Time-Varying Moth-Inspired Algorithm for Chemical Plume Tracing in Turbulent Environment

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.