Optimal robot arm control using the minimum variance model
Optimal robot arm control using the minimum variance model
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DOI：
10.1002/rob.20092
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
G. Simmons;Y. Demiris
中科院分区：
文献类型：
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作者：
G. Simmons;Y. Demiris
来自计算神经科学的人类运动模型为构建能够在机器人上产生灵活自适应运动的系统提供了起点。已经提出了许多人类上肢运动的计算模型，每个模型都试图解释一个或多个表征这种运动的刻板特征。虽然这些模型成功地捕捉到了人类运动的一些特征，但它们往往缺乏令人信服的生物学基础来支持它们选择优化的标准。提供这样一个基础的是最小方差模型及其在信号相关噪声存在下的扩展任务优化。这里，在移动结束时的手的位置的方差被最小化，假设在臂的致动器上的控制信号受到具有零均值和方差与信号的幅度成比例的随机噪声的影响。由于需要快速移动的大控制信号将具有更高的幅度噪声，因此速度-精度权衡作为优化过程的直接结果出现。我们选择实现这个模型的一个版本，这将是适合于机器人手臂的控制，使用基于离散时间线性二次调节器的最优控制方案。这个实现使我们能够检查最小方差模型产生类人运动的适用性。在本文中，我们描述了最小方差模型的实现，既适用于点对点到达移动，也适用于涉及通过点的更复杂轨迹。我们还评估了它在产生类似人类运动方面的性能，并展示了它相对于其他基于优化的模型（众所周知的用于控制机器人手臂的最小加加速度和最小扭矩变化模型）的优势。© 2005 Wiley Periodicals，Inc.
Models of human movement from computational neuroscience provide a starting point for building a system that can produce flexible adaptive movement on a robot. There have been many computational models of human upper limb movement put forward, each attempting to explain one or more of the stereotypical features that characterize such movements. While these models successfully capture some of the features of human movement, they often lack a compelling biological basis for the criteria they choose to optimize. One that does provide such a basis is the minimum variance model and its extension—task optimization in the presence of signal-dependent noise . Here, the variance of the hand position at the end of a movement is minimized, given that the control signals on the arm’s actuators are subject to random noise with zero mean and variance proportional to the amplitude of the signal. Since large control signals, required to move fast, would have higher amplitude noise, the speed-accuracy trade-off emerges as a direct result of the optimization process. We chose to implement a version of this model that would be suitable for the control of a robot arm, using an optimal control scheme based on the discrete-time linear quadratic regulator. This implementation allowed us to examine the applicability of the minimum variance model to producing humanlike movement. In this paper, we describe our implementation of the minimum variance model, both for point-to-point reaching movements and for more complex trajectories involving via points. We also evaluate its performance in producing humanlike movement and show its advantages over other optimization based models the well-known minimum jerk and minimum torque-change models for the control of a robot arm. © 2005 Wiley Periodicals, Inc.