity. Experiments were conducted in GRC-1, a washed industrial silica-sand devoid of agglutinates and of the sub-75-micron basaltic fines that make up 40 of lunar regolith. The important dangers related to dust are thus not directly addressed. The achieved densities for experimentation are 1640 kg m3 very loose loose and 1720 kg m3 medium dense. This work develops a novel robotic bucket-wheel excavator, featuring unique direct transfer from bucket-wheel to dump bed as a solution to material transfer difficulties identified in past literature. Presents an approach for real time planning and execution of the motions of complicated robotic systems. The approach is motivated by the observation that a robot's task can be described as a series of simple steps, or a script. The script is a general template which encodes knowledge for a class of tasks and is fitted to a specific instance of a task. The script receives information about its environment in the form of parameters, which it uses to bind variables in the template and allows it to deal with the current task conditions. Changes or variations in the robot's environment can be easily handled with this parameterized script approach. New tasks for the robot to perform can be added in the form of subscripts, which could handle exceptional cases. We apply this approach to the task of autonomous excavation, and demonstrate its validity on an actual hydraulic excavator. We obtain good results, with the autonomous system approaching the performance of an expert human operator. The excavation of foundations, general earthworks and earth removal tasks are activities which involve the machine operator in a series of repetitive operations, suggesting opportunities for the automation through the introduction of robotic technologies with subsequent improvements in machine utilisation and throughput. The automation of the earth removal process is also likely to provide a number of other benefits such as a reduced dependence on operator skills and a lower operator work load, both of which might be expected to contribute to improvements in quality and, in particular, the removal of the need for a local operator when working in hazardous environments. The Lancaster University Computerised Intelligent Excavator or LUCIE has demonstrated the achievement of automated and robotic excavation through the implementation of an integrated, real-time, artificial intelligence based control system utilising a novel form of motion control strategy for movement of the excavator bucket through ground. Having its origins in the systematic observation of a range of machine operators of differing levels of expertise, the control strategy as evolved enables the autonomous excavation of a high quality rectangular trench in a wide variety of types and conditions of ground and the autonomous removal of obstacles such as boulders along the line of that trench. The paper considers the development of the LUCIE programme since its inception and sets out in terms of the machine kinematics the evolution and development of the real-time control strategy from an implementation on a one-fifth scale model of a back-hoe arm to a full working system on a JCB801 360° tracked excavator. We consider the case in which a robot has to navigate in an unknown environment but does not have enough on-board power or payload to carry a traditional depth sensor (e.g., a 3D lidar) and thus can only acquire a few (point-wise) depth measurements. We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements? Reconstruction from incomplete data is not possible in general, but when the robot operates in man-made environments, the depth exhibits some regularity (e.g., many planar surfaces with only a few edges); we leverage this regularity to infer depth from a small number of measurements. Our first contribution is a formulation of the depth reconstruction problem that bridges robot perception with the compressive sensing literature in signal processing. The second contribution includes a set of formal results that ascertain the exactness and stability of the depth reconstruction in 2D and 3D problems, and completely characterize the geometry of the profiles that we can reconstruct. Our third contribution is a set of practical algorithms for depth reconstruction: our formulation directly translates into algorithms for depth estimation based on convex programming. In real-world problems, these convex programs are very large and general-purpose solvers are relatively slow. For this reason, we discuss ad-hoc solvers that enable fast depth reconstruction in real problems. The last contribution is an extensive experimental evaluation in 2D and 3D problems, including Monte Carlo runs on simulated instances and testing on multiple real datasets. Empirical results confirm that the proposed approach ensures accurate depth reconstruction, outperforms interpolation-based strategies, and performs well even when the assumption of structured environment is violated.