<<PackageHeader(micros_hopfield)>>
<<TOC(4)>>

== Overview ==
The package aims to give an optimal path for an agent to get its destination in an obstacle environment. The agent starts from a random position except the barrier area in the map, and then generates a feasible path to its destination while avoiding all the obstacles. Furthermore, it can support multiple agents to plan in the map.

== Quick Start ==
=== Installation ===
{{{
cd catkin_ws/src
git clone http://github.com/micros-uav/micros_hopfield
cd ..
catkin_make
}}}
=== Running ===
-Setup rviz
{{{
rosrun rviz rviz
}}}
set the config parameter in rviz : File-open config-user.rviz
-Run server node
{{{
source devel/setup.bash
rosrun micros_hopfield plan_server
}}}
'''Note:'''Run these commands under ~/catkin_ws directory for the terrain map loading.

-Run Client node
Open a new consol
{{{
source devel/setup.bash
rosrun micros_hopfield plan_client i
}}}
where i is the client ID, i = 1,2,3...

'''Note:'''run different clients in independent terminal.
=== Result demo ===
{{https://cloud.githubusercontent.com/assets/11674154/8541617/243de8ec-24c2-11e5-9187-e21bb6d23943.png|alt text|width=700}}

* The green texture presents a terrain with different altitude

* The blue cylinder is an obstacle

* The red and blue lines are the pathes generated for two different agents

'''Note:'''The package is inspired by and adapted from [1]. Related details about neural network based path planning may also be found in  [[http://www.mbfys.ru.nl/staff/s.gielen/path.pdf/|[2]]], [[http://s3.amazonaws.com/academia.edu.documents/40402436/An_efficient_neural_network_approach_to_20151126-31384-62g0pu.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1476936246&Signature=iU3XD6IoJlUQkR8YCzTS698atLk%3D&response-content-disposition=inline%3B%20filename%3DAn_efficient_neural_network_approach_to.pdf/|[3]]] and [[https://www.researchgate.net/profile/Simon_Yang/publication/3413487_Neural_network_approaches_to_dynamic_collision-free_trajectory_generation/links/54a96c220cf257a6360d5594.pdf/|[4]]].

== Reference ==
[1]  Chonghong Fan, Youzhang Lu, Hong Liu, Shangteng Huang. Path planning for mobile robot based on neural networks. Computer Engineering and Application, 2004, 8: 86-89. (in Chinese)

[2] Roy Glasius, Andrzej Komoda, Stan C.A.M. Gielen. Neural network dynamics for path planning and obstacle avoidance. Neural Networks, 1995, 8(1):125-133.

[3] Simon X. Yang, Max Meng. An efficient neural network approach to dynamic robot motion planning. Neural Networks, 2000, 13(2):143-148.

[4] Simon X. Yang, Max Meng. Neural network approaches to dynamic collision-free trajectory generation. IEEE SMC Part B, 2001, 31(3): 302-318.

## AUTOGENERATED DON'T DELETE
## CategoryPackage