可用于机械臂控制的小脑脉冲神经元网络研究与FPGA实现
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天津大学

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TP183

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国家自然科学基金项目(面上项目)


Research on Cerebellar SNN model and FPGA implementation for robotic arm control
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Tianjin University

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    摘要:

    小脑对运动的调控和对环境的适应性是人体完成快速精准运动的关键,模拟并研究小脑的运行机制将为控制复杂多变的机器人模型提供更有效的方法。本文遵循神经元数量的真实生物比率,构建大规模小脑脉冲神经网络模型,模拟大脑中小脑的真实结构、信息传递方式和学习机制,实现对机械臂的误差纠正控制,依据系统在不同控制任务下的控制结果,得到不同突触可塑性对小脑网络控制效果的影响规律。为了进一步增加小脑控制系统的生物真实性,以更贴近人脑的并行运算方式在现场可编程门阵列(Field Programmable Gate Array, FPGA)平台上实现了所构建的模型,并进行了相应的资源优化,增加了可实现的网络规模。FPGA实现结果显示系统成功完成了基于小脑误差纠正功能的自适应类脑机械臂控制,验证了小脑的真实细胞动力学和大规模颗粒层提供的高容错性,提供了兼顾小脑应用功能实现和理论研究的平台。

    Abstract:

    The movement control of the cerebellum and its adaptability to the environment are the keys to complete rapid and precise movement for humans. Simulating and studying the operating mechanism of the cerebellum will provide a better way to control complex and changeable robot models. In this paper, by following the real biological ratio of different types of cerebellar neurons, a large-scale spiking neural network model of the cerebellum is built and the realistic structure, information transmission method, and learning mechanism of the cerebellum are simulated. We also complete the error correction control of a simulated robotic arm and clarify the influence of different synaptic plasticities on the control effect of the cerebellar network with the control results of the system under different control tasks. In order to further increase the biological authenticity of the cerebellum control system, the model was implemented on a Field Programmable Gate Array (FPGA) platform in a parallel operation approach closer to the human brain, and corresponding resource optimization methods were proposed so that the achievable network scale is increased. The FPGA implementation results reveal that the system successfully simulates the adaptive brain-inspired robotic arm control based on the cerebellar error correction ability. The cell dynamics of the cerebellum can also be reproduced on the system and the high fault tolerance from large-scale granule cells is proven. This work provides a platform that takes into account both the realization of cerebellar application functions and the theoretical research.

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历史
  • 收稿日期:2021-07-27
  • 最后修改日期:2021-11-22
  • 录用日期:2021-11-26
  • 在线发布日期: 2022-01-02
  • 出版日期: