scholarly journals Path Finding and Collision Avoidance in Crowd Simulation

2009 ◽  
Vol 17 (3) ◽  
pp. 217 ◽  
Author(s):  
Cherif Foudil ◽  
Djedi Noureddine ◽  
Cedric Sanza ◽  
Yves Duthen
Keyword(s):  
Collision Avoidance ◽  
Crowd Simulation ◽  
Path Finding

Manoeuvring highly redundant manipulators

Robotica ◽  
1997 ◽  
Vol 15 (4) ◽  
pp. 435-447 ◽  
Author(s):  
E. Sahin Conkur ◽  
Rob Buckingham
Keyword(s):  
Collision Avoidance ◽  
Potential Fields ◽  
Redundant Manipulators ◽  
Path Finding ◽  
Soft Contact ◽  
New Approach ◽  
Digital Computation ◽  
Redundant Robots ◽  
Measurement Scheme ◽  
Environment Measurement

A task based approach to the issue of redundant robots starts from the premise that there are obstacles that cannot be removed from the working area and which therefore must be avoided. This statement produces the requirement for a device with a certain degree of mobility, and stresses the need to ensure that the aim is twofold: reach the goal and avoid obstacles. But avoiding obstacles is not the same objective as keeping as far away from an obstacle as possible; the primary goal is still to reach the target. In fact humans use soft contact to reach targets that are at the periphery of their reach. This soft distributed contact has the effect of smoothing the surface of the object and hence there is an element of only being interested in obstacle detail at the appropriate scale to achieve the task. This paper describes a new approach to collision avoidance based on using a global path finding algorithm, in this case using Laplacian potential fields, in conjunction with a simple local geometrically based algorithm for avoiding obstacles and maximising the use of manoeuvring space in a manner which is not limited by digital computation resolution issues. This extra technique is in some ways analogous to the human soft contact approach. Three examples are presented to illustrate the robustness of the algorithm. In order to be able to compare results with other techniques, an environment measurement scheme is defined which gives an indication of the difficulty of the trajectory being followed.

scholarly journals A panoramic synthetic vision model for improving collision avoidance in crowd simulation

2015 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Ricardo Bustamante de Queiroz ◽  
Teófilo Dutra ◽  
Creto Vidal ◽  
Joaquim Cavalcante-Neto
Keyword(s):  
Virtual Reality ◽  
Visual Perception ◽  
Collision Avoidance ◽  
Vision System ◽  
Crowd Simulation ◽  
Field Of View ◽  
Panoramic View ◽  
Natural Manner ◽  
Synthetic Vision ◽  
The Common

Crowd Simulation is very important in many virtual reality applications, because it improves the sense of immersion of the users by making the population of agents in the environment to move as real crowds do. Recently, models for simulating crowds, in which each agent is equipped with a synthetic vision system, have shown interesting results regarding the natural manner in which the agents navigate inside the environment thanks to their visual perception. In this article, we propose an upgrade to the agent’s visual system with a panoramic view in order to allow an agent to expand its vision beyond the limit of 180o imposed by the common projection provided by rendering APIs. Also, we analyze different parameters, which are used to define the field of view, to investigate the influence they have on the agent’s behavior. The impacts that those changes may cause on the efficiency of the algorithms are also analysed. A visible change on the agent’s behavior is achieved by using the technique, with a slight loss of performance.

A collision avoidance behavior model for crowd simulation based on psychological findings

2013 ◽  
Vol 24 (3-4) ◽  
pp. 173-183 ◽  
Author(s):  
Jin Hyoung Park ◽  
Francisco Arturo Rojas ◽  
Hyun Seung Yang
Keyword(s):  
Collision Avoidance ◽  
Avoidance Behavior ◽  
Crowd Simulation ◽  
Behavior Model ◽  
Simulation Based

scholarly journals Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

2021 ◽  
Author(s):  
Nir Greshler ◽  
Ofir Gordon ◽  
Oren Salzman ◽  
Nahum Shimkin
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Path Finding ◽  
Multi Agent
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