دانلود کتاب Intelligent Autonomous Systems 17: Proceedings of the 17th International Conference IAS-17

فهرست مطالب :

Program Committee
Preface
Contents
Collaborative Robots
Evaluation of Software Solutions for Risk Assessment Focusing on Human-Robot Collaboration
1 Introduction
2 Risk Assessment
3 Study Procedure
3.1 Selection of Software Solutions Included in the Study
3.2 Study Realization
3.3 Definition of Evaluation Criteria
3.4 Evaluation System and Weighting
4 Evaluation Results
4.1 Expert Requirements
4.2 Suitability for HRC
5 Discussion
6 Conclusion
References
Goal Inference via Corrective Path Demonstration for Human-Robot Collaboration
1 Introduction
2 Overview of the Proposed System
2.1 Assumed Environment
2.2 Flow of the Proposed System
3 Learning from Corrective Path Demonstration
3.1 Position Samples Generation
3.2 Learning Relations with Surrounding Objects Probabilistically
4 Goal Inference
4.1 Partial Probability Map Calculation
4.2 Integration and Goal Inference
5 Experiment
5.1 Experimental Setting
5.2 Result
6 Conclusion
References
Skeleton-Based Action and Gesture Recognition for Human-Robot Collaboration
1 Introduction
2 Related Works
3 Methods
3.1 Pose Estimation
3.2 3D Pose Estimation
3.3 Action and Gesture Recognition
3.4 Ensemble Averaging of the Classifiers Predictions
4 IAS-Lab Collaborative HAR Dataset
5 Experimental Results
5.1 Pre-training on the NTU RGB+D Dataset
5.2 Fine-Tuning on the IAS-Lab Collaborative HAR Dataset
5.3 Ensemble Results Using Body and Hands Models
6 Conclusions
References
Benchmarking Variable-Stiffness Grippers
1 Introduction
2 State of the Art
3 Test Description
3.1 Adaptation Test
3.2 Finger Force Test
3.3 Grasp Strength Test
3.4 Gripper Payload Test
4 Experiments
4.1 Variable-Stiffness Gripper (VSGrip)
4.2 Piston Gripper (PistGrip)
4.3 Parallel Gripper (ParGrip)
4.4 Stiffness States
5 Results and Discussions
5.1 Object Grasping Test
5.2 Finger Force Test
5.3 Grasp Strength Test
5.4 Gripper Payload Test
6 Conclusions
References
Evaluation of Safe Reinforcement Learning with CoMirror Algorithm in a Non-Markovian Reward Problem
1 Introduction
2 Related Work
3 Preliminaries and Problem Setting
4 CoMirror Descent Search
5 Experiments
5.1 Experiment with Non-Markovian Reward and Cost
5.2 Result
5.3 Discussion
6 Conclusion
References
Validation of Shared Intelligence Approach for Teleoperating Telepresence Robots Through Inaccurate Interfaces
1 Introduction
2 Shared Intelligence
3 Methods
3.1 Participants
3.2 User\'s Interfaces
3.3 Telepresence Platform
3.4 Experiment
4 Results
5 Conclusion
References
Uncertainty Estimation for Safe Human-Robot Collaboration Using Conservation Measures
1 Introduction
2 Uncertainty and Safety in HRC
3 Methodology
3.1 Problem Statement
3.2 Classification of Uncertainties
3.3 Uncertainty Quantification
3.4 Use Case: Human Robot Collaboration
4 Experiments
4.1 Datasets
4.2 Conservation Properties and Type B Uncertainties
4.3 Validation, Evaluation and Results
4.4 Results
4.5 Mapping on Safety Limit
4.6 Limitations
4.7 Discussion
5 Conclusion and Outlook
References
Post-facto Misrecognition Filter Based on Resumable Interruptions for Coping with Real World Uncertainty in the Development of Reactive Robotic Behaviors
1 Introduction
2 Past Work
2.1 Control Structures for Reactive Task Definition
2.2 Robust Recognition Using Neural Networks and Data Filtering Algorithms
2.3 Attaining Zero Resumption Overhead
3 Post-facto Misrecognition Filter Based on Resumable Interruptions
4 Experiments
4.1 Setup
4.2 Results
5 Conclusions
References
Gestural and Touchscreen Interaction for Human-Robot Collaboration: A Comparative Study
1 Introduction
2 Background
3 Design Principles
4 Implementation
4.1 Graphical User Interface
4.2 Input
4.3 Logic
5 Experiment
5.1 Experimental Setup
5.2 Description of the Experiment
6 Results
6.1 Assessment of SLOTH Performance
6.2 Analysis of Questionnaires
6.3 Time Analysis
7 Conclusions
References
Development and Evaluation of Fiber Reinforced Modular Soft Actuators and an Individualized Soft Rehabilitation Glove
1 Introduction
2 Methods
2.1 Soft Actuators
2.2 Dummy Finger and Hand
2.3 Rehabilitation Glove
2.4 Characterization of Soft Actuators with the Dummy Finger
2.5 Evaluation of Prototype Rehabilitation Glove
3 Results
3.1 Characterization of Actuators with the Dummy Fingers
3.2 Evaluation of Prototype Rehabilitation Glove
4 Discussion
5 Conclusion
References
Navigation
Minimum Displacement Motion Planning for Movable Obstacles
1 Introduction
2 Minimum Displacement Motion Planning
3 Algorithm for Minimum Displacement Motion Planning
3.1 (a) Planning Phase
3.2 (b) Refinement Phase
3.3 Optimality
4 Examples and Applications
4.1 Theoretical Examples
4.2 Applications
5 Related Work
6 Conclusion
References
Mixed Use of Pontryagin\'s Principle and the Hamilton-Jacobi-Bellman Equation in Infinite- and Finite-Horizon Constrained Optimal Control
1 Introduction
2 Preliminaries
2.1 Motivations for Using DIDO
3 Problem Formulation
4 Methodology
4.1 Application to State- Constrained Problem
4.2 Weak Solutions to HJB
5 Solving the Original Problem
5.1 Convergence to Original Infinite-Horizon Problem
5.2 Approximation of Original Finite-Time Problem
6 Example
6.1 AUV
7 Conclusions and Future Work
References
Comparing SONN Types for Efficient Robot Motion Planning in the Configuration Space
1 Introduction
2 Self-Organizing Neural Networks
3 Methodology
3.1 Model Selection
3.2 Connection Reduction
4 Evaluation
4.1 Qualitative Analysis
4.2 Parameter Setting
4.3 Comparison of the Different Models
4.4 Path Analysis
5 Conclusion
References
Physics-Based Motion Planning of a Fruit Harvesting Manipulator for Pushing Obstacles in a Cluttered Environment
1 Introduction
2 Overview of the System
2.1 Work
2.2 Robot
2.3 System Flow
3 Physics-Based Motion Planning
3.1 Exploration in the Configuration Space
3.2 Physics Simulation
3.3 State Validity Check
3.4 Motion Selection and Reconstruction
4 Consideration of an Evaluation Index
5 Evaluation
5.1 Method
5.2 Conditions
5.3 Results
6 Conclusion
References
A Dynamics-Aware NMPC Method for Robot Navigation Among Moving Obstacles
1 Introduction
2 Problem Formulation
3 The Proposed NMPC Approach
4 Collision Avoidance
4.1 Preliminaries
4.2 Avoidable Collision States
4.3 Use of the ACS Condition in the NLP
5 Simulations
5.1 Static Environments
5.2 Dynamic Environments
6 Conclusion
References
Benchmark of Sampling-Based Optimizing Planners for Outdoor Robot Navigation
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Evaluation Metrics
3.2 Evaluation Methodology
4 Experiments
4.1 Scenarios and Evaluation Setup
4.2 Interpreting Results Metric-Wise
4.3 Combining the Metrics for Absolute Ranking
5 Conclusion
References
3D Traversability Analysis in Forest Environments Based on Mechanical Effort
1 Introduction
2 System Architecture
3 Experimental Evaluation
4 Results and Discussion
5 Conclusion
References
Two-step Planning of Dynamic UAV Trajectories Using Iterative -Spaces
1 Introduction
2 Related Work
3 Method
3.1 -Space Definition
3.2 Iterative -Spaces
3.3 Application to UAV Trajectory Planning
4 Evaluation
5 Conclusion
References
End-to-End Path Estimation and Automatic Dataset Generation for Robot Navigation in Plant-Rich Environments
1 Introduction
2 Related Work
2.1 Traversable Region and Path Detection
2.2 Automatic Data Annotation
3 Overview of the Proposed Method
4 Automatic Data Generation by Trajectory Estimation
5 Path Estimation
6 Robot Control Based on the Estimated Path
7 Experiments
7.1 Training with the Automatically Generated Dataset
7.2 Navigation
8 Conclusions and Future Work
References
Localization on Indoor Topological Maps—SCAM: Scale-Compatible Adaptive Monte-Carlo Localization
1 Introduction
2 Background and Related Work
3 SCAM: Scale-Compatible Adaptive Monte-Carlo Localization
3.1 Prediction
3.2 Observation Update
4 Experimental Results and Discussion
4.1 Experimental Hardware and Set-up
4.2 Localization on Hand-Drawn Maps
4.3 Consistency Checks of Localization Results
4.4 Applicability of SCAM
4.5 Increased Initial Pose Error Tolerance
5 Conclusion
References
Perception
Hyperspectral 3D Point Cloud Segmentation Using RandLA-Net
1 Introduction
2 Related Work
3 The Hyperspectral Semantic Street Scene Dataset
4 Semantic Classification with RandLA-Net
4.1 Pre-filtering
4.2 RandLA-Net Implementation and Parameterization
5 Evaluation
6 Conclusion and Outlook
References
3D Semantic Scene Perception Using Distributed Smart Edge Sensors
1 Introduction
2 Related Work
3 Method
3.1 Smart Edge Sensor Hardware
3.2 Single-View Embedded Semantic Perception
3.3 Multi-modal Semantic Point Cloud Fusion
3.4 3D Semantic Mapping
3.5 3D Human Pose Estimation with Occlusion Feedback
4 Evaluation
4.1 Implementation Details
4.2 Quantitative Results
4.3 Qualitative Results
4.4 Run-time Analysis
5 Conclusion
References
Collision Warning by Rotating 2D LiDAR for Safe Crane Operation
1 Introduction
2 Sensor System Development and Preliminary Tests
3 Collision Warning
3.1 Approach
3.2 Determining the Condition on Scanning Angular Velocity scan
3.3 Determining Safety Margin rmargin
3.4 Determining Safety Margin for Collision of Moving Load and Static Obstacles
4 Experiments in Simulation
4.1 Crane and Sensor Simulator
4.2 Verifying Collision Warning in Simulation
5 Preliminary Real-World Experiments
5.1 Experimental Setup
5.2 Results
6 Conclusions and Future Work
References
Semantic Classification in Uncolored 3D Point Clouds Using Multiscale Features
1 Introduction
2 Random Forest Classification
2.1 Parameters in Our RF Implementation
2.2 Performance Evaluation Strategy
2.3 Handling of Highly Imbalanced Data
3 Tools and Datasets
4 Semantic Classification
4.1 Finding the Optimal Neighborhood Size
4.2 Definition of Multiscale Features
4.3 Related Work: Deep Learning for 3D Point Cloud Classification
5 Results and Comparison
5.1 Classification Results
5.2 Run Times
6 Conclusions and Future Work
References
On the Evaluation of RGB-D-Based Categorical Pose and Shape Estimation
1 Introduction
2 Related Work
3 Evaluation Protocol
3.1 Problem Definition
3.2 Metrics
3.3 Datasets
4 Experiments
5 Limitations
6 Conclusion and Outlook
References
On Hand-Eye Calibration via On-Manifold Gauss-Newton Optimization
1 Introduction
2 Mathematical Background
3 Proposed Method
4 Experimental Results
4.1 Simulation Results
4.2 Real World Data
5 Conclusion
References
YOLOPose: Transformer-Based Multi-object 6D Pose Estimation Using Keypoint Regression
1 Related Work
1.1 RGB Object Pose Estimation
1.2 Learned PnP
2 Method
2.1 Multi-object Keypoint Regression as Set Prediction
2.2 Keypoints Representation
2.3 RotEst
2.4 Loss Function
2.5 Model Architecture
3 Evaluation
3.1 Dataset
3.2 Metrics
3.3 Hyperparameters
3.4 Results
3.5 Inference Time Analysis
4 Ablation Study
4.1 Effectiveness of Keypoints Representations
4.2 Effectiveness of RotEst
5 Discussion & Conclusion
References
People Tracking in Panoramic Video for Guiding Robots
1 Introduction
2 Related Work
2.1 People Detection
2.2 People Tracking
3 Methodology
3.1 Baseline
3.2 The TILES Approach
3.3 The ROI Approach
3.4 People Tracking in Panoramic Videos
4 Experimental Evaluation
4.1 Experimental Setup
4.2 Evaluation Metrics
4.3 Results and Discussions
5 Conclusions and Future Work
References
Clustering-Based Refinement for 3D Human Body Parts Segmentation
1 Introduction
2 Related Works
2.1 Human Parsing
2.2 3D Human Parsing
2.3 Clustering Algorithms and Applications
3 Clustering-Based Algorithm for 3D Segmentation Refinement
3.1 Human Body Clusters Detection
3.2 Feature Extraction and Graph Construction
3.3 Segmentation Label Refinement
4 Experiments
4.1 Performance on Synthetic Data
4.2 Performance on Real Data
5 Conclusions
References
Autonomous Exploration for 3D Mapping Using a Mobile Manipulator Robot with an RGB-D Camera
1 Introduction
2 Related Works
3 Overview of the Proposed Exploration
4 Frontier Observation Map Generation
4.1 Frontier Detection
4.2 Frontier Observation Map Generation
5 Determination of Travel Destination and Arm Posture Control
5.1 Determination of Travel Destination
5.2 Moving to the Destination and Controlling the Arm Posture
6 Verifications
6.1 Verification Scenarios
6.2 Results and Discussion
7 Conclusion
References
Reaching Motion Planning with Vision-Based Deep Neural Networks for Dual Arm Robots
1 Introduction
2 Approach to Generate Target Object Images
3 Instance Segmentation Based on Mask R-CNN ch31Kaimingsps2017
4 Reaching Order Classifier Based on CNN
4.1 Architecture of CNN
4.2 Network Training
5 Reaching Motion Planning for Dual Arm Robot
5.1 Overview of Reaching Motion Control
5.2 Grasping Classifier Based on CNN
5.3 Reaching Motion Planner Based on CNN
6 Reaching Experiments for Multiple Objects
6.1 Settings
6.2 Results
7 Conclusions
References
Kinematic Calibration of a Collaborative Robot by a Marker Based Optical Measurement Procedure
1 Introduction
2 Hardware, Data Aquisition and Algorithms
2.1 Experimental Setting and Preparation
2.2 Camera Calibration
2.3 Referencing of the Coordinate System of the Robot
2.4 Measurement of Kinematics and Detection of Static Phases
3 Experiments
3.1 Determination of Joint Axes and DH-parameters
3.2 Moving the Robot Based on Optimized Modified DH-parameters
4 Results
5 Discussion
6 Conclusion and Future Work
References
Randomized Robotic Visual Quality Inspection with In-hand Camera
1 Introduction
2 Use-Case
3 Motion Planning
4 Experimental Evaluation
5 Discussion
6 Conclusion
References
Articulated Objects: From Detection to Manipulation—Survey
1 Introduction
2 Detection and Segmentation of Articulated Objects
3 Estimation of Joint Parameters and Configuration
4 Robot Manipulation with Articulated Objects
4.1 Manipulation for Estimation of Kinematic Parameters
4.2 Manipulation for Evaluation of Joint Parameters
5 Conclusion
References
Learning
Learning Sequential Latent Variable Models from Multimodal Time Series Data
1 Introduction
2 Related Work
3 Methodology
3.1 Sequential Latent Variable Models
3.2 Multimodal Sequential Latent Variable Models
4 Experiments
4.1 Datasets
4.2 Network Architecture and Training
4.3 Image Prediction Experiments
4.4 Regression Experiments
5 Conclusions and Future Work
References
Pushing the Limits of Learning-Based Traversability Analysis for Autonomous Driving on CPU
1 Introduction
2 Related Work
3 Method
3.1 Grid-Based Space Representation
3.2 Geometric-Based Feature Extraction
3.3 Appearance-Based Feature Extraction
3.4 Point Clouds Integration
3.5 Point Sorting into the Grid Cells
3.6 Outliers Filter
4 Experiments
4.1 SemanticKITTI Dataset
4.2 Traversability Ground Truth Extraction
4.3 Training and Parameters
4.4 Metrics
4.5 Comparison
4.6 Ablation Study
5 Conclusions
References
Sensor-Based Navigation Using Hierarchical Reinforcement Learning
1 Introduction
2 Related Work
3 Our Approach
3.1 Navigation Task
3.2 Preliminaries
3.3 Agent Structure
3.4 Reward Function
3.5 Reinforcement Learning
4 Experimental Evaluation
4.1 Setup
4.2 Top Layer Training
4.3 Comparative Evaluation
4.4 Transfer to Real-World
5 Conclusion
References
Traction Optimization for Robust Navigation in Unstructured Environments Using Deep Neural Networks on the Example of the Off-Road Truck Unimog
1 Introduction
2 Related Work
3 Off-Road Robot Unimog
4 Traction Opimization Concept Based on Surface Data
4.1 Off-Road Surface Data
4.2 Traction-Dependent Vehicle Configuration
5 Semantic Segmentation
5.1 Network Training
6 Experiments
6.1 Performance on Dataset
6.2 Performance on Vehicle Data
7 Conclusion
References
Flattening Clothes with a Single-Arm Robot Based on Reinforcement Learning
1 Introduction
2 Problem Description
2.1 State
2.2 Actions
2.3 Rewards
3 Methodology
3.1 Visual Perception
3.2 Neural Network Structure for DDPG
3.3 Simulation
4 Experiments
5 Discussion
6 Conclusions
References
Robot Simulations
A Monte Carlo Framework for Incremental Improvement of Simulation Fidelity
1 Introduction
2 Related Work
3 Approach
3.1 Kernel Generation
3.2 Kernel Manager
4 Delivery Vehicle
5 Field Test
5.1 Design and Simulation
5.2 Description of the Field Trials
6 Results
6.1 Kernels Non-identity Transfer Functions
6.2 Kernels with Identity Transfer Functions
7 Conclusions
References
Real2Sim or Sim2Real: Robotics Visual Insertion Using Deep Reinforcement Learning and Real2Sim Policy Adaptation
1 Introduction
1.1 Minimum Infrastructure and Prerequisites
1.2 Real2Sim or Sim2Real
1.3 Contributions
2 Task Description
3 Related Works
3.1 Robotics Insertion
3.2 Reinforcement Learning and Sim2real Adaptation
4 Our Approach
4.1 Overview
4.2 Reinforcement Learning for Insertion Action Learning
4.3 Real2Sim and Sim2Real Domain Adaptation
4.4 Task Mixing, Randomization, and Vectorization
5 Experiment
5.1 Experiment Overview
5.2 Task Design
5.3 Convergence of Reinforcement Learning on Tasks
5.4 Mixing Learning
5.5 Test in Real Scenario and Real2Sim
5.6 Benchmark Comparison
5.7 Discussion and Future Work
6 Conclusion
References
Towards Synthetic Data: Dealing with the Texture-Bias in Sim2real Learning
1 Introduction
1.1 Related Work
1.2 Contributions
2 Method
2.1 Datasets
2.2 Semantic Segmentation Neural Network
3 Experimental Validation
3.1 Texture-Bias in Semantic Segmentation Model
3.2 Sim2real Approach for Leaf Segmentation in Robotic Harvesting
4 Conclusion
References
On Scene Engineering and Domain Randomization: Synthetic Data for Industrial Item Picking
1 Introduction
2 Related work
2.1 Cut and Paste
2.2 Domain Adaptation
2.3 Domain Randomization
3 Method
3.1 Data Generation
3.2 Training and Evaluation
4 Experiments
4.1 Base Scenes
4.2 Ablation Study: Scene Variations
5 Conclusions
References
Hybrid Intelligence for Visual Identity Design: A Case Study
1 Introduction
2 Related Work
3 Method
3.1 A Hand-Guided Letter “C”
3.2 An Artificially-Generated Letter “C”
3.3 A Brain Surgery Trajectory by RONNA, CRTA’S Flagship Robotic Innovation
4 Conclusion
References
Multi-robot Systems
State-Aware Layered BTs—Behavior Tree Extensions for Post-Actions, Preferences and Local Priorities in Robotic Applications
1 Introduction
2 Past Work
2.1 Basic Syntax of Behavior Trees
2.2 Benefits of Behavior Trees Over Other Control Architectures
2.3 Behavior Tree Extensions for Robotics
2.4 Approaches for Handling Statelessness
3 State-Aware Layered Behavior Trees
3.1 Introducing State Operators
3.2 Introducing Priority Layers and Expanding the State Operator
4 Case Analysis
5 Conclusions
References
Multi-agent Coordination Based on POMDPs and Consensus for Active Perception
1 Introduction
2 Preliminaries
2.1 Partially Observable Markov Decision Processes
2.2 Consensus Protocol
3 Methodology
3.1 Measured Variables
3.2 Simulating Multiple Missions with Sensing Failures
4 Multi-agent Framework Based on the POMDP and Consensus
4.1 Single-agent POMDP Model
4.2 Novelty-Biased Consensus
4.3 Integration of Novelty-Biased Consensus and POMDPs
5 Results
5.1 Experimental Evaluation
6 Conclusion and Future Work
References
KI5GRob: Fusing Cloud Computing and AI for Scalable Robotic System in Production and Logistics
1 Introduction
2 State of the Art
2.1 Cloud Robotics
2.2 Cloud Based Motion Planning and Control
2.3 Robotic Manipulation and Transfer Learning
2.4 Sensor Data Processing
3 The KI5GRob Approach
3.1 Decoupled Robot Hardware
3.2 Microservice-Based Software Architecture
3.3 Software Deployment in Hybrid Environment
3.4 Motion Control as a Service
3.5 Robotic Manipulation
3.6 Multi-modal Perception
3.7 Transfer Learning
4 Conclusion
References
A Correlated Random Walk Model to Rapidly Approximate Hitting Time Distributions in Multi-robot Systems
1 Introduction
2 Problem Settings
3 Correlated Random Walk Computations
4 Verification by ARGoS
5 Discussion and Conclusions
References
Synthesis and Monitoring of Complex Tasks for Heterogeneous Robots in an Industry 4.0 Scenario
1 Introduction
2 Synthesis of State-Charts for Complex Tasks
2.1 State-Chart Generator Layer
2.2 State-chart and Observer Layer
2.3 Primitives Layer
3 Industry 4.0 Application
3.1 Observer
3.2 Primitives of the RB-Kairos 5
3.3 Primitives of the KUKA LBR Iiwa 14
3.4 Primitives ABB YuMi
4 Experimental Results
4.1 RB-Kairos
4.2 KUKA LBR Iiwa 14 and ABB Yumi for the Assembly Process
5 Conclusion
References
Automated Driving and Traffic
Training Traffic Light Behavior with End-to-End Learning
1 Introduction
2 Related Work
2.1 Conditional Imitation Learning
2.2 Reinforcement Learning
3 Method
3.1 Network Architecture
3.2 PID Controller
4 Experimental Setup
4.1 Simulation Environment
4.2 Dataset
4.3 Evaluation
5 Results
5.1 Learning Curves
5.2 NoCrash results
6 Conclusion
References
On Demand Ride Sharing: Scheduling of an Autonoumous Bus Fleet for Last Mile Travel
1 Introduction
2 Related Work
3 Bus Fleet Scheduling
3.1 Representation of Street Network
3.2 Graph Generation
3.3 Optimization of Bus Trips Using Simulated Annealing
3.4 Construction and Solution of the ILP Problem
4 Experiments
5 Conclusion
References
Lane Change Classification with Neural Networks for Automated Conversion of Logical Scenarios
1 Introduction
2 Problem Description
3 Proposed Neural Network Models for Lane Change Classification
4 Training and Evaluation
4.1 Dataset Generation
5 Conclusion
References
Trajectory Analysis in a Lane-Based UAS Traffic Management System
1 Introduction and Background
1.1 Example ATOC Requirements by Utah Department of Transportation
2 Lane-Based Monitoring
2.1 NAB Modeling
2.2 NAB Analysis
3 Next Steps
References
Advanced and Intelligent Control Design for Underwater Robots
Acoustical Underwater Localization of a Remotely Operated Vehicle in Mariculture
1 Introduction
2 Overview of Net Pen Inspection and Acoustical Underwater Localization Methods
3 Vehicles
3.1 Remotely Operated Underwater Vehicle (ROV)
3.2 Autonomous Surface Vehicle (ASV)
4 Underwater Acoustical Localization System
4.1 Acoustics Specifications
4.2 Mount Design
4.3 UWGPS-ROS2 Interface
5 Results
6 Conclusion
References
A Virtual Online Simulator Design for the Docking of Unmanned Underwater Vehicle
1 Introduction
2 Overall Framework of Virtual Online Simulator
3 Unit Design for Virtual Online Simulator
3.1 Virtual Docking Scene Design
3.2 Motion and Control Unit Design
3.3 Environmental Perception and Localization Unit Design
3.4 The Docking Navigation Unit Design
3.5 The Data Monitoring Unit Design
4 Application for the Underwater Tasks of UUV
4.1 The Motion Verification of UUV
4.2 The Underwater Docking of UUV
5 Conclusion
References
Simulation Environment for Underwater Vehicles Testing and Training in Unity3D
1 Introduction
2 Approach
3 Simulation Tests
4 Conclusions
References
Sliding Mode Control for Underwater Multi-DoF Hydraulic Manipulator
1 Introduction
2 Problem Formulation and Dynamic Models
3 Controller Design
4 Experiment Verification
4.1 Experimental Setup
4.2 Experimental Result
5 Conclusion
References
Path Following for Underwater Inspection Allowing Manoeuvring Constraints
1 Introduction
2 Modelling of the ROV
3 Path Planning
4 Path Following
4.1 Objective
4.2 Guidance Law
5 Control System
6 Simulation
6.1 Setup
6.2 Lawnmower Pattern
6.3 Inspection Pattern
7 Discussion
8 Conclusion
References
Autonomous Robots in Warehouse Automation
Path Planning of Multiple Automatic Guided Vehicles with Tricycle Kinematics Considering Priorities and Occupancy Time Windows
1 Introduction
2 Path Planning of Multiple AGVs
2.1 Road Occupancy Check
2.2 Node Occupancy Check
2.3 Road and Node Occupancy Times for Tricycle Kinematics
2.4 Waiting Time Due to Occupancy
3 Experimental Set-up
3.1 Control of Multiple AGVs Along the Desired Paths
4 Results
4.1 Test Case 1: All the AGVs with the Same Speeds (Simulation)
4.2 Test Case 2: AGVs with Different Speeds (Simulation)
4.3 Test Case 3: AGVs with Different Speeds Considering Tricycle Kinematics (Simulation)
4.4 Test Case 4: AGVs with Different Speeds Considering Tricycle Kinematics (Real AGVs)
5 Conclusions
References
Cyber-Physical Platform with Miniature Robotic Vehicles for Research and Development of Autonomous Mobile Systems
1 Introduction
2 Small-Scale Physical Model of the Environment
2.1 Model of a Town
2.2 Model of an Industrial Hall
3 Miniature Robotic Vehicles
3.1 Miniature Wheeled Mobile Robot with Differential Drive
3.2 Miniature Automated Guided Vehicle
4 Global Vision-Based Object Tracking
5 Soft Sensors
6 Conclusions
References
Autonomous Hierarchy Creation for Path Planning of Mobile Robots in Large Environments
1 Introduction
2 Modeling of the Large Environment Using H-Graph
2.1 H-Graph Definition
2.2 Conditions for Optimality
3 Autonomous H-Graph Creation
3.1 Creating Occupancy Grid Map from the Floor Plan
3.2 Finding Narrow Passages, Bridge Nodes and Rooms
3.3 Creation of Higher Level Nodes
3.4 Finding Partial Paths
4 Simulation Results
4.1 H-Graph Analysis
4.2 H-Graph and Plain Graph Paths
4.3 Comparison of Partial Paths by E* and A*
5 Conclusion and Future Work
References
Improving the Flow in Multi-robot Logistic Systems Through Optimization of Layout Roadmaps
1 Introduction
2 Problem Formulation
3 Roadmap Generation Inspired by Ant Colony Optimization
4 Approach Validation
5 Discussion, Conclusions, and Future Work
References
Author Index