Neuromorphic Sensorimotor Integration for Legged Locomotion (NSILL)

在行走过程中，机器人必须使用多种感官来引导其运动。例如，它可能需要避开障碍物，抬起腿越过颠簸，或者迈出更大的一步以避开间隙。基本反射反应直接接触（或缺乏接触）不平整的地面，但行走将有助于感知地面变化或障碍物提前。这将有助于真正看到前方的情况。在活体动物中，行走模式通常依赖于中枢模式发生器（CPG）来产生规律的步态。然而，CPG输出必须学会在复杂和不熟悉的地形上控制行走——它必须使用动物的感官来调整行走模式。或者，模式可能来自分布式控制，其中每条腿影响其邻居。当“模式”由于复杂的地形而变得非常不规则时，这种方法可能是有利的。这些行走问题的生物学解决方案为行走机器人提供了工程学解决方案。我们将使用模拟/数字硅芯片设计和构建生物启发系统，控制6条腿的机器人。有三个研究方向。1)一个CPG芯片，可以学习产生自适应的行走模式。生物cpg在产生不同的节奏方面非常灵活。神经调节剂、中枢指令和输入信号都会影响CPG产生的模式。它们通过改变单个神经元的电学和化学特性以及不同神经元群之间的耦合来实现这一目标。例如，视觉输入可以使动物飞奔起来。我们之前已经开发了一种能够产生各种动物步态的CPG电路。现在，我们将在硅中构建这种新型CPG，并在项目期间设计和制造的六足昆虫机器人上进行测试。然后，我们将评估其产生足够灵活的输出以处理复杂地形的能力，并将其与更分散的控制系统或这两种形式的步行控制的适当混合进行比较。2)估计图像中物体距离的视觉芯片。神经形态视觉传感器已被用于边缘检测、速度和深度感知。然而，这些都是孤立的案例研究，没有将机器人的视觉与其他感官结合起来。我们将设计一种硅芯片，实现一种新颖的视觉深度传感方法，该方法是在先前的资助下开发的，基于尖峰神经元模型。这种程式化的早期视觉模型将由一个二维光敏晶体管阵列组成，具有边缘传感和深度检测的能力。3)一种受生物学启发的芯片，可以整合感官来做决定。该芯片将结合视觉处理器的输出（检测到的物体及其深度）和额外的感官（触觉和关节角度传感器）来调整行走模式，从而在粗糙的地面上实现平稳、稳定的移动。特别是，它将学会使用视觉深度信息来预测避免碰撞和保持稳定立足点所需的运动。我们将在模拟中测试这些方法，然后使用微处理器上的软件在真实机器人上测试结果。然后，我们将创建一个芯片，将传感器对世界的看法转换为步行控制器的适当调制。该项目的最后阶段将是将所有三种芯片集成到六足机器人上，以生产出具有明确生物前因的新型步行机器人。因此，该项目将推进神经形态VLSI和传感器-运动集成的研究，直接应用于家庭应用和在危险、困难和未知环境中工作的移动机器人。它还将有助于识别神经系统中一些基本的计算和控制原理。

During walking, a robot must use several senses to guide its movements. For example, it may need to avoid obstacles, to lift its legs over bumps, or to take a longer stride to avoid a gap. Basic reflexes respond to direct contact (or lack of contact) with uneven ground, but walking would be aided by sensing the ground surface variation or obstacles in advance. It would help to literally see what is ahead . In living animals, walking patterns often depend on a Central Pattern Generator (CPG) to generate a regular gait. The CPG output must, however, learn to control walking on complex and unfamiliar terrain - it must use the animal's senses to adjust walking patterns. Alternatively, the patterns might emerge from distributed control in which each leg influences its neighbours. This approach might be advantageous when the 'pattern' becomes very irregular due to complex terrain.These biological solutions to the problem of walking suggest engineering solutions for walking robots. We will design and build biologically inspired systems using analogue/digital silicon chips, controlling a 6-legged robot. There are three research strands. 1) A CPG chip that learns to generate adaptive walking patterns. Biological CPGs are extremely flexible for producing different rhythms. Neuromodulators, central commands and input signals all influence the pattern produced by a CPG. They do so by altering both the electrical and chemical properties of individual neurons and the coupling between different groups of neurons. For example, visual inputs can cause an animal to break into a gallop. We have previously developed a CPG circuit capable of producing a wide range of animal gaits. We will now build this novel CPG in silicon and test it on a 6-legged insect-like robot, designed and built during the project. We will then evaluate its ability to produce sufficiently flexible output to deal with complex terrain, and compare it to a more distributed control system, or to a suitable hybrid of these two forms of walking control.2) A vision chip that estimates the distance to objects in an image. Neuromorphic vision sensors have been built for edge detection, velocity- and depth-sensing. These are, however, isolated case studies that do not integrate vision with other senses for robotic applications. We will design a silicon chip that implements a novel vision depth sensing method, developed under a previous grant and based on a spiking neuronal model. This stylised early vision model will consist of a 2-D array of light-sensitive transistors with the ability to perform both edge sensing and depth detection. 3) A biologically-inspired chip that integrates senses to make decisions. This chip will combine the output of the visual processor (detected objects and their depth) with additional senses (touch and joint angle sensors) to adjust the walking patterns to achieve smooth, stable movement across rough ground. In particular it will learn to use the visual depth information to anticipate movements required to avoid collisions and maintain stable footing. We will test the methods in simulation, then use software on microprocessors to test the results on real robots. We will then create a chip which will translate the sensors' view of the world into appropriate modulation of the walking controller. The final stage of the project will be the integration of all three chips on to the 6-legged robot to produce a new walking robot with explicit biological antecedents. The project will thus advance research in neuromorphic VLSI and sensor-motor integration, with direct applications in mobile robots for domestic applications and work in hazardous, difficult and unknown environments. It will also help to identify some of the basic computing and control principles in the nervous system.