Motion planning based on simultaneous perturbation stochastic approximation for mobile auditory robots
Motion planning based on simultaneous perturbation stochastic approximation for mobile auditory robots
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DOI：
10.1109/iros.2010.5649244
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
M. Kumon;K. Fukushima;S. Kunimatsu;M. Ishitobi
中科院分区：
文献类型：
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作者：
M. Kumon;K. Fukushima;S. Kunimatsu;M. Ishitobi
提出了一种基于优化技术的移动的听觉机器人运动规划方法。由于它是听觉机器人正确识别语音信息的最重要的能力之一，所提出的方法的目的是最大限度地提高语音识别的置信度，因为该措施被认为是强烈相关的语音识别的准确性。然而，要优化的成本函数很难明确建模，并且很难获得通常用于导出运动的梯度。为了克服这一困难，同时扰动随机近似（SPSA），不需要一个明确的模型的成本函数被应用到生成机器人运动。通过真实的实验验证了该方法的有效性：机器人在接近声源后，通过测量置信度可以获得较好的语音识别率。
In this paper, a motion planning method for mobile auditory robots is proposed based on an optimization technique. Since it is one of the most important abilities for auditory robots to recognize vocal messages correctly, the proposed method is designed to maximize the confidence measure of a speech recognition since the measure is thought to be strongly related to the accuracy of the speech recognition. However, the cost function to optimize is hard to model explicitly, and it is difficult to obtain the gradient that is normally utilized to derive the motion. In order to overcome this difficulty, simultaneous perturbation stochastic approximation(SPSA) that does not require an explicit model of the cost function is applied to generate robot motion. The effectiveness of the approach was verified through real experiments: the robot could get better speech recognition rate after it approached the sound source by measuring the confidence measure.