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

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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FDTD model of photonic crystal laser with four-energy-level gain media

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Jisuan Wuli/Chinese Journal of Computational Physics 2007年第4期24卷 452-456页

作者： Wang, Hong Ouyang, Zhengbiao Han, Yanling Meng, Qingsheng Center of THz Technology Shenzhen University Shenzhen 518060 China College of Engineering and Technology Shenzhen University Shenzhen 518060 China School of Physical Electronics University of Electronic Science and Technology of China Chengdu 610054 China Mathematics and Physics Department China University of Geosciences Wuhan 430074 China State Education Commission Laboratory for Image Processing and Intelligent Control Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

A model of one-dimensional photonic crystal laser with four-energy-level gain media is presented. The model consists of Maxwell-Bloch equations coupled with rate equations of electronic population in a photonic crystal laser system. The discretization of coupled equations is accomplished with finite different time domain (FDTD). Spectra, lasing mode, lasing threshold and dynamics of the photonic crystal laser are obtained. The simulation shows good agreement with theoretical predictions.

关键词： Photonic crystals

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Sonar grid map building of mobile robots based on DSmT

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Information Technology Journal 2006年第2期5卷 267-272页

作者： Li, Xinde Huang, Xinhan Wang, Min Intelligent Control and Robotics Laboratory Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

The research of self-location and map building among some entirely unknown environments is an important and popular subject of intelligent mobile robots. Someone compared it to the problem of chicken and egg. But the application of Sonar sensors with some advantages such as cheapness, simple-operation and convenient-disposal resulted in uncertain and imprecise knowledge acquired. Aimed to this, a new method of information fusion recently developed from Bayesian theory and Dempster-Shafer theory was applied and on the base of experiment, according to sonar's metrical characteristic, the general basic belief assignment functions were constructed for modeling of static environment. Finally, grid map of 3D building on-line for mobile robot was used to compare from the real environment by the experiment. By analyzing the result of comparison, the validity of this method was fully verified. © 2006 Asian Network for Scientific Information.

关键词： Mobile robots

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A Comparison of the Effect of Sonar Grid Map Building Based on DSmT and DST

A Comparison of the Effect of Sonar Grid Map Building Based ...

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World Congress on intelligent control and Automation (WCICA)

作者： Xinde Li Xinhan Huang Min Wang Gang Peng Intelligent Control and Robotics Laboratory Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

Information acquired in map building presents characteristics of uncertainty, imprecision and even high conflict, especially in the course of building grid map using sonar. Here a new method of information fusion - DSmT (Dezert-Smarandache theory) recently developed from DST and Bayesian theory was introduced to apply to managing and dealing with this uncertainty. Aiming to static environment, classic DSm mathematic modal was established by constructing the general basic belief assignment function, so that the global evidence grid map is built by applying the classic DSm rule. Pioneer II virtual mobile robot served as an experiment platform. By comparing the numbers of acquiring-information spot, the quality of grid map building and the time in need of finishing the global map building, better fusion results than that with DST are gotten. Especially when dealing with high conflictive information from sonar grid map building, DSmT was more effective and successful than DST. In short, this study not only found a new method for building map under static environment, but also supplied with a theory foundation for us to further apply hybrid DSmT to dynamic unknown environment and multi-robots' building map together

关键词： Sonar Uncertainty intelligent robots Mobile robots Buildings Bayesian methods intelligent control Laboratories Mathematics Finishing

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Effects of a Dynamic Reference Frame in Mental Task Classification for EEG-Based Brain-Machine Interface

Effects of a Dynamic Reference Frame in Mental Task Classifi...

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IEEE Region 10 International Conference TENCON

作者： M. S. Abundo L. G. Sison Imstrumentation Robotics and Control Laboratory Department of Electronics Engineering University of Philippines Quezon Philippines

We study the effects of a moving reference frame on the classification of different mental tasks in a brain-machine interface (BMI). We use the band powers and power differences of the electroencephalogram (EEG) signals from 8 surface electrodes during 5 pre-determined mental tasks as the features for the neural network (NN) mental task classifier. We compare the NN classifier performance when the BMI user is visually subjected to a non-moving (static) reference frame (SRF) and to a moving (dynamic) reference frame (DRF) while performing the mental tasks. Results consistently show the degradation of classification accuracy for mental tasks when the user is in a DRF. Furthermore, we observe that mental tasks that require more focus are more sensitive to the SRF-DRF switch than those tasks which need less concentration when performed

关键词： Neural networks Switches Electroencephalography Electrodes Biological neural networks Degradation

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Selection of sources as a prerequesite for information fusion with application to SLAM

Selection of sources as a prerequesite for information fusio...

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International Conference on Information Fusion

作者： Xinde Li Jean Dezert Xinhan Huang Intelligent Control and Robotics Laboratory Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China ONERA Chatillon France

ISBN: (纸本)1424409535

We consider in this work evidential sources of information and propose a very general evidence supporting measure of similarity (ESMS) for selecting the most coherent subset of sources to combine among all sources available at each instant. The methodology proposed here coupled with a DSmT-based fusion machine is tested in robotics for the automatic estimation of an unknown simulated environment with obstacles where an autonomous mobile Pioneer II robot with sonar sensors evolves. Our simulation results are based on the fusion of similar and equireliable sensors but same approach can also be used with dissimilar sources as well by using a discounting method taking into account the reliability of each sensor. Our results show clearly the benefit of the selection of the sources as prerequisite for improvement of information fusion

关键词： Simultaneous localization and mapping Information resources Sensor fusion Mobile robots Robot sensing systems robotics and automation intelligent control intelligent robots Laboratories Automatic testing

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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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A FUSION MACHINE BASED ON DSMT AND PCR5 FOR ROBOT'S MAP RECONSTRUCTION

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International Journal of Information Acquisition 2006年第3期3卷 201-211页

作者： XINHAN HUANG XINDE LI MIN WANG JEAN DEZERT Intelligent Control and Robotics Laboratory Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China ONERA 29 Av. de la Division Leclerc 92320 Chǎtillon France

Characteristics of uncertainty, imprecision, and even imperfection are presented from knowledge acquisition in map reconstruction using sonar sensors fixed on autonomous mobile robot. In order to improve the precision of the fusion and performances of map reconstruction, we propose in this paper a new fusion machine based on Dezert-Smarandache Theory (DSmT) coupled with the fifth Proportional Conflict Redistribution rule (PCR5) for dealing with uncertain and conflicting evidences provided by homogeneous or heterogeneous sources of information. We propose a belief model for sonar grid map and show how to construct efficiently generalized basic belief assignment functions for sensors onboard. A Pioneer II mobile robot with 16 sonar range finders serves as the experiment platform. In our experiment, the robot evolves in a real environment with some obstacles and the environment map is rebuilt online with our self-developing software platform. In this study, we also compare our new approach with other ones based on probability theory, fuzzy theory, Dempster-Shafer Theory (DST) and gray system theory. Our results show an improvement of the performances in precision of map reconstruction of mobile robot with respect to those obtained from aforementioned classical approaches.

关键词： Uncertainty DSmT grid map information fusion mobile robot PCR

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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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A support value transform-based multi focus image fusion method

A support value transform-based multi focus image fusion met...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Sheng Zheng Jian Liu Guangxi Zhu Jinwen Tian Electronic & Information Engineering Department Huazhong University of Science and Technology Wuhan China College of Electrical Engineering & Information Technology China Three Gorges University Yichang China State Edu Commission Key Laboratory for Image Processing & Intelligent Control HUST Wuhan China Chinese Key laboratory of optic & electronics HUST Wuhan China

The image fusion is an important approach to produce a single complete image which preserves all relevant information from different sensors. In this paper, we proposed a support value transform-based multi focus image fusion method, where the fused saliency features are represented by support values. Based on the mapped least squares support vector machine, the support value transform is developed as a multi-scale analysis tool. The fusing results on the multi focus images demonstrate that the proposed image fusion method is effective and efficient

关键词： Focusing Image fusion Support vector machines Discrete wavelet transforms Optical sensors Image sensors Sensor fusion Support vector machine classification Image processing Least squares methods

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