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2007 IEEE/ASME International Conference on advanced intelligent Mechatronics, Pages 805-1210

作者： Abdol Hossein Aminaiee Majid Nili Ahmadabadi Robotics and AI Laboratory Control and Intelligent Processing Center of Excellence Department of Electerical and Computer Engineering University of Tehran

ISBN: (纸本)9781424412631;1424412633

Learning in multi-agent environments where each agent's action directly affects other agents would be an important matter and a complicated task. To reduce the learning time and simplifying the learning process, it is suitable to learn individual skills and then provide cooperation and coordination utilizing the learned individual skills. In the approach proposed in this paper, agents benefit from their individual knowledge obtained in the individual learning phase to cooperate with other agents. Cooperative object pushing system is used as a testbed to our proposed method where cooperation and coordination between agents are needed in these systems. Agents independently learn to push the object to the target by using the proposed fuzzy reinforcement learning method. Agents attain their cooperative behaviors properly making use of the coded knowledge in the Q-table. Simulation and experimental results show that by interpreting the knowledge in the Q-table, agents can achieve high level behaviors with a high degree of cooperation.

关键词： Agents Learning Point contacts pushing variable contact

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Challenges in implementation of human-automation interaction models

Challenges in implementation of human-automation interaction...

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Mediterranean Conference on control and Automation (MED)

作者： Afreza Fereidunian Caro Lucas Hamid Lesani Matti Lehtonen Mikael Nordman Helsinki University of Technology Finland Imperial College London UK Center of Excellence for Control and Intelligent Processing School of ECE University of Tehran Iran Power Systems and High Voltage Engineering Laboratory Helsinki University of Technology Finland Researcher with the Power Systems and High Voltage Engineering Laboratory Helsinki University of Technology Finland

This paper presents the practical experience gained in the process of implementing the human-centered automation design methodology to an electric power utility management automation function, along with the analytical discussions on the implementation challenges. This report of implementation challenges and the discussions made on the methods devised to conquer those challenges lead to an alternative approach to the original approach to the well-known human-centered automation design model in the literature.

关键词： Design automation Human computer interaction Scholarships intelligent agent Power system modeling Process control intelligent control Design methodology Energy management Collaboration

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Adaptive autonomy: Smart cooperative cybernetic systems for more humane automation solutions

Adaptive autonomy: Smart cooperative cybernetic systems for ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Alireza Fereidunian Matti Lehtonen Hamid Lesani Caro Lucas Mikael Nordman Eslamshahr Barnch Islamic Azad University Iran Helsinki University of Technology Finland Imperial College London UK Center of Excellence for Control and Intelligent Processing University of Tehran Iran Power Systems and High Voltage Engineering Laboratory Helsinki University of Technology Finland

Smart cooperation of human and computer agents should be harmonized by adapting the automation level of the cybernetic systems to the changing environmental and peripheral situations. This paper presents an adaptive autonomy methodology that is based on an extension of a well-known human-automation interaction model, as well as the expert judgment technique and the performance shaping factors concept. The method is implemented to a power distribution automation system, and the results are discussed through a scenario-based approach. Then, the performance of the proposed methodology and the effectiveness of adaptive autonomy are shown by the wide span of the changes in the resulting automation levels. The trends of the automation levels are investigated versus the criticality of the situations and the automation stages. The application-oriented matters are also discussed to stress on the context-based nature of the human-centered automation models.

关键词： Cybernetics Scholarships Process control intelligent control Human computer interaction Power systems Voltage Power engineering and energy Design automation Computer peripherals

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

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Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Who moved?! An investigation into distribution of actions and behaviors in a multi agent system

Who moved?! An investigation into distribution of actions an...

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2006 IEEE International Conference on Mechatronics, ICM

作者： Daneshvar, Roozbeh Lucas, Caro Artificial Intelligence and Robotics Lab. Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence for Control and Intelligent Processing Faculty of Electrical and Computer Engineering University of Tehran and School of Intelligent Systems Tehran Iran

ISBN: (纸本)0780397134

In this paper we have considered systems in which agents communicate via their environment. In these systems the agents do not have direct and explicit communication with each other and instead they have implicit communication which is performed via the environment they are located in. Different kinds of environment elements are considered to be changed while on the other hand for all of these situations there is a change in the environment and this change means potential stimuli that fires behaviors of agents for future. It is considered that if we can find a track of changes in the environment and find the roots of a movement in an agent that is originally made by another agent of the group. © 2006 IEEE.

关键词： Multi agent systems

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Learning Individual Skills and Team Behaviors for Distributed Object Pushing

Learning Individual Skills and Team Behaviors for Distribute...

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IEEE International Conference on Mechatronics and Automation

作者： Hossein Aminaiee Majid Nili Ahmadabadi Control and Intelligent Processing Center of Excellence Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we discuss a learning approach to distributed object pushing. In the proposed approach, first the required individual skills for single-robot object pushing are learned using a fuzzy reinforcement learning method. Then, the robots learn how to coordinate their actions to push the object to the desired configuration cooperatively in a distributed manner. The proposed team-level learning benefits from the knowledge, which is in the form of a Q-table, that the agent has gained in its individual learning phase by a special design of reward signal and state-action representation. Each robot learns a threshold on its Q-value using a single state reinforcement learning method and pushes the object when the Q-value of its best action in the current state is above this threshold. The reward signal is designed based on the robots' Q-tables and no external critic is needed for learning cooperation. Simulation results show that the robots learn their individual skills and a cooperation protocol to push the object cooperatively

关键词： Robot kinematics Learning Protocols Robot control robotics and automation intelligent robots Signal design Mechatronics Process control intelligent control

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Who Moved?! An Investigation into Distribution of Actions and Behaviors in a Multi Agent System

Who Moved?! An Investigation into Distribution of Actions an...

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International Conference on Mechatronics (ICM)

作者： Roozbeh Daneshvar Caro Lucas Artificial Intelligence and Robotics Laboratory Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence for Control and Intelligent Processing Faculty of Electrical and Computer Engineering School of Intelligent Systems IPM University of Tehran Tehran Iran

关键词： Artificial intelligence intelligent robots Fires Tracking Process control Communication system control control systems intelligent control Optical computing intelligent systems

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Two Dimensional Dynamic Manipulation of a Disc Using Two Manipulators

Two Dimensional Dynamic Manipulation of a Disc Using Two Man...

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IEEE International Conference on Mechatronics and Automation

作者： Borhan Beigzadeh Majid Nili Ahmadabadi Ali Meghdari Center of Excellence in Design Robotics and Automation (CEDRA) ME Department Sharif University of Technology Iran Control and Intelligent Processing Center of Excellence Robotics and AI Laboratory ECE Department University of Tehran Iran

In this paper, we analyze a dynamic object manipulation (DOM) problem; throwing upward and catching a disc using two planar manipulators. The manipulators control the disc xy position dynamically and use its angular movement as a free parameter. We use a simple model for the system. Each manipulator has three links and three active joints. In addition, the disc has considerable radius and mass. Therefore, we design a simple mechanism in the palm to damp a portion of the impact

关键词： Manipulator dynamics robotics and automation intelligent robots Leg Grippers Motion planning Humans Design automation Process control Force control

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A New Architecture to Execute CAs-Based Path-Planning Algorithm in Mobile Robots

A New Architecture to Execute CAs-Based Path-Planning Algori...

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International Conference on Mechatronics (ICM)

作者： Adel Akbarimajd Caro Lucas Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Iran Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran Iran

Several path-planning algorithms for mobile robots have been introduced. Proper architectures for mobile robots to implement the path-planning algorithms are also of interest. If the mobile robots are to perform complicated tasks including complex sensing and planning operations and have accepted performance, must be autonomous: capable of acquiring information and performing tasks without programmatic intervention. In this paper we employ a layered architecture for mobile robots to perform our previously introduced cellular automata based path planning technique. It employs an abstraction approach which makes the complexity manageable. The architecture has an important feature which is its internal artifacts; it has some beliefs about the world and these beliefs are represented in artifacts and most actions are planned and performed with respect to these artifacts

关键词： Path planning Mobile robots Robot kinematics Content addressable storage Lattices Computer architecture Orbital robotics Automata intelligent sensors robotics and automation

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Heterogeneous and Hierarchical Cooperative Learning via Combining Decision Trees

Heterogeneous and Hierarchical Cooperative Learning via Comb...

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2006 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Masoud Asadpour Majid Nili Ahmadabadi Roland Siegwart Autonomous Systems Laboratory Ecole Polytechnique Federale de Lausanne Switzerland Control and Intelligent Processing Center of Excellence ECE Department University of Tehran Iran Autonomous Systems Laboratory Swiss Federal Institute of Technology Zurich Switzerland

Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the available knowledge for a reinforcement learning (RL) agent. In this paper, we address two approaches to combine and purify the available knowledge in the abstraction trees, stored among different RL agents in a multi-agent system, or among the decision trees learned by the same agent using different methods. Simulation results in nondeterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance

关键词： Decision trees Multiagent systems Learning systems intelligent robots Humans Process control control systems intelligent agent intelligent control intelligent structures

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