Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining
Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and dangerous task. The challenge is to scale up productivity with an ever-increasing number of robots. This paper presents a novel method for developing scalable controllers for use in multi-robot excavation and site-preparation scenarios. The controller starts with a blank slate and does not require human-authored operations scripts nor detailed modeling of the kinematics and dynamics of the excavator. The 'Artificial Neural Tissue' (ANT) architecture is used as a control system for autonomous robot teams to perform resource gathering. This control architecture combines a variable-topology neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue. Our work in this field shows that fleets of autonomous decentralized robots have an optimal operating density. Too few robots result in insufficient labor, while too many robots cause antagonism, where the robots undo each other's work and are stuck in gridlock. In this paper, we explore the use of templates and task cues to improve group performance further and minimize antagonism. Our results show light beacons and task cues are effective in sparking new and innovative solutions at improving robot performance when placed under stressful situations such as severe time-constraint.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Evolving Multi-Objective Neural Network Controllers for Robot Swarms
Many swarm robotics tasks consist of multiple conflicting objectives. This research proposes a multi-objective evolutionary neural network approach to developing controllers for swarms of robots. The swarm robot controll…
On an Immuno-inspired Distributed, Embodied Action-Evolution cum Selection Algorithm
Traditional Evolutionary Robotics (ER) employs evolutionary techniques to search for a single monolithic controller which can aid a robot to learn a desired task. These techniques suffer from bootstrap and deception issu…
Limits of Lamarckian Evolution Under Pressure of Morphological Novelty
Lamarckian inheritance has been shown to be a powerful accelerator in systems where the joint evolution of robot morphologies and controllers is enhanced with individual learning. Its defining advantage lies in the offsp…
Exploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans in various games and perceive features imperceptible to humans during classification tasks. Current machine learning techniques have clearly d…
Computational EfficiencyEvolutionary AlgorithmsEvolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a…