2,613 research outputs found
Bounding inconsistency using a novel threshold metric for dead reckoning update packet generation
Human-to-human interaction across distributed applications requires that sufficient consistency be maintained among participants in the face of network characteristics such as latency and limited bandwidth. The level of inconsistency arising from the network is proportional to the network delay, and thus a function of bandwidth consumption. Distributed simulation has often used a bandwidth reduction technique known as dead reckoning that combines approximation and estimation in the communication of entity movement to reduce network traffic, and thus improve consistency. However, unless carefully tuned to application and network characteristics, such an approach can introduce more inconsistency than it avoids. The key tuning metric is the distance threshold. This paper questions the suitability of the standard distance threshold as a metric for use in the dead reckoning scheme. Using a model relating entity path curvature and inconsistency, a major performance related limitation of the distance threshold technique is highlighted. We then propose an alternative time—space threshold criterion. The time—space threshold is demonstrated, through simulation, to perform better for low curvature movement. However, it too has a limitation. Based on this, we further propose a novel hybrid scheme. Through simulation and live trials, this scheme is shown to perform well across a range of curvature values, and places bounds on both the spatial and absolute inconsistency arising from dead reckoning
Exploring the use of local consistency measures as thresholds for dead reckoning update packet generation
Human-to-human interaction across distributed applications requires that sufficient consistency be maintained among participants in the face of network characteristics such as latency and limited bandwidth. Techniques and approaches for reducing bandwidth usage can minimize network delays by reducing the network traffic and therefore better exploiting available bandwidth. However, these approaches induce inconsistencies within the level of human perception. Dead reckoning is a well-known technique for reducing the number of update packets transmitted between participating nodes. It employs a distance threshold for deciding when to generate update packets. This paper questions the use of such a distance threshold in the context of absolute consistency and it highlights a major drawback with such a technique. An alternative threshold criterion based on time and distance is examined and it is compared to the distance only threshold. A drawback with this proposed technique is also identified and a hybrid threshold criterion is then proposed. However, the trade-off between spatial and temporal inconsistency remains
A Stochastic Model of Plausibility in Live-Virtual-Constructive Environments
Distributed live-virtual-constructive simulation promises a number of benefits for the test and evaluation community, including reduced costs, access to simulations of limited availability assets, the ability to conduct large-scale multi-service test events, and recapitalization of existing simulation investments. However, geographically distributed systems are subject to fundamental state consistency limitations that make assessing the data quality of live-virtual-constructive experiments difficult. This research presents a data quality model based on the notion of plausible interaction outcomes. This model explicitly accounts for the lack of absolute state consistency in distributed real-time systems and offers system designers a means of estimating data quality and fitness for purpose. Experiments with World of Warcraft player trace data validate the plausibility model and exceedance probability estimates. Additional experiments with synthetic data illustrate the model\u27s use in ensuring fitness for purpose of live-virtual-constructive simulations and estimating the quality of data obtained from live-virtual-constructive experiments
Implementation of an autonomous mobile robot navigation algorithm using C language
Navigation is a major challenge for autonomous, mobile robots. The problem can
basically be divided into positioning and path planning. This report basically
discusses the study and also work that has been done from previous of the chosen
topic, which is Implementation of an Autonomous Mobile Robot Navigation
Algorithm using 'C' language. The objective of the project is to develop navigation
algorithm for the autonomous mobile robot. After that, implement the navigation
algorithm on the mobile robot and without using the external sensor for navigation of
the mobile robot to reach the specified point. From the encoder programming to the
motor speed control, this project basically focusing on how to control the motor speed
movement such as rotation speed, turning speed, turning angle of the robot by
controlling the movement of the motor and distance travel or displacement of the
mobile robot from initial point to end point through some path that required turning
algorithm and forward movement in controlled speed
The development and evaluation of computer vision algorithms for the control of an autonomous horticultural vehicle
Economic and environmental pressures have led to a demand for reduced chemical use in crop production. In response to this, precision agriculture techniques have been developed that aim to increase the efficiency of farming operations by more targeted application of chemical treatment. The concept of plant scale husbandry (PSH) has emerged as the logical extreme of precision techniques, where crop and weed plants are treated on an individual basis. To investigate the feasibility of PSH, an autonomous horticultural vehicle has been developed at the Silsoe Research Institute. This thesis describes the development of computer vision algorithms for the experimental vehicle which aim to aid navigation in the field and also allow differential treatment of crop and weed. The algorithm, based upon an extended Kalman filter, exploits the semi-structured nature of the field environment in which the vehicle operates, namely the grid pattern formed by the crop planting. By tracking this grid pattern in the images captured by the vehicles camera as it traverses the field, it is possible to extract information to aid vehicle navigation, such as bearing and offset from the grid of plants. The grid structure can also act as a cue for crop/weed discrimination on the basis of plant position on the ground plane. In addition to tracking the grid pattern, the Kalman filter also estimates the mean distances between the rows of lines and plants in the grid, to cater for variations in the planting procedure. Experiments are described which test the localisation accuracy of the algorithms in offline trials with data captured from the vehicle's camera, and on-line in both a simplified testbed environment and the field. It is found that the algorithms allow safe navigation along the rows of crop. Further experiments demonstrate the crop/weed discrimination performance of the algorithm, both off-line and on-line in a crop treatment experiment performed in the field where all of the crop plants are correctly targeted and no weeds are mistakenly treated
An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform
With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations
Dynamic Hybrid Strategy Models for Networked Mulitplayer Games
Two of the primary factors in the development of
networked multiplayer computer games are network
latency and network bandwidth. Reducing the effects of
network latency helps maintain game-state fidelity,
while reducing network bandwidth usage increases the
scalability of the game to support more players. The
current technique to address these issues is to have each
player locally simulate remote objects (e.g. other
players). This is known as dead reckoning. Provided the
local simulations are accurate to within a given
tolerance, dead reckoning reduces the amount of
information required to be transmitted between players.
This paper presents an extension to the recently
proposed Hybrid Strategy Model (HSM) technique,
known as the Dynamic Hybrid Strategy Model
(DHSM). By dynamically switching between models of
user behaviour, the DHSM attempts to improve the
prediction capability of the local simulations, allowing
them to stay within a given tolerance for a longer
amount of time. This can lead to further reductions in
the amount of information required to be transmitted.
Presented results for the case of a simple first-person
shooter (FPS) game demonstrate the validity of the
DHSM approach over dead reckoning, leading to a
reduction in the number of state update packets sent and
indicating significant potential for network traffic
reduction in various multiplayer games/simulations
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
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