University of Cincinnati, 2600 Clifton Ave, Cincinnati, OH 45221, United States of America.
Bioinspir Biomim. 2017 Nov 8;12(6):066011. doi: 10.1088/1748-3190/aa7eab.
Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.
仿生机器人因其在资源搜索到灾难搜救等各种应用中的广泛关注而受到关注。众所周知，生物物种能够直观地从环境中学习，收集和处理数据，并做出适当的决策。机器人中如此复杂的计算能力很难实现，尤其是在超低能耗的情况下实时完成。在这里，我们提出了一种基于新型忆阻器件的机器人学习架构。通过在交叉阵列中模拟具有氧化物层电阻开关的两个终端忆阻器件，开发出一种神经形态平台，使机器人能够赋予主动实时学习能力。通过在具有随机放置障碍物的未知环境中导航机器人车辆来验证该方法。此外，还使用环境的局部和全局知识对所提出的方案与基于强化学习的算法进行了比较。仿真和实验结果都证实了所提出的机器人学习方案的有效性和潜力。结果还表明，我们的学习方案在机器人导航的某些环境布局中接近最优解。
