Chemical Plume Tracing and Odour Source Localisation by Autonomous Vehicles

Abstract

Autonomous vehicles with an ability to trace chemical plumes can be instrumental in tasks such as detection of unexploded ordnance, search for undersea wreckage and environmental monitoring. As a consequence, use of autonomous vehicles to perform chemical plume tracing has received an increasing interest from the research community in recent years. Owing to the diversity of applications and ambient fluid environment of the plumes, there are numerous plume tracing strategies and approaches. This paper reviews two main approaches and a number of strategies that have been successfully implemented to track air or water borne plumes in order to locate odour sources using autonomous vehicles. The first strategy considered is the biomimetic approach that offers excellent models for the development of robotic systems. Strategies inspired by lobsters and bacterium are the main focus in this study. The second scheme considers parallelization of the search procedure by employing a multi-robot approach. This approach has the advantage of utilising a group of smaller and simpler communicating robots which are capable of performing a collaborative search of the plume.

Sensor-based problems and techniques for Autonomous Underwater Vehicles

“…Considering the case of CPT, underwater communication restrictions are given as the reasons for unsatisfactory algorithm performance thus the lack of pursuit of multiple vehicles extensions [35,Sec. 6].…”

Reactive planning for olfactory-based mobile robots

“…If a dense array of sensors were distributed over an area through which a turbulent flow was advecting odor and the output of each sensor were averaged for a suitably long time (i.e., several minutes), then this average odor distribution would be Gaussian [29], [30]; however, the required dense spatial sampling and long time-averaging makes such an approach inefficient in a turbulence dominated environment [25]. It is known that the instantaneous odor distribution will be distinct from the time-averaged plume [14], [22].…”

Mobile robot odor source localization via semi-tensor product

“…One is olfaction-based, which mainly use olfaction or/and wind information to search for gas sources without visual information. Most work has been focused on this field and has become a mature and popular filed [4][5][6][7][8][9]. The other is vision-based, which take the visual information as an assistant of olfaction to accomplish the OSL task.…”