Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Particle Swarm Optimization (PSO) for Simulating Robot Movement on Two-Dimensional Space Based on Odor Sensing

Nowadays, researches in robotic field have grown increasingly. There are several types of research categories in the field of robotic. Recently, one of the famous research works recently was odor sensing. Within the technology that grows rapidly, this topic has become an interest among researchers. An odor sensing is not only applied in the medical field, but it has also been widely used in the industry. The gradient of concentration of an odor is measured by diluting some amount to reach the threshold of an odor. This paper focused on the implementation of the Particle Swarm Optimization (PSO) method based on odor sensing in two (2) dimensional space. However, it only discusses and focuses on applying in ideal condition. An ideal condition here means that there is no disturbance included in this simulation. The main idea of this paper was to observe how the particle agents make the movement based on concentration by applying the PSO method. The real sensor cannot be implemented in this simulation because the value of concentration is measured due to the distance from the particles agent to the goal of agents. Higher gradient concentration is shown at the shorter distance to the goal. The contributions in this paper are mainly to create an algorithms model by using Particle Swarm Optimization (PSO) to calculate the paths of movement of mobile robot until they reach the goals (source of odor) with respect to the concepts of odor sensing

Human Swarm Interaction for Radiation Source Search and Localization

This study shows that appropriate human interaction can benefit a swarm of robots to achieve goals more efficiently. A set of desirable features for human swarm interaction is identified based on the principles of swarm robotics. Human swarm interaction architecture is then proposed that has all of the desirable features. A swarm simulation environment is created that allows simulating a swarm behavior in an indoor environment. The swarm behavior and the results of user interaction are studied by considering radiation source search and localization application of the swarm. Particle swarm optimization algorithm is slightly modified to enable the swarm to autonomously explore the indoor environment for radiation source search and localization. The emergence of intelligence is observed that enables the swarm to locate the radiation source completely on its own. Proposed human swarm interaction is then integrated in a simulation environment and user evaluation experiments are conducted. Participants are introduced to the interaction tool and asked to deploy the swarm to complete the missions. The performance comparison of the user guided swarm to that of the autonomous swarm shows that the interaction interface is fairly easy to learn and that user guided swarm is more efficient in achieving the goals. The results clearly indicate that the proposed interaction helped the swarm achieve emergence

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations