LEADER 07671nam 22007215 450 001 9910767568403321 005 20251113201844.0 010 $a9783031483035 010 $a3031483030 024 7 $a10.1007/978-3-031-48303-5 035 $a(CKB)29092584400041 035 $a(MiAaPQ)EBC30977740 035 $a(Au-PeEL)EBL30977740 035 $a(OCoLC)1411185547 035 $a(DE-He213)978-3-031-48303-5 035 $a(EXLCZ)9929092584400041 100 $a20231129d2023 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInformatics in Control, Automation and Robotics $e19th International Conference, ICINCO 2022 Lisbon, Portugal, July 14-16, 2022 Revised Selected Papers /$fedited by Giuseppina Gini, Henk Nijmeijer, Wolfram Burgard, Dimitar Filev 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (158 pages) 225 1 $aLecture Notes in Networks and Systems,$x2367-3389 ;$v836 311 08$a9783031483028 327 $aIntro -- Preface -- Organization -- Contents -- Improved Robust Neural Network for Sim2Real Gap in System Dynamics for End-to-End Autonomous Driving -- 1 Introduction -- 2 Simple Mathematical Model -- 2.1 System Model Without Controller -- 2.2 P-Controller for Vehicle -- 2.3 Offset to Steering Angle -- 3 Previous Results with PilotNetRLSTM -- 4 PilotNetCLSTM and PilotNetCF -- 4.1 Naming Convention -- 4.2 Discretization of Steering Angles -- 4.3 Neural Network Architecture -- 5 Experimental Setup -- 5.1 Dataset Creation Methods -- 5.2 Dataset Overview -- 5.3 Evaluation Methods -- 6 Experimental Results -- 6.1 Comparing Results for Steering Offset -- 6.2 Comparing Results for Normal Driving Test -- 6.3 Comparing Results for Accuracies on the Validation Dataset -- 7 Discussion and Conclusion -- References -- Optimal Robust Control with Applications for a Reconfigurable Robot -- 1 Introduction -- 2 Kinematics Development -- 3 Optimal Robust Control -- 3.1 Mixed Sensitivity H Control -- 3.2 H Control Design -- 4 Application of Mixed Sensitivity H Control (Simulation and Results) -- 4.1 Application of H Control (Simulation and Results) -- 4.2 Application of -Synthesis Control and DK Iterations (Simulation and Results) -- 4.3 Comparison of H and -Controllers -- 5 Conclusions -- References -- Improving 2D Scanning Radar and 3D Lidar Calibration -- 1 Introduction -- 2 Radar Basics -- 3 Related Works -- 3.1 Target-Based Methods -- 3.2 Target-Less Methods -- 3.3 Speckle Filtering of Radar Signals -- 3.4 Improving Resolution of Radar Signals -- 3.5 Contribution -- 4 Methodology -- 4.1 Assumptions -- 4.2 Synchronization -- 4.3 Motion Correction -- 4.4 Preprocessing -- 4.5 Matching -- 4.6 Optimization -- 5 Experiments -- 5.1 Test Data and Environment -- 5.2 Improved Radar Filtering -- 5.3 Plane Optimization -- 5.4 Validation -- 6 Results. 327 $a6.1 Improved Radar Filtering -- 6.2 Plane Optimization -- 6.3 Validation -- 7 Conclusion -- References -- Mobile Robots for Teleoperated Radiation Protection Tasks in the Super Proton Synchrotron -- 1 Introduction -- 1.1 The Significance of Radiation Protection Measures at CERN -- 1.2 Conducting Radiation Surveys in the Super Proton Synchrotron (SPS) -- 1.3 Mobile Robotics for Inspection: Advantages and Challenges -- 2 State of the Art on Teleoperated Robotics for Inspection -- 3 A Mobile Robot for Radiation Protection Operations -- 3.1 Hardware -- 3.2 Software Development and Integration -- 4 Experimental Assessment -- 4.1 Testing Methodology -- 4.2 Robotic Radiation Survey Results -- 4.3 Dual Robot Setup -- 5 Final Thoughts and Future Directions -- References -- A Review of Classical and Learning Based Approaches in Task and Motion Planning -- 1 Introduction -- 2 Background -- 2.1 Task Planning -- 2.2 Motion Planning -- 2.3 Task and Motion Planning Objectives -- 3 Task and Motion Planning Methods -- 3.1 Classical Methods -- 3.2 Learning Based Methods -- 3.3 Hybrid Methods -- 4 Benchmarks and Tools -- 5 Challenges -- 5.1 Observation Uncertainty -- 5.2 Action Uncertainty -- 5.3 Context-Aware Decision Making -- 5.4 Balance Between Optimum and Feasibility -- 6 Conclusion -- References -- Multi-objective Ranking to Optimize CNN's Encoding Features: Application to the Optimization of Tracer Dose for Scintigraphic Imagery -- 1 Introduction -- 2 A Reminder on Texture Encoding -- 3 The Rank-Order Aggregation Problem -- 3.1 Explicit or Implicit Resolution -- 3.2 Euclidean Distance (Spearman Distance) -- 3.3 Rank Absolute Deviation Distance -- 4 Implementation -- 5 Optimization of Tracer Dose for Scintigraphic Imagery -- 6 Conclusion -- References -- Linear Delta Kinematics Feedrate Planning for NURBS Toolpaths Implemented in a Real-Time Linux Control System. 327 $a1 Introduction -- 2 NURBS Path Interpolation -- 3 Delta Parallel Kinematics -- 4 S-Curve Feedrate Planning -- 5 Delta Machine and Control System -- 6 Experimental Results -- 7 Conclusion -- References -- Automatic Fault Detection in Soldering Process During Semiconductor Encapsulation -- 1 Introduction -- 2 Related Work -- 3 Visual Inspection of Solder Balls -- 3.1 Image Segmentation -- 3.2 Image Classification -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Image Acquisition -- 4.3 Solder Ball Regions Detection Evaluation -- 4.4 Solder Ball Classification Evaluation -- 4.5 Robustness Evaluation of Solder Ball Classification in Presence of Noise -- 5 Conclusion and Future Work -- References -- Author Index. 330 $aThe book focuses the latest endeavors relating researches and developments conducted in fields of control, robotics, and automation. Through ten revised and extended articles, the present book aims to provide the most up-to-date state-of-the-art of the aforementioned fields allowing researcher, Ph.D. students, and engineers not only updating their knowledge but also benefiting from the source of inspiration that represents the set of selected articles of the book. The deliberate intention of editors to cover as well theoretical facets of those fields as their practical accomplishments and implementations offers the benefit of gathering in a same volume a factual and well-balanced prospect of nowadays research in those topics. A special attention toward ?Intelligent Robots and Control? may characterize another benefit of this book. 410 0$aLecture Notes in Networks and Systems,$x2367-3389 ;$v836 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aAutomatic control 606 $aRobotics 606 $aAutomation 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aControl, Robotics, Automation 606 $aControl and Systems Theory 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aAutomation. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aControl, Robotics, Automation. 615 24$aControl and Systems Theory. 676 $a006.3 700 $aGini$b Giuseppina$025514 701 $aNijmeijer$b Henk$027871 701 $aBurgard$b Wolfram$0321791 701 $aFilev$b Dimitar P.$f1959-$01612237 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910767568403321 996 $aInformatics in Control, Automation and Robotics$94333437 997 $aUNINA