Embodiment sensing for self-generated zigzag turning algorithm using vision-based plume diffusion

Abstract

Biomimetic Chemical Plume Tracing (CPT) problem is complex because it couples nonlinearity of biological systems with uncertainty of time-varying plume diffusion. A vision-based simulator is proposed to decouple these difficulties to facilitate multiple runs under controlled environment. This enables identification of efficient biological CPT algorithm. The simulator is used to simulate Embodiment Sensing (ES), i.e. sensing using physical attributes of animals. Wings and antennae of silk moth are used for ES, and evaluated for CPT using vision-based simulator. Results suggest (1) vision-based plume field mimics actual plume diffusion in terms intermittency, and (2) similar performance as that for surge-cast algorithm. The contribution is two-fold, (1) vision-based plume diffusion simulator decouples uncertainty of plume diffusion from nonlinearity of biological system to facilitate biomimetic CPT study, and (2) feasibility of using physical attributes of silk moth to achieve good CPT performance.