A Swarm Robotic Approach to Inspection of 2.5 D Surfaces in Orbit

Robotic inspection offers a robust, scalable, and flexible alternative to deploying fixed sensor networks or humaninspectors. While prior work has mostly focused on single robot inspections, this work studies the deployment of a swarm ofinspecting robots on a simplified surface of an in-orbit infrastructure. The robots look for points of mechanical failure and inspectthe surface by assessing propagating vibration signals. In particular, they measure the magnitude of acceleration they sense ateach location on the surface. Our choice for sensing and analyzing vibration signals is supported by the established position ofvibration analysis methods in industrial infrastructure health assessment. We perform simulation studies in Webots, a physicsbased robotic simulator, and present a distributed inspection algorithm based on bio-inspired particle swarm optimization andevolutionary algorithm niching techniques to collectively localize an a priori unknown number of mechanical failure points. Toperform the vibration analysis and obtain realistic acceleration data, we use the ANSYS multi-physics simulation software andmodel mechanical failure points as vibration sources on the surface. We deploy a robot swarm comprising eight robots of 10-cmsize that use a bio-inspired inchworming locomotion pattern. The swarm is deployed on 2.5D (that is curved 2D) cylindricalsurfaces with and without obstacles to investigate the robustness of the algorithm in environments with varying geometric complexity. We study three performance metrics: (1) proximity of the localized sources to their ground truth locations, (2) time tolocalize each source, and (3) time to finish the inspection task given an 80% surface coverage threshold. Our results show thatthe robots accurately localize all the failure sources and reach the coverage threshold required to complete the inspection. Thiswork demonstrates the viability of deploying robot swarms for inspection of potentially complex 3D environments.<br/

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

Odor Localization Sub Tasks: A Survey

This paper discusses about the sub tasks of odor localization research. Three steps of odor localization, i.e. Plume finding, plume tracking/tracing, and source declaration are explained. The difficulty of plume finding is discussed. Farrell’s Filamentous and Pseudo-Gaussian plume models that have been analyzed by previous researcher are presented. Some approaches used in plume tracking/tracing based on advection/turbulent and the estimation of odors’ distribution are provided. The advantages of source declaration are showed. Some problems occur in plume finding become a great consideration for the future research

Optimal Gas Sensors Arrangement in Odor Searching Robot

This paper presents an analysis of an optimal sensor arrangement in Odor Searching Robot (OSR). 5 gas sensors integrated in OSR can help the OSR to navigate to the source. Since low cost, low computation and robust robot is preferred in swarm robot application, the OSR, as an individual robot of swarm in this study, is designed to be able to switch into the mode 3 or the mode 5 in order to analyze the optimal distance of the gas sensors arrangement that can be integrated in the OSR. By knowing the optimal sensor arrangement, the low cost and or the low computation OSR can be established. Algorithms of fuzzy logic for 3 and 5 gas sensors are tested in open environment. The concentration of gas is used as the input of the fuzzy logic. The robot uses the concentration, as its parameters in determining which way that it should take. From this research, it can be concluded that there was no significant difference between using 3 gas sensors or 5 gas sensors