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Robots are ideal for open-pit mining on the Moon as its a dull, dirty, anddangerous task. The challenge is to scale up productivity with anever-increasing number of robots. This paper presents a novel method fordeveloping scalable controllers for use in multi-robot excavation andsite-preparation scenarios. The controller starts with a blank slate and doesnot require human-authored operations scripts nor detailed modeling of thekinematics and dynamics of the excavator. The 'Artificial Neural Tissue' (ANT)architecture is used as a control system for autonomous robot teams to performresource gathering. This control architecture combines a variable-topologyneural-network structure with a coarse-coding strategy that permits specializedareas to develop in the tissue. Our work in this field shows that fleets ofautonomous decentralized robots have an optimal operating density. Too fewrobots result in insufficient labor, while too many robots cause antagonism,where the robots undo each other's work and are stuck in gridlock. In thispaper, we explore the use of templates and task cues to improve groupperformance further and minimize antagonism. Our results show light beacons andtask cues are effective in sparking new and innovative solutions at improvingrobot performance when placed under stressful situations such as severetime-constraint.

Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining