Modelling and simulation for autonomous systems 6th International Conference, MESAS 2019, Palermo, Italy, October 29-31, 2019, revised selected papers /: 6th International Conference, MESAS 2019, Palermo, Italy, October 29-31, 2019, revised selected papers. (©2020)

• Record Type: Book
• Further Information: Note: Jan Mazal, Adriano Fagiolini, Petr Vasik (eds.).
• Other Names: Mazal, Jan; Fagiolini, Adriano; Vasik, Petr; MESAS (Workshop), 6th
• Contents: Intro -- Preface -- MESAS 2019 Organizer -- Organization -- Contents -- M&S of Intelligent Systems -- AI, R&D and Application -- Aerial Reconnaissance and Ground Robot Terrain Learning in Traversal Cost Assessment -- 1 Introduction -- 2 Multi-goal Path Planning from Aerial Imagery -- 3 Proposed Method -- 4 Results -- 5 Conclusion -- References -- Combined PSO Methods for UAVs Swarm Modelling and Simulation -- 1 Introduction -- 2 Combined PSO Methods -- 2.1 Leadership -- 2.2 Formations -- 2.3 Simple Collision Avoidance -- 2.4 Predicted Collision Avoidance -- 2.5 Combinations 3 UAVs Swarm Simulation Environment -- 4 Simulation Experiments -- 4.1 Collision Avoidance Mechanism Experiments -- 4.2 Combined Leadership Mechanism -- 4.3 Combined Formations Mechanism -- 5 Conclusion -- References -- Kinematic Model of a Specific Robotic Manipulator -- 1 Introduction -- 1.1 Rigid Motion -- 1.2 Homogeneous Representation -- 1.3 Exponential Coordinates for Rigid Motion and Twists -- 2 Model of the Manipulator -- 3 Forward Kinematics -- 4 Inverse Kinematics -- 4.1 Denavit-Hartenberg Parameters -- 5 Conclusion -- References Low-Cost RGB-D-SLAM Experimental Results for Real Building Interior Mapping -- 1 Introduction -- 2 Low-Cost Mapping -- 2.1 The SLAM Loop -- 2.2 The Sensor: Microsoft Kinect for Windows v1 -- 2.3 The Localization Algorithm: RGB-D-SLAM -- 2.4 The Closing: g2o Algorithm -- 2.5 The Representation: 3D Point Cloud -- 3 Application to the Mapping of the Interior of a Building -- 3.1 Overview -- 3.2 Caveats -- 3.3 Post-processing -- 4 Experimental Results -- 5 Conclusion and Future Work -- References Deep Learning Algorithms for Vehicle Detection on UAV Platforms: First Investigations on the Effects of Synthetic Training -- 1 Introduction -- 2 Object of Research -- 3 State of the Art: Datasets, Simulation Testbed and Algorithms -- 3.1 Natural Datasets for UAV Vehicle Detection -- 3.2 Presagis M&S Suite for Training and Testing in Synthetic Environments -- 3.3 Algorithms for UAV Vehicle Detection -- 4 Generation and Analysis of the Synthetic Data Set -- 4.1 Bounding Box and Ground Truth Generation for Synthetic Environments 4.2 Image Generation Scheme, Parameter Distribution and Explorative Data Analysis -- 5 Experimental Setup and Results -- 5.1 Metrics for Evaluation -- 5.2 Training -- 5.3 Natural Training Data and Influence of the Learning Rate -- 5.4 Performance on Synthetic Test Data for Natural Training -- 5.5 Training with the Virtual Training Data Set -- 5.6 Training with Mixed Data Set -- 6 Conclusion and Future Work -- References -- Building a Generic Simulation Model for Analyzing the Feasibility of Multi-Robot Task Allocation (MRTA) Problems -- 1 Introduction
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Copyright Date: 2020
• Extent: 1 online resource (450 p.)
• Subjects: 003/.3; Computer simulation -- Congresses; Drone aircraft -- Automatic control -- Congresses; Robotics -- Congresses; Artificial intelligence -- Congresses; Intelligent control systems -- Congresses; Artificial intelligence; Computer simulation; Intelligent control systems; Robotics; Electronic books; Conference papers and proceedings
• Languages: English
• ISBNs: 9783030438906; 3030438902
• Notes: Note: Includes bibliographical references and index.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.