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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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. We present a composite forward model of the mechanics of an excavator backhoe digging in soil. This model is used to predict the trajectories developed by a closed-loop force based control scheme given initial conditions, some of which can be controlled (excavator control parameters), some directly measured (shape of the terrain), and some estimated (soil properties). Since soil conditions can vary significantly, it is necessary that soil properties be estimated online. Our models are used to both estimate soil properties and predict contact forces between the excavator and the terrain. In a large set of experiments we have conducted, we find that these models are accurate to within approximately 20 and run about 10 times faster than real-time. In this paper we motivate the development of these models and discuss experimental data from our testbed. This paper describes automation of the digging cycle of a mining rope shovel which considers autonomous dipper (bucket) filling and determining methods to detect when to disengage the dipper from the bank. Novel techniques to overcome dipper stall and the online estimation of dipper “fullness” are described with in-field experimental results of laser DTM generation, machine automation and digging using a 1 7th scale model rope shovel presented.