论文标题
使用loihi处理器的神经形态控制MAV的基于光流的登陆
Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor
论文作者
论文摘要
像Loihi这样的神经形态处理器为赋予良好，高效和自主技能的微型航空车（MAV）（例如，起飞和降落，避免障碍和追捕）提供了常规计算模块的有希望的替代方法。但是，在机器人平台上使用此类处理器的主要挑战是模拟与现实世界之间的现实差距。在这项研究中，我们首次展示了Loihi神经形态芯片原型在飞行机器人中的完全嵌入式应用。进化了尖峰神经网络（SNN），以根据腹侧视野的差异来计算推力命令，以执行自主着陆。使用PYSNN库在基于Python的模拟器中进行进化。由此产生的网络体系结构仅包含35层中分布的35个神经元。模拟和Loihi之间的定量分析揭示了推力设定值低至0.005 g的根平方误差，以及隐藏层中尖峰序列的99.8％匹配，在输出层中匹配99.7％。拟议的方法成功地弥合了现实差距，为机器人技术中的未来神经形态应用提供了重要的见解。补充材料可在https://mavlab.tudelft.nl/loihi/上获得。
Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, efficient and autonomous skills such as take-off and landing, obstacle avoidance, and pursuit. However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world. In this study, we present for the very first time a fully embedded application of the Loihi neuromorphic chip prototype in a flying robot. A spiking neural network (SNN) was evolved to compute the thrust command based on the divergence of the ventral optic flow field to perform autonomous landing. Evolution was performed in a Python-based simulator using the PySNN library. The resulting network architecture consists of only 35 neurons distributed among 3 layers. Quantitative analysis between simulation and Loihi reveals a root-mean-square error of the thrust setpoint as low as 0.005 g, along with a 99.8% matching of the spike sequences in the hidden layer, and 99.7% in the output layer. The proposed approach successfully bridges the reality gap, offering important insights for future neuromorphic applications in robotics. Supplementary material is available at https://mavlab.tudelft.nl/loihi/.
