Time-Varying Moth-Inspired Algorithm for Chemical Plume Tracing in Turbulent Environment
Time-Varying Moth-Inspired Algorithm for Chemical Plume Tracing in Turbulent Environment
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DOI：
10.1109/lra.2017.2730361
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发表时间：
2018-01-01
影响因子：
5.2
通讯作者：
Kanzaki, Ryohei
中科院分区：
文献类型：
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作者：
Shigaki, Shunsuke;Sakurai, Takeshi;Kanzaki, Ryohei
在这项研究中，我们提出了一个强大的化学羽流跟踪（CPT）算法的各种环境。我们基于全球具有CPT能力的昆虫的行为开发了这个算法。然而，由于轨迹包含高水平的测量噪声，因此难以从轨迹数据准确地估计行为模式。我们采用飞行肌肌电图，其运动指令被假定为在相同的神经节中产生的行为，使用支持向量机来估计的行为模式。通过使用估计的结果，我们模拟了昆虫的时变行为变化，并验证了使用建设性的方法CPT现象的有效性。我们将这种时变CPT算法命名为时变蛾启发算法。从使用机器人的CPT实验结果中，我们发现，通过以随时间变化的方式改变行为，定位成功率得到了提高。
In this study, we propose a robust chemical plume tracing (CPT) algorithm for various environments. We developed this algorithm based on the behavior of insects that have CPT abilities globally. However, it is difficult to accurately estimate the behavior pattern from trajectory data because the trajectory contains a high level of measurement noise. We employed flight muscle electromyograms, whose motor commands were assumed to be generated in the same ganglion as the behavior, to estimate the behavior pattern using a support vector machine. By using the estimated results, we modeled the time-varying behavioral change of insects and verified the effectiveness of the phenomenon for CPT using the constructive approach. We named this time-varying CPT algorithm as time-varying moth-inspired algorithm. From the CPT experiment results using the robot, we found that the localization success rate improved by changing the behavior in a time-variant manner.