scholarly journals Exponential Fields Formulation for WMR Navigation

2012 ◽  
Vol 9 (4) ◽  
pp. 375-397 ◽  
Author(s):  
Edgar A. Martínez-García ◽  
Rafael Torres-Cordoba
Keyword(s):  
Autonomous Navigation ◽  
Sensory Information ◽  
Dynamic Environments ◽  
Force Model ◽  
Social Force ◽  
Wheeled Mobile Robots ◽  
Navigation Algorithm ◽  
The Social ◽  
Planning Scheme ◽  
Fast Dynamic

In this manuscript, an autonomous navigation algorithm for wheeled mobile robots (WMR) operating in dynamic environments (indoors or structured outdoors) is formulated. The planning scheme is of critical importance for autonomous navigational tasks in complex dynamic environments. In fast dynamic environments, path planning needs algorithms able to sense simultaneously a diversity of obstacles, and use such sensory information to improve real-time navigation control, while moving towards a desired goal destination. The framework tackles 4 issues. 1) Reformulation of the Social Force Model (SFM) adapted to WMR; 2) the cohesion of a general inertial scheme to represents motion in any coordinate system; 3) control of actuators rotational speed as a general model regardless kinematic restrictions; 4) assuming detection of features (obstacles/goals), adaptive numeric weights are formulated to affect navigational exponential components. Simulation and experimental outdoors results are presented to show the feasibility of the proposed framework.

scholarly journals Modeling the Pedestrian Ability of Detecting Lanes and Lane Changing Behavior

2016 ◽  
Vol 10 (7) ◽  
pp. 1
Author(s):  
Mohammed Mahmod Shuaib
Keyword(s):  
Quantitative Measurement ◽  
Force Model ◽  
Social Force ◽  
Lane Changing ◽  
Crowd Dynamics ◽  
Proposed Model ◽  
The Social ◽  
Lane Formation ◽  
Dynamics Models ◽  
The Impact

Incorporating decision-making capability as an intelligence aspect into crowd dynamics models is crucial factor for reproducing realistic pedestrian flow. Crowd dynamics models are still suffering from poor representation of essential behaviors such as lane changing behavior. In this article, we provide the simulated pedestrians in the social force model more intelligence as an extension to the pedestrian’s investigation capability in bidirectional walkways, to let the model appear more representative of what actually happens in reality. In the proposed model, the lane’s structure is modeled as social network. Thereby, the simulated pedestrians with inconvenient walking can detect the available lanes inside his environment, investigate their attractions, and then make decisions to join the most attractive one. Simulations are performed to validate the work qualitatively by tracing the behavior of the simulated pedestrians and studying the impact of this behavior on lane formation. Finally, a quantitative measurement is used to study the effect of our contribution on the pedestrians’ efficiency of motion.

scholarly journals Validation of a Dynamic Planning Navigation Strategy Applied to Mobile Terrestrial Robots

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4322 ◽  
Author(s):  
Caroline Silva ◽  
Átila de Oliveira ◽  
Marcelo Fernandes
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Autonomous Navigation ◽  
Genetic Parameters ◽  
Dynamic Environments ◽  
Dynamic Planning ◽  
Navigation Algorithm ◽  
Navigation Strategy ◽  
Terrestrial Environments ◽  
Simulation Results

This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations of genetic parameters including the crossover rate and population size. To this end, simulations were performed of static and dynamic environments, applying the different conditions. The simulation results showed satisfactory efficiency and robustness of the DPNA-GA technique, validating it for real applications involving mobile terrestrial robots.

scholarly journals SPECIFICATION OF THE SOCIAL FORCE PEDESTRIAN MODEL BY EVOLUTIONARY ADJUSTMENT TO VIDEO TRACKING DATA

2007 ◽  
Vol 10 (supp02) ◽  
pp. 271-288 ◽  
Author(s):  
ANDERS JOHANSSON ◽  
DIRK HELBING ◽  
PRADYUMN K. SHUKLA
Keyword(s):  
Large Scale ◽  
Optimal Parameter ◽  
Video Tracking ◽  
Urban Environments ◽  
Force Model ◽  
Tracking Data ◽  
Social Force ◽  
Social Force Model ◽  
Video Recordings ◽  
The Social

Based on suitable video recordings of interactive pedestrian motion and improved tracking software, we apply an evolutionary optimization algorithm to determine optimal parameter specifications for the social force model. The calibrated model is then used for large-scale pedestrian simulations of evacuation scenarios, pilgrimage, and urban environments.

scholarly journals Intelligent Agent Simulator in Massive Crowd

2018 ◽  
Vol 11 (2) ◽  
pp. 577
Author(s):  
Mohamad, S ◽  
Nasir, F.M ◽  
Sunar, M.S. ◽  
Isa, K. ◽  
Hanifa, R.M. ◽  
...  
Keyword(s):  
Computer Games ◽  
Real Life ◽  
Intelligent Agent ◽  
Training Data ◽  
Support Vector ◽  
Force Model ◽  
Social Force ◽  
The Social ◽  
Crowd Simulations ◽  
Crowded Environments

Crowd simulations have many benefits over real-life research such as in computer games, architecture and entertainment. One of the key elements in this study is to include elements of decision-making into the crowd. The aim of this simulator is to simulate the features of an intelligent agent to escape from crowded environments especially in one-way corridor, two-way corridor and four-way intersection. The addition of the graphical user interface enables intuitive and fast handling in all settings and features of the Intelligent Agent Simulator and allows convenient research in the field of intelligent behaviour in massive crowd. This paper describes the development of a simulator by using the Open Graphics Library (OpenGL), starting from the production of training data, the simulation process, until the simulation results. The Social Force Model (SFM) is used to generate the motion of agents and the Support Vector Machine (SVM) is used to predict the next step for intelligent agent.

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