Robotteamsim 3d Visualization Of Cooperative Mobile Robot Missions In Gazebo Virtual Environment
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RobotTeamSim – 3D Visualization of Cooperative Mobile Robot Missions in Gazebo Virtual Environment
Author: Gonçalo Nuno dos Santos Augusto
language: en
Publisher: University of Coimbra
Release Date: 2013-09-01
This dissertation intends to create a simulation of search and rescue (SaR) missions using Gazebo virtual world and Robot Operating System (ROS). The simulator is evaluated with multiple robots search using the Robotic Darwinian Particle Swarm Optimization (RDPSO) exploration algorithm, considering several real-world phenomena, such as radio frequency (RF) and voice. Although the RDPSO algorithm has already been developed and evaluated in Matlab environment, it cannot be directly implemented in real platforms. To address this disadvantage, ROS was chosen to implement the algorithm and, due to its compatibility with ROS, Gazebo was chosen as the simulation platform to evaluate multi-robot systems in SaR scenarios. For the RDPSO to be fully implemented, the RF signal and voice propagation models were equally implemented in ROS. For that matter, mathematic models, previously proposed in the literature, are analyzed and a study about the environment noise influence in missions of SaR based on voice localization is conducted. The RF model was simulated using the multi-wall method, which does not only consider the free space signal loss but also the loss of walls with different properties (e.g., thickness and type). The voice model is based on the RF model by adjusting the parameters to better adapt to the voice properties. The RDPSO algorithm, implemented in ROS, uses the RF model to simulate its mobile ad hoc network (MANET) connectivity component. In the context of finding victims, the RDPSO uses the voice propagation model to simulate the call for help by the victims. Environment noise was introduced in the simulation, which influenced the victims’ rescue rate by making harder for the rescue team to listen to the victim’s call. This influence is bigger as the environment noise level gets higher, thus there is the need for a better exploration of the map as the rescue team has to walk closer and closer to the victims, so that voice and noise can be distinguished.
Fractional Order Darwinian Particle Swarm Optimization
This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, such as its ability to provide an improved convergence towards a solution, while avoiding sub-optimality. This book offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for students of electrical engineering and computer science.
Mathematical Methods in Engineering
Author: K. Tas
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-11-25
This book contains some of the contributions that have been carefully selected and peer-reviewed, which were presented at the International Symposium MME06 Mathematical Methods in Engineering, held in Cankaya University, Ankara, April 2006. The Symposium provided a setting for discussing recent developments in Fractional Mathematics, Neutrices and Generalized Functions, Boundary Value Problems, Applications of Wavelets, Dynamical Systems and Control Theory.