Robots can locate odor sources in extreme conditions, such as underwater or in poisonous air, and work more effectively than animals while staying unharmed. Robotic odor source localization has grown in popularity over the past three decades. The majority of approaches proposed by researchers are categorized under four broad areas: bio or swarm-inspired methods, formation-based methods, probabilistic or infotaxis methods, and learning-based methods. In this work, we have included one more category that has recently been introduced in odor source localization. Further, we analyze the literature on these five categories and weigh their benefits and drawbacks. This study also addresses various odor source localization cases and examines the particular difficulties associated with each one. The significance of elements like environmental conditions and odor source modelling has also been analyzed. This review paper not only summarizes the state-of-the-art methods in odor source localization but also provides an analysis of their potential and limitations, offering insights into the challenges and opportunities for future advancements.

This chapter concerns the problem of odor source localization by a team of mobile robots. A brief overview of odor source localization is given which is followed by related work. Three methods are proposed for odor source localization. These methods are largely inspired by gravitational search algorithm, grey wolf optimizer, and particle swarm optimization. Objective of the proposed approaches is to reduce the time required to localize the odor source by a team of mobile robots. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. Robots start search from the corner of the workspace. As robots enter in the vicinity of plume area, they form groups using K-nearest neighbor algorithm. To avoid stagnation of the robots at local optima, search counter concept is used. Proposed approaches are tested and validated through simulation.

This paper concerns with the problem of odor source localization by a team of mobile robots. The authors propose two methods for odor source localization which are largely inspired from gravitational search algorithm and particle swarm optimization. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. As robots enter in the vicinity of plume area they form groups using K-nearest neighbor algorithm. The problem of local optima is handled through the use of search counter concept. The proposed approaches are tested and validated through simulation.