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检索条件"机构=Robotics and Intelligent Systems Laboratory Department of Elecnical and Computer Engineering"

共 173 条记录，以下是121-130 订阅

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Entire region filling in indoor environments using neural networks

Entire region filling in indoor environments using neural ne...

引用

World Congress on intelligent Control and Automation (WCICA)

作者： Chaomin Luo S.X. Yang M. Meng Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Entire region filling is a special type of robot path planning strategy that requires the mobile robot to cover every part of the whole workspace, which has many applications such as cleaning robots, vacuum cleaners, painter robots, land mine detectors, lawn mowers, and window cleaners. In this paper, a novel biologically inspired neural network approach is proposed for entire region filling with obstacle avoidance of a mobile cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally efficient. It can deal with an unstructured environment with irregular obstacles. The effectiveness of the proposed model is demonstrated by simulation results.

关键词： Filling Indoor environments Neural networks Mobile robots Equations Cleaning Landmine detection Neurons Computational modeling Path planning

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Cooperative Q-learning with heterogeneity in actions

Cooperative Q-learning with heterogeneity in actions

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IEEE International Conference on systems, Man and Cybernetics

作者： S.M. Reza MirFattah M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Cooperation in learning improves the speed of convergence and the quality of learning. Special care is needed when heterogeneous agents cooperate in learning. It is discussed that, cooperation in learning may cause the learning process to diverge if heterogeneity is not handled properly. In this paper, it is assumed that two heterogeneous Q-learning agents cooperate to learn. The heterogeneity is assumed in their action order (and not in their action set). A Q-learning-based method is introduced for the agents to learn the mapping among their actions. It is shown that, the agents are able to learn this mapping while cooperating in learning. Some simulation results are reported to show the effectiveness of the proposed method.

关键词： intelligent agent intelligent robots Humans Telecommunication control Artificial intelligence Laboratories intelligent systems Physics Mathematics Convergence

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A new approach to credit assignment in a team of cooperative Q-learning agents

A new approach to credit assignment in a team of cooperative...

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IEEE International Conference on systems, Man and Cybernetics

作者： A. Harati M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Q-learning is widely used in many multi agent systems. In most cases, a separate critic is considered for qualifying each individual agent behavior or it is assumed that the critic is completely aware of effects of all agents' actions on the team qualification. But, in many cases, the role of each team member in the group performance is not known. In order to distribute a common credit among the agents, a suitable criterion must be provided to estimate the role of each agent in the team performance and to judge if an agent has done a wrong action. In this paper two such criteria, named certainty and expertness, for a team of agents with parallel tasks are introduced. In addition, two methods for reinforcing the agents based on the proposed measures are provided. Some simulation results are also reported to show the effectiveness of the proposed measures and methods.

关键词： intelligent agent Learning systems Multiagent systems intelligent sensors Artificial intelligence Laboratories intelligent systems intelligent robots Physics Mathematics

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An extension of weighted strategy sharing in cooperative Q-learning for specialized agents

An extension of weighted strategy sharing in cooperative Q-l...

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International Conference on Neural Information Processing

作者： S.M. Eshgh M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.

关键词： intelligent robots intelligent agent Process control Control systems intelligent control Laboratories intelligent systems Physics Mathematics Entropy

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Certainty and expertness-based credit assignment for cooperative Q-Learning agents with an AND-type task

Certainty and expertness-based credit assignment for coopera...

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International Conference on Neural Information Processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback intelligent agent intelligent robots Process control Control systems intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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A new model of the visual feedback coordinate transformation in humans based on disturbance noise and feedback error that accounts for time delays

A new model of the visual feedback coordinate transformation...

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IEEE International Workshop on intelligent Robots and systems (IROS)

作者： E. Oyama K.F. MacDorman T. Maeda S. Tachi A. Agah Science and Technology (AIST) National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Intelligent Robotics Laboratory Graduate School of Systems Engineering Wakayama University Wakayama Japan School of Engineering University of Tokyo Bunkyo Tokyo Japan Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

An essential factor in understanding the motor learning capability of humans, is the coordinate transformation learning of the visual feedback controller. Although a number of learning models for the visual feedback controller have been proposed, none has been able to establish a definitive approach. In our previous work, we have suggested a learning model that uses disturbance noise and the feedback error signal to learn the human visual feedback controller's coordinate transformation. However, the model does not fully consider the time delay in the visual feedback control loop. This paper presents a modified learning model taking into account the time delay and the convergence properties of the model. Numerical simulations are presented to illustrate the effectiveness of the proposed approach.

关键词： Humans Delay effects Adaptive control Error correction Biological system modeling Feedback control Convergence Kinematics intelligent robots Velocity control

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Using Motives and Artificial Emotions for Prolonged Activity of a Group of Autonomous Robots

Using Motives and Artificial Emotions for Prolonged Activity...

引用

2001 AAAI Fall Symposium

作者： Michaud, François Robichaud, Etienne Audet, Jonathan LABORIUS - Research Laboratory on Mobile Robotics and Intelligent Systems Department of Electrical and Computer Engineering Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada

To operate over a long period of time in the real world, autonomous mobile robots must have the capability of recharging themselves whenever necessary. In addition to be able to find and dock into a charging station, robots must be able to decide when and for how long to recharge. This decision is influenced by the energetic capacity of their batteries and the contingencies of the environment. To deal with this temporality issue and using research evidences from the field of psychology, this study investigates the use of motives and artificial emotions to regulate the recharging need of autonomous robots. © 2001 AI Access Foundation. All rights reserved.

关键词： Robots

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Finding objects in a 3D environment by combining distance measurement and color indexing

Finding objects in a 3D environment by combining distance me...

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IEEE International Conference on Image Processing

作者： A. Koschan SunHo Lee M.A. Abidi Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee USA

A new method is presented for the localization and recognition of three-dimensional objects using color information. In the first processing step, we estimate depth information by either applying a chromatic block matching method to color stereo images or acquiring a range image from a laser scanner. Second, the computed depth maps are segmented to distinguish between the image background and the objects that should be recognized. Assuming that the segmented regions represent single objects in the three-dimensional scene, feature vectors are generated based on color histograms. The Euclidean distance is used as well as the scalar product to measure the similarity between the feature vectors computed from the color image and the feature vectors stored in a database.

关键词： Distance measurement Indexing Color Histograms Image recognition Image segmentation Colored noise Layout Lighting intelligent robots

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Light signaling for social interaction with mobile robots

Light signaling for social interaction with mobile robots

引用

IEEE International Symposium on Computational Intelligence in robotics and Automation (CIRA)

作者： F. Michaud Minh Tuan Vu Laborius-Research Laboratory on Mobile Robotics and Intelligent Systems Department of Electrical and Computer Engineering Université de Sherbrook Sherbrooke QUE Canada

To give autonomous mobile robots some kind of "social intelligence", they need to be able to recognize and interact with other agents in the world. This paper describes how a light signaling device can be used to identify another individual and to communicate simple information. By having the agents relatively close to each other, they share the same perceptual space, which allows them to sense or deduce implicit information concerning the context of their interaction. Using a vision- and sonar-based Pioneer I robot equipped with a colored-light signaling device, experimental results demonstrate how the robot. can interact with a human interlocutor in a ball-passing game.

关键词： Mobile robots intelligent robots intelligent agent Human robot interaction Humanoid robots Robot sensing systems Orbital robotics Animals Robot vision systems Protocols

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Mobile robot that can read symbols

Mobile robot that can read symbols

引用

IEEE International Symposium on Computational Intelligence in robotics and Automation (CIRA)

作者： F. Michaud D. Letourneau LABORIUS-Research Laboratory on Mobile Robotics aid Intelligent Systems Department of Electrical and Computer Engineering Université de Sherbrook Sherbrooke QUE Canada

To give an autonomous robot the ability to read symbols, we need to integrate character recognition techniques with methods to position the robot in front of the symbol, to capture the image that will be used in the identification process, and to validate the overall approach on a robot. Our goal is to address the different aspects required in making a mobile robotic platform recognize symbols placed in real world environment, using current hardware and software capabilities. Validated on a Pioneer 2 robot, the approach described in this paper uses colors to detect symbols, a PID controller to position the camera, simple heuristics to select image regions that could contain symbols, and finally a neural system for symbol identification. Results in different lighting conditions are presented.

关键词： Mobile robots intelligent robots Robot vision systems Hardware Cameras Roads Sonar Mobile computing intelligent systems Character recognition

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