Autonomous dust detection and removal for space surface vehicles navigation and exploration awareness

There has been an exponential increase in planned Lunar missions with the advent of commercial missions and future manned missions. Understanding the lunar environment is crucial to providing information for future missions that will land on the Moon and other celestial bodies. The necessity to navigate freely, without loss of sensing capability is paramount to many of these explorations. Some of the vehicles that will pioneer these missions will encounter various natural hazards and awareness challenges-such as lunar dust and regolith. Due to their electrical and mechanical properties, the constituent particles will tend to adhere to the surface of the exploratory craft and the on-board equipment. This behavior poses a significant threat as the charged dust can cause vital electronics to short or render experimental instruments unusable due to accumulation and electrical discharges, as experienced by the Apollo crews on each visit to the Moon. This paper will focus on the design and experimentation of an autonomous control system that will detect regolith using the deep learning computer vision architecture MobileNetV2, and remove it using an electrodynamic dust shield covering a camera lens. This entire system provides an active method to detect optical obstructions, assess, and then remove them in order to regain navigation and/or scientific capabilities.