Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm Optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by Particle Swarm Optimization. Additionally, we exploit this inspiration by modifying the Particle Swarm Optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range

Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization

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