论文标题
CircuitBot：学习在机器人电路图中生存
CircuitBot: Learning to Survive with Robotic Circuit Drawing
论文作者
论文摘要
具有积极从周围环境中获得权力的能力的机器人将对长期自治，并在动态，不确定的环境中生存非常有益。在这项工作中，在机器人的能量有限的情况下呈现了一种情况，生存的唯一方法是从电源获取能量。机器人没有电缆或电线可用，可以在连接过程中构建电路并避免潜在的障碍物。我们提出了该机器人，能够用基于石墨烯的导电墨水绘制连接的电路模式。采用了最先进的混合贝叶斯优化，以优化导电形状的放置，以最大程度地发挥该机器人获得的功率。我们的结果表明，在少量试验中，机器人学会了建立并行电路以最大化收到的电压，并避免从机器人那里窃取能量的障碍。
Robots with the ability to actively acquire power from surroundings will be greatly beneficial for long-term autonomy, and to survive in dynamic, uncertain environments. In this work, a scenario is presented where a robot has limited energy, and the only way to survive is to access the energy from a power source. With no cables or wires available, the robot learns to construct an electrical path and avoid potential obstacles during the connection. We present this robot, capable of drawing connected circuit patterns with graphene-based conductive ink. A state-of-the-art Mix-Variable Bayesian Optimization is adopted to optimize the placement of conductive shapes to maximize the power this robot receives. Our results show that, within a small number of trials, the robot learns to build parallel circuits to maximize the voltage received and avoid obstacles which steal energy from the robot.
