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Virtual and intelligent traffic signs in rescue simulation system: Imitation of human society in agent society

Virtual and intelligent traffic signs in rescue simulation s...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Mostafa Asghari Behrooz Masoumi Mohammad Reza Meybodi Azad University Iran Azad University of Qazvin Iran Department of Computer Engineering and IT Amirkabir University of Technology슠 Tehran Iran

ISBN: (纸本)9781424448081

Robocup rescue simulation system is a suitable test-bed for test and evaluation of multi-agent system's related ideas and techniques. Hence, the world robocup competitions are held each year and the used ideas and techniques are evaluated in the form of different teams. In rescue simulation system, at the start of simulation, police agents should search and explore the earthquake area and open the blocked roads. In this paper, which is the first application of distributed learning automata in rescue simulation system, the agents imitate the ways which real people use for solving traffic problems in their societies. Like the real peoples' solutions for traffic problem, the agents use virtual police and street signs in their virtual environment junctions. The results indicate the fact that the proposed method gives more exploration power to rescue agents.

关键词： Intelligent agent Traffic control Humans Learning automata Roads Path planning computational modeling Computer simulation System testing Earthquakes

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Composite visual servoing for catching a 3-D flying object using RLS trajectory estimation from a monocular image sequence

Composite visual servoing for catching a 3-D flying object u...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： R. Herrejon S. Kagami K. Hashimoto Intelligent Control Systems Laboratory Department of System Information Sciences University of Tohoku Sendai Japan

Online coordination of visual information with slow speed manipulator control is studied in the specific task of three dimensional robotic catching using position based visual servoing. The problem involves the design and application of a recursive algorithm to extract and predict the position of an object in a 3D environment from one feature correspondence from a monocular image sequence. The measured data are the noisy image plane coordinates of object match taken from image in the sequence. Image plane noise levels are allowed and investigated. The target trajectory estimation is formulated as a tracking problem, which can use an arbitrary large number of images in a sequence and is done using recursive least squares (RLS). The feasibility of our methods for catching are demonstrated by both simulations and experiments using a a real-time vision system and a six-degree-of-freedom robotic arm with speed capabilities of up to 1.0 m/s.

关键词： Visual servoing Resonance light scattering Image sequences Robot kinematics Manipulators Algorithm design and analysis Data mining Coordinate measuring machines Working environment noise Noise level

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Applying a neuro-fuzzy classifier for gesture-based control using a single wrist-mounted accelerometer

Applying a neuro-fuzzy classifier for gesture-based control ...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Nona Helmi Mohammad Helmi Department of Computer Engineering Islamic Azad University Iran Department of Computer and Electrical Engineering Yazd University Iran

ISBN: (纸本)9781424448081

Gestures, due to their natural modality, can be normally used in human-computer interaction (HCI) domains such as robotics, design environments and handheld devices. In this paper, a single wrist-mounted triaxial accelerometer is used to collect the acceleration data generated by hand movements forming 36 different gestures. This study intends to find the gestures which are capable of controlling an appliance with a maximum accuracy. A neuro-fuzzy classifier is devised for gestures detection to improve the classification rate in relative terms. The neuro-fuzzy system also selects the best features which yield the highest rate of classification. It reduces the dimensionality of feature set in two phases; the first phase is before carrying out the classification and the second phase is after selecting the most suitable gestures. The feature selection process finally reduces the number of features from 120 to 19. Our neuro-fuzzy system detects 25 gestures that can be classified with an accuracy of 100%, which is the highest rate among other classifiers. So, since the gesture-based control is accurately performed, it can be a proper method for HCI applications.

关键词： Accelerometers Acceleration Handheld computers Human computer interaction Filtering Communication system control Signal processing Human robot interaction Fuzzy neural networks Instruments

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Bacterial foraging oriented by Particle Swarm Optimization strategy for PID tuning

Bacterial foraging oriented by Particle Swarm Optimization s...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Wael M. Korani Hassen Taher Dorrah Hassan M. Emara Department of Electrical Power and Machines Engineering Cairo University Giza Egypt

ISBN: (纸本)9781424448081

Proportional integral derivative (PID) controller tuning is an area of interest for researchers in many disciplines of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of the foraging behavior of E coli bacteria foraging and Particle Swarm Optimization (PSO). The E coli algorithm depends on random search directions which may lead to delay in reaching the global solution. The PSO algorithm may lead to possible entrapment in local minimum solutions. This paper proposed a new algorithm Bacteria Foraging oriented by PSO (BF-PSO). The new algorithm is proposed to combines both algorithms' advantages in order to get better optimization values. The proposed algorithm is applied to the problem of PID controller tuning and is compared with conveniently Bacterial Foraging algorithm and Particle swarm optimization.

关键词： Microorganisms Particle swarm optimization Three-term control Power engineering and energy Ant colony optimization Open loop systems Zinc Delay Spinning Testing

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Information-based exploration strategy for mobile robot in dynamic environment

Information-based exploration strategy for mobile robot in d...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Satoshi Hirashita Takehisa Yairi Faculty of Engineering Aeronautics and Astronautics University of Tokyo Meguro Tokyo Japan

ISBN: (纸本)9781424448081

To meet the necessity of handling environmental uncertainties of mobile robots, we proposed an efficient exploration strategy to gather information, called entropy sweeper. To do so, we utilized the entropy distribution and the utility function to determine which positions have more uncertainties. Proposed strategy is divided into two phases: the learning phase and the action phase. In general, uncertainties increase unevenly and never disappear in dynamic environments. So in the learning phase, robots move wall to wall to learn which positions are likely to increase uncertainties actively. In the action phase, robots explore the environment efficiently and continue lifelong learning to handle environmental uncertainties. This strategy is an optimization not only for paths but also for sequences of exploration points using information about uncertainties of dynamic environments. We demonstrated its effectiveness with several simulations.

关键词： Mobile robots Uncertainty Aerodynamics Robot sensing systems Entropy Orbital robotics Intelligent robots Intelligent sensors Robot control Cleaning

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Path planning strategy design under the experience of mobile robot navigation

Path planning strategy design under the experience of mobile...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Wen-Yo Lee An-Doo Yang Ta-Chih Hung Jhu-Syuan Guo Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Information Management Lunghwa University of Science and Technology Taiwan Industrial Technology and Research Institute Hsinchu Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taiwan

ISBN: (纸本)9781424448081

This paper presents a path planning strategy for a mobile robot. The path planning strategy is according to the experience that the mobile robot has been experienced in target navigation. The mixed reality technique is implemented by both the virtools tool and mobile robot. There are three basic interactive behaviors of human beings which have been implemented on this proposed mobile robot. They are targeting navigation behavior, face tracking behavior, and obstacle avoidance behavior; all of them are designed by the fuzzy rule methods, respectively. These behaviors make the proposed path planning strategy possible. The virtual reality environment is used to implement the arbitrator of the behaviors and to choose the behavior rules for dynamic environment changing. Experiments show that the proposed behaviors are well designed. The proposed robot system can optimize the path planning based on the previous navigation experience.

关键词： Path planning Mobile robots Navigation Service robots Humans Humanoid robots Robot sensing systems Robot control Virtual reality Fuzzy control

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Realtime execution of automated plans using evolutionary robotics

Realtime execution of automated plans using evolutionary rob...

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ieee symposium on computational intelligence and Games, CIG

作者： Tommy Thompson John Levine Strathclyde Planning Group University of Strathclyde Glasgow UK

Applying neural networks to generate robust agent controllers is now a seasoned practice, with time needed only to isolate particulars of domain and execution. However we are often constrained to local problems due to an agents inability to reason in an abstract manner. While there are suitable approaches for abstract reasoning and search, there is often the issues that arise in using offline processes in real-time situations. In this paper we explore the feasibility of creating a decentralised architecture that combines these approaches. The approach in this paper explores utilising a classical automated planner that interfaces with a library of neural network actuators through the use of a Prolog rule base. We explore the validity of solving a variety of goals with and without additional hostile entities as well as added uncertainty in the the world. The end results providing a goal driven agent that adapts to situations and reacts accordingly.

关键词： Robotics and automation Neural networks Testing Application software Intelligent sensors Games Robust control Automatic generation control Libraries Actuators

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Designing integrated guidance law for aerodynamic missiles by multi-objectives evolutionary algorithm and Tabu search

Designing integrated guidance law for aerodynamic missiles b...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Hanafy M. Omar M. A. Abido AeroSpace Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9781424448081

In this paper, a strength Pareto evolutionary algorithm (SPEA) based approach is proposed for designing an integrated fuzzy guidance law which consists of three fuzzy controllers. Each of these controllers is activated in a region of the interception. The distribution of the membership functions and the rules are obtained by solving a nonlinear constrained multi-objectives optimization problem where final time, energy consumption, and miss distance are treated as competing objectives. Tabu search is proposed to get the initial feasible solution for the multi-objective optimization algorithm. Then a hierarchical clustering technique is implemented to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the tradeoff front. Moreover, a fuzzy-based mechanism is employed to extract the best compromise solution over the trade-off curve. The simulation results show that the proposed design technique was able to generate a missile guidance law with satisfactory performance with the existence of noisy measurements.

关键词： Algorithm design and analysis Aerodynamics Missiles Evolutionary computation Fuzzy control Navigation Constraint optimization Energy consumption Fuzzy logic Acceleration

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A combined skin model and feature approach for tracking of human faces

A combined skin model and feature approach for tracking of h...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Jamil Abou Saleh Malek Baklouti Fakhreddine Karray PAMI Lab University of Waterloo Canada Thales Research Departement France

ISBN: (纸本)9781424448081

In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first estimated using the Estimation Maximization (EM) algorithm. Then, feature based classification differentiates face related pixels from other skin regions and objects with close intensity values. A novel approach for classifying faces using a structure of cooperating neural networks, for which learning parameters are generated using Adaboost learning method is proposed. In addition, a new approach is also proposed for training the neural network with reduced space Haar-like features instead of working with image pixels themselves. Principle component analysis was used to find the aspects of features that are crucial for detection. The features dimensionality was reduced by nearly 90 percent, hence improving radically the training time. When adequate parameters are chosen, the system yields face detection characteristics that outperform the best existing algorithms (such as the one proposed by Viola and Jones) in terms of accuracy. Finally, the parameters of the mixture of Gaussians model are updated based on the results of the classification testing results to increase its robustness against illuminations and other external environmental changes, as well as reducing even more the search space.

关键词： Skin Humans Face detection Gaussian processes Neural networks Programmable control Adaptive control Parameter estimation Learning systems Pixel

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Evolving multirobot excavation controllers and choice of platforms using an artificial neural tissue paradigm

Evolving multirobot excavation controllers and choice of pla...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： Jekanthan Thangavelautham Nader Abu El Samid Paul Grouchy Ernest Earon Terence Fu Nagina Nagrani Gabriele M. T. D'Eleuterio Mechanical Engineering Dept. Massachusetts Institute of Technology Cambridge MA USA Institute for Aerospace Studies University of Toronto Toronto Canada

ISBN: (纸本)9781424448081

Autonomous robotic excavation has often been limited to a single robotic platform using a specified excavation vehicle. This paper presents a novel method for developing scalable controllers for use in multirobot scenarios and that do not require human defined operations scripts nor extensive modeling of the kinematics and dynamics of the excavation vehicles. Furthermore, the control system does not require specifying an excavation vehicle type such as a bulldozer, front-loader or bucket-wheel and it can evolve to select for an appropriate choice of excavation vehicles to successfully complete a task. The ¿artificial neural tissue¿ (ANT) architecture is used as a control system for autonomous multirobot excavation and clearing tasks. This control architecture combines a variable-topology neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue. Training is done in a low-fidelity grid world simulation environment and where a single global fitness function and a set of allowable basis behaviors need be specified. This approach is found to provide improved training performance over fixed-topology neural networks and can be easily ported onto different robot platforms. Aspects of the controller functionality have been tested using high fidelity dynamics simulation and in hardware. An evolutionary training process discovers novel decentralized methods of cooperation employing aggregation behaviors (via synchronized movements). These aggregation behaviors are found to improve controller scalability (with increasing robot density) and better handle robot interference (antagonism) that reduces the overall efficiency of the group.

关键词： Remotely operated vehicles Vehicle dynamics control systems Mobile robots Humans Kinematics Neural networks Testing Hardware Scalability

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