scholarly journals Visual Attention in Dynamic Environments and its Application to Playing On-line Games

2014 ◽  
Vol 14 (10) ◽  
pp. 523-523
Author(s):  
Y. Kotseruba ◽  
J. Tsotsos
Keyword(s):  
Visual Attention ◽  
Dynamic Environments ◽  
On Line

scholarly journals Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments

2001 ◽  
Vol 20 (1) ◽  
pp. 39-65 ◽  
Author(s):  
Hector Yee ◽  
Sumanita Pattanaik ◽  
Donald P. Greenberg
Keyword(s):  
Visual Attention ◽  
Dynamic Environments

scholarly journals Epistemic guidance of visual attention for robotic agents in dynamic visual scenes

2021 ◽  
Author(s):  
◽  
Arindam Bhakta
Keyword(s):  
Visual Attention ◽  
Utility Function ◽  
Inhibition Of Return ◽  
Real Life ◽  
Dynamic Environments ◽  
Saliency Map ◽  
Utility Functions ◽  
Visual Scene ◽  
Underlying Structure ◽  
Pixel Intensity

<p>Humans and many animals can selectively sample important parts of their visual surroundings to carry out their daily activities like foraging or finding prey or mates. Selective attention allows them to efficiently use the limited resources of the brain by deploying sensory apparatus to collect data believed to be pertinent to the organism's current task in hand.  Robots or other computational agents operating in dynamic environments are similarly exposed to a wide variety of stimuli, which they must process with limited sensory and computational resources. Developing computational models of visual attention has long been of interest as such models enable artificial systems to select necessary information from complex and cluttered visual environments, hence reducing the data-processing burden.  Biologically inspired computational saliency models have previously been used in selectively sampling a visual scene, but these have limited capacity to deal with dynamic environments and have no capacity to reason about uncertainty when planning their visual scene sampling strategy. These models typically select contrast in colour, shape or orientation as salient and sample locations of a visual scene in descending order of salience. After each observation, the area around the sampled location is blocked using inhibition of return mechanism to keep it from being re-visited.  This thesis generalises the traditional model of saliency by using an adaptive Kalman filter estimator to model an agent's understanding of the world and uses a utility function based approach to describe what the agent cares about in the visual scene. This allows the agents to adopt a richer set of perceptual strategies than is possible with the classical winner-take-all mechanism of the traditional saliency model. In contrast with the traditional approach, inhibition of return is achieved without implementing an extra mechanism on top of the underlying structure.  This thesis demonstrates the use of five utility functions that are used to encapsulate the perceptual state that is valued by the agent. Each utility function thereby produces a distinct perceptual behaviour that is matched to particular scenarios.  The resulting visual attention distribution of the five proposed utility functions is demonstrated on five real-life videos.  In most of the experiments, pixel intensity has been used as the source of the saliency map. As the proposed approach is independent of the saliency map used, it can be used with other existing more complex saliency map building models. Moreover, the underlying structure of the model is sufficiently general and flexible, hence it can be used as the base of a new range of more sophisticated gaze control systems.</p>

On-Line Centralized Path Planning of Multiple Mobile Robots in Dynamic Environments

2012 ◽  
Vol 155-156 ◽  
pp. 1074-1079
Author(s):  
Zi Hui Zhang ◽  
Yue Shan Xiong
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Dynamic Environments ◽  
Characteristic Line ◽  
Multiple Mobile Robots ◽  
Time Performance ◽  
Planning Algorithm ◽  
On Line ◽  
Path Planning Algorithm

To study the path planning problem of multiple mobile robots in dynamic environments, an on-line centralized path planning algorithm is proposed. It is difficult to obtain real-time performance for path planning of multiple robots in dynamic environment. The harmonic potential field for multiple mobile robots is built by using the panel method known in fluid mechanics, which represents the outward normal velocity of each line of a polygonal obstacle as a function of the length of its characteristic line. The simulation results indicate that it is a simple, efficient and effective path planning algorithm for multiple mobile robots in the dynamic environments that the geometries and trajectories of obstacles are known in advance, and can achieve real-time performance.

Comparative analysis of collision-free path-planning methods for multi-manipulator systems

Robotica ◽  
2006 ◽  
Vol 24 (6) ◽  
pp. 711-726 ◽  
Author(s):  
J. C. Fraile ◽  
J. Perez-Turiel ◽  
J. L. Gonzalez-Sanchez ◽  
E. Baeyens ◽  
R. Perez
Keyword(s):  
Comparative Analysis ◽  
Path Planning ◽  
Potential Field ◽  
Degrees Of Freedom ◽  
Dynamic Environments ◽  
Elastic Strip ◽  
Field Methods ◽  
Planning Methods ◽  
On Line ◽  
Line Path

Motion planning for manipulators with many degrees of freedom is a complex task. The research in this area has been mostly restricted to static environments. This paper presents a comparative analysis of three reactive on-line path-planning methods for manipulators: the elastic-strip, strategy-based and potential field methods. Both the elastic-strip method [O. Brock and O. Khatib, “Elastic strips: A framework for integrated planning and execution,” Int. Symp. Exp. Robot. 245–254 (1999)] and the potential field method [O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res.5(1), 90–98 (1986)] have been adapted by the authors to the problem at hand related to our multi-manipulator system (MMS) (three manipulators with five degrees of freedom each). Strategy-based method is an original contribution by the authors [M. Mediavilla, J. L. González, J. C. Fraile and J. R. Perán, “Reactive approach to on-line path planning for robot manipulators in dynamic environments,” Robotica20, 375–384 (2002); M. Mediavilla, J. C. Fraile, T. González and I. J. Galindo, “Selection of strategies for collision-free motion in multi-manipulator systems,” J. Intell. Robot Syst38, 85–104 (2003)].The three methods facilitate on-line path planning for our MMS in dynamic environments with collision avoidance, where the three manipulators may move at the same time in their common workspace. We have defined some ‘basic motion problems’ for the MMS, and a series of simulations has been running that will tell us how effective each path-planning method is. The simulations have been performed and the obtained results have been analysed by using a software program developed by the authors.The paper also presents experimental results obtained applying the path-planning methods to our MMS, that perform pick-and-place tasks sharing common working areas.

On-line EM Algorithm for the Normalized Gaussian Network

2000 ◽  
Vol 12 (2) ◽  
pp. 407-432 ◽  
Author(s):  
Masa-aki Sato ◽  
Shin Ishii
Keyword(s):  
Em Algorithm ◽  
Dynamic Environments ◽  
Local Linear Regression ◽  
Discount Factor ◽  
Robot Dynamics ◽  
Mixtures Of Experts ◽  
On Line ◽  
Dynamics Problems ◽  
Changes Over Time ◽  
Gaussian Network

A normalized gaussian network (NGnet) (Moody & Darken, 1989) is a network of local linear regression units. The model softly partitions the input space by normalized gaussian functions, and each local unit linearly approximates the output within the partition. In this article, we propose a new on-line EM algorithm for the NGnet, which is derived from the batch EM algorithm (Xu, Jordan, & Hinton 1995), by introducing a discount factor. We show that the on-line EM algorithm is equivalent to the batch EM algorithm if a specific scheduling of the discount factor is employed. In addition, we show that the on-line EM algorithm can be considered as a stochastic approximation method to find the maximum likelihood estimator. A new regularization method is proposed in order to deal with a singular input distribution. In order to manage dynamic environments, where the input-output distribution of data changes over time, unit manipulation mechanisms such as unit production, unit deletion, and unit division are also introduced based on probabilistic interpretation. Experimental results show that our approach is suitable for function approximation problems in dynamic environments. We also apply our on-line EM algorithm to robot dynamics problems and compare our algorithm with the mixtures-of-experts family.

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