Recent Progress and Trend of Robot Odor Source Localization

Abstract

Accurate and prompt localization of hazardous release sources ensures a timely emergency response to potential damages. Robot odor source localization is the study of locating the release source based on mobile robots in real‐time, which has been experiencing rapid growth in recent years. This paper presents a review of this field, focusing on recent progress and development trends. Research works are grouped according to method principles, leading to four categories: reactive methods, heuristic searching methods, probabilistic inference methods, and learning methods. The odor‐source direction and distance prediction via in situ sensing and developments of simulators are also listed separately for discussion. Several outlooks are highlighted at the end. Along with the developments of related fields and practical needs, we believe this field is bound to keep on flourishing in the future. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Game Theory in Defence Applications: A Review

“…Similarly, it could be noted that search is a ‘hide and seek’ game with a long history in military applications [ 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 ]. The theory was pioneered by Koopman [ 106 ] primarily in the military context (search for an escaping target), followed by developments by Stone et al [ 107 ].…”

“…The theory was pioneered by Koopman [ 106 ] primarily in the military context (search for an escaping target), followed by developments by Stone et al [ 107 ]. The applications include submarine hunting, mine detection, rescue operations, the risk for the first responders, and localization of a hazardous source [ 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 ]. This framework provides the optimal a priori search plan for a given detection model, target motion and the cost of the search.…”

Rapid location technology of odor sources by multi‐UAV

“…Traditionally, the active location of odor sources often requires human participation. Since the 1990s, researchers have tried to use mobile robots instead of humans to complete this task to reduce the damage caused to people in locating the odor source (Jing et al, 2021; Tosato et al, 2019). Multirotor unmanned aerial vehicles (UAVs), such as robots, have been widely used in atmospheric environment monitoring because of their flexible maneuverability, minimal interference from terrain, and fast response speed (Burgués & Marco, 2020; Mochammad et al, 2016).…”

Odor source localization of multi-robots with swarm intelligence algorithms: A review

“…Currently, there are four mainstream methods for locating odor sources (Jing et al, 2021 ), viz., reactive methods, heuristic search methods, probabilistic inference methods, and learning methods. The heuristic search method treats odor source localization as a functional optimization problem, that is, finding the optimal odor concentration in a particular region.…”

“…Chen and Huang ( 2019 ) used a new classification approach in their review: using algorithmic principles at the top level of the classification hierarchy, such as how to handle input signals (such as chemotaxis and anemotaxis), classified existing OSL algorithms into four categories, and noted that gradient-based algorithms currently lack attention and that probability and map-based algorithms are more attractive for research. In a recent review, Jing et al ( 2021 ) pointed out that the classification method of the former may lead to some overlap between different categories and proposed a stricter classification method based on the principle of method according to the literatures published in recent years, and also emphasized some aspects not discussed in previous studies: in-situ sensing and simulator development for odor source direction and distance prediction. Meanwhile, they pointed out that the algorithm extensions to 3D environments, multiple robots, and their mixing situation have progressed in the past 10 years.…”