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.
Other techniques closely mimic insect in there ability to build road ways and ramps using amorphous construction. A laboratory robot is used to heat, melt and deposit foam to produce a ramp and other complex structures @cite_31 . More recent work by @cite_42 have performed simulations of multiple robots to perform excavation on Mars. The intent is to setup a permanent human base and utilize Martian resources for construction and in-situ resource utilization. The work has focused on human assisted high level planning required to locate a base and key facilities and the process of resource extraction. Human assistance is utilized in planning the high level tasks and giving execution orders to the multiple robots. It is presumed human astronauts are already located on Mars and can perform tele-operation on site (from a safe distance). Recent work by @cite_26 have show bucket wheels to be the most effective excavation platform for low gravity on the Moon. As will be shown later, our results also show bucket wheels to be most efficient for excavation.
@cite_31 @cite_42 @cite_26
2344081731 2530835184 2571268510 2136213179
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. Planetary exploration scenarios illustrate the need for autonomous robots that are capable to operate in unknown environments without direct human interaction. At the DARPA Robotics Challenge, we demonstrated that our Centaur-like mobile manipulation robot Momaro can solve complex tasks when teleoperated. Motivated by the DLR SpaceBot Cup 2015, where robots should explore a Mars-like environment, find and transport objects, take a soil sample, and perform assembly tasks, we developed autonomous capabilities for Momaro. Our robot perceives and maps previously unknown, uneven terrain using a 3D laser scanner. Based on the generated height map, we assess drivability, plan navigation paths, and execute them using the omnidirectional drive. Using its four legs, the robot adapts to the slope of the terrain. Momaro perceives objects with cameras, estimates their pose, and manipulates them with its two arms autonomously. For specifying missions, monitoring mission progress, on-the-fly reconfiguration, and teleoperation, we developed a ground station with suitable operator interfaces. To handle network communication interruptions and latencies between robot and ground station, we implemented a robust network layer for the ROS middleware. With the developed system, our team NimbRo Explorer solved all tasks of the DLR SpaceBot Camp 2015. We also discuss the lessons learned from this demonstration. We address the problem of planning a path for a ground robot through unknown terrain, using observations from a flying robot. In search and rescue missions, which are our target scenarios, the time from arrival at the disaster site to the delivery of aid is critically important. Previous works required exhaustive exploration before path planning, which is time-consuming but eventually leads to an optimal path for the ground robot. Instead, we propose active exploration of the environment, where the flying robot chooses regions to map in a way that optimizes the overall response time of the system, which is the combined time for the air and ground robots to execute their missions. In our approach, we estimate terrain classes throughout our terrain map, and we also add elevation information in areas where the active exploration algorithm has chosen to perform 3-D reconstruction. This terrain information is used to estimate feasible and efficient paths for the ground robot. By exploring the environment actively, we achieve superior response times compared to both exhaustive and greedy exploration strategies. We demonstrate the performance and capabilities of the proposed system in simulated and real-world outdoor experiments. To the best of our knowledge, this is the first work to address ground robot path planning using active aerial exploration. In future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. To improve rover-based terrain sensing, this paper proposes a self-supervised learning framework that will enable a robotic system to learn to predict mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been traversed previously. In this framework, a proprioceptive terrain classifier is used to distinguish terrain classes based on features derived from rover–terrain interaction, and labels from this classifier are used to train an exteroceptive (i.e., vision-based) terrain classifier. Once trained, the vision-based classifier is able to recognize similar terrain classes in stereo imagery. This paper presents two distinct proprioceptive classifiers—a novel approach based on optimization of a traction force model and a previously described approach based on wheel vibration—as well as a vision-based terrain classification approach suitable for environments with unexpected appearances. The high accuracy of the self-supervised learning framework and its supporting algorithms is demonstrated using experimental data from a four-wheeled robot in an outdoor Mars-analogue environment. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.