Intelligent Autonomous Systems: IAS-5Y. Kakazu, M. Wada, T. Sato IOS Press, 1998 - 799 lappuses This book contains scientific and engineering activities of the fifth international conference of Intelligent Autonomous Systems (IAS-5). The exploration for automatic systems has much attention over the centuries and created attractive research activities. The Intelligent and Autonomous systems are the current trend toward fully automatic systems that can adapt to changes in their environment. The purpose of the fifth IAS conference is to provide an opportunity for the international community of researchers in the field of autonomous systems as well as architectures, tools, components, techniques, and new IAS design methodologies. The emphasis will be on science and technology for autonomous systems working in a complex environment. |
Saturs
Ultrasonic Range Sensor with Phase Sensitive Detection and Variable Transmission | 2 |
Obstacle Detection from Motion Using a Hypothesis and Test Approach | 13 |
An Adaptive Sensor Network System for Complex Environments M Kohno | 22 |
Iterative Transportation by Cooperative Mobile Robots in Unknown Environment | 30 |
Aviation Knowledge Based Multiple Mobile Robot Systems Consideration | 38 |
Emergent Cooperative Behavior for Multirobot Systems E Pagello A DAngelo | 45 |
Griffith Universitys Creation L Vlacic | 53 |
Filtering in a Unit Quaternion Space for ModelBased Object Tracking A Ude | 62 |
Expression of Emotion and Intention by Robot Body Movement T Nakata T Sato | 352 |
Variable Template Matching for Autonomous Mobile Robot with Hierarchical | 368 |
A Motion Analysis of the Bunraku Puppet Based on KM₂OLangevin Equation with | 384 |
Object Oriented BeNet Programming for DataFocused BottomUp Design | 399 |
Its Concept and Realization T Sato and T Mori | 415 |
Robust Path Planning in Unknown Environments T T Lee J T Jeng and W B Lai | 432 |
A Conflict Resolution Algorithm for Cellular Warehouse Problem K Hama | 446 |
Automated Model Verification of the International Space Station for Path Planning | 462 |
Terrain and Obstacle Detection for Walking Machines Using a StereoCameraSystem | 70 |
Hybrid Video for TeleRobotics J Baldwin A Basu and H Zhang | 78 |
Finding Human Based on the Interactive Sensing T Inamura M Inaba and H Inoue | 86 |
Recognition of Human Pointing Action Based on Color Extraction and Stereo | 93 |
Accuracy Tests and Sensor Fusion Using a Surgical Robot and an Optical Tracking | 101 |
Vanish Point Estimation Based on RBFN L Yang M Guo K He and B Zhang | 109 |
Depth Estimation Using Focusing and Zooming for High Speed Vision Chip | 116 |
A Visual Odometer for Autonomous Helicopter Flight O Amidi T Kanade | 123 |
The Study on the Model of the Autonomous Mobile Robot Based on Neural | 132 |
Autonomous Watercraft Navigation R Jarvis | 140 |
A Robotic Travel Aid for the Blind H Mori and S Kotani | 148 |
Outdoor Navigation of a Mobile Robot Using Natural Landmarks S Maeyama | 170 |
Evolutionary Computation | 187 |
PerceptionBased Localisation and Spatial Reasoning | 188 |
Time Variant NonLinear StateFeedback Control of a NonHolonomic Autonomous | 205 |
Intelligent Control for Autonomous Systems Based on NoiseResistant Hierarchical | 219 |
State Abstraction from Heterogeneous and Redundant Sensor Information T Yairi | 234 |
Learning Deterministic Policies in Partially Observable Markov Decision Processes | 250 |
Adaptive Electromyographic EMG Prosthetic Hand Control Using Reinforcement | 266 |
Learning of Temporal Sequences for Motor Control of a Robot System in Complex | 280 |
A Stochastic Exploration Strategy for Satisficing Reinforcement Learning | 296 |
Evolutionary Acquisition of Reinforcement Modules in Classifier Systems | 312 |
Design for Sequence of Behavior Rules in Multiple Robots Reinforcement Learning | 327 |
Cranes Control Using MultiAgent Reinforcement Learning S Arai K Miyazaki | 335 |
Radial Fusion of Ultrasonic and OmniDirectional Vision Sensor in Local | 479 |
Grasp Planning for a Multifingered HandArm Robot in Consideration of | 496 |
Distributed Control of a Group of Unifunctional Robots K Munawar | 511 |
xii | 519 |
Extended Reactive System Using Reconstructed Environmental Information | 535 |
Swarming Behavior of the Autonomous Block Agents for the Block Removal | 550 |
ChaosTheoryBased Analysis of Manufacturing RobotTeams P Levi M Schanz | 564 |
A MultiAgent System for Parallelizing Image Analysis Tasks M Lückenhaus | 579 |
Control of Star Fish Robot Using MultiAgent Programming System N Hondo | 595 |
Traffic Signal Control with Iterated Prisoners Dilemma Model K Yamada | 610 |
Collective Behaviors of Market Agents and their Emergent Properties | 623 |
Commitment in Agent Cooperation Applied to AgentBased Simulation S Hägg | 639 |
A Cooperation Method to Exploit Design Rationales for Agents T Yoshida | 654 |
Characterization of Biological Internal Dynamics by the Synchronization of Coupled | 670 |
Antlike Collective Sorting for Massive Data Analysis A Quantitative Study | 686 |
Social Complex Systems and its Management H Deguchi | 702 |
A Genetic Algorithm Taking Account of Characteristics Preservation for Job Shop | 711 |
A Vibration Control Approach | 727 |
Multiple Alignment of Amino Acid Sequences by Parallel Genetic Algorithm | 743 |
Extraction of Swarm Intelligence from Sequential Image of Amoeba by Evolutionary | 757 |
An Integrated Multimodal Control Architecture for Teleautonomous Systems | 775 |
Face Recognition among Others as an Approximately Affine Transformed | 791 |
Bieži izmantoti vārdi un frāzes
action adaptive algorithm applied approach architecture Artificial Artificial Intelligence attention control autonomous agents autonomous robots behavior camera classifier systems collision complex cooperative Crane2 defined described detected distance dynamic environment error estimation evaluation experiments factors function Genetic Algorithms goal human IBSI IEEE implemented initial Intelligent interaction IOS Press Japan Kakazu landmark Machine Learning magnetic bearing matching mechanism method microrobots module motion motion planning move multi-agent multi-agent system navigation neural network node object obstacle operation optical flow optimal output paper parameters path performance pheromone planning position problem Proc proposed Q-learning reinforcement learning reward robot system rotation rules selected sensor vector sensory input sequence shown in Figure shows signal simulation situation solving step stochastic strategy structure target task template trajectory Turing machine unit University of Karlsruhe update vehicle velocity virtual coordinates vision