Embedded Motion Control 2014 Group 7

Group members

Name	Email
Maurice van de Ven	m dot l dot j dot v dot d dot ven @ student dot tue dot nl
Pim Duijsens	p dot j dot h dot duijsens @ student dot tue dot nl
Glenn Roumen	g dot h dot j dot roumen @ student dot tue dot nl
Wesley Peijnenburg	w dot peijnenburg @ student dot tue dot nl
Roel Offermans	r dot r dot offermans @ student dot tue dot nl

Planning

Week	Deadline	General plan	Groupmember	Specific task
4	16-05-2014	Complete Corridor Challenge
- - - -	- - - - -	- - - - -	- - - - -	- - - - -
5	19-05-2014	Research Maze Strategies and define modules
	19-05-2014 before 10:45 h.		Roel	Research possibilities for wall and corner detection with OpenCV
	19-05-2014 before 10:45 h.		Glenn	Research possibilities for wall and corner detection with OpenCV
	19-05-2014 before 10:45 h.		Wesley	Research possibilities for arrow detection with OpenCV
	19-05-2014 before 10:45 h.		Maurice	Research possibilities for path planning and path tracker/driver
	19-05-2014 before 10:45 h.		Pim	Research possibilities for path planning and path tracker/driver
- - - -	- - - - -	- - - - -	- - - - -	- - - - -
6	26-05-2014	Research functionality of ROS Packages: Gmapping and move_base
	28-05-2014	Decide: use of packages or write complete new code
	30-05-2014	Conceptual version of all modules are finished
- - - -	- - - - -	- - - - -	- - - - -	- - - - -
7	09-06-2014	Development of modules:	Cutting corners	Glenn
	t.b.d.		Publishing and listening to arrow	Wesley/Maurice
	t.b.d.		Exploration	(Pim/Glenn)
	09-06-2014		OpenCV - local rectangles solver	Wesley/Maurice
	09-06-2014		(OpenCV -) floodfill solver	Roel
- - - -	- - - - -	- - - - -	- - - - -	- - - - -
8	13-06-2014	Final presentation of the Maze Strategy
- - - -	- - - - -	- - - - -	- - - - -	- - - - -
9	20-06-2014	Maze Competition

Corridor strategy

The provided safe_drive file is extended to a file which solves the corridor challenge for a left and right corridor. The strategy, stated below, is implemented and tested. From these tests, it can be concluded that the strategy works properly. Everything is designed and implemented in one single node. The disadvantages of this approach are that it is hard to maintain, hard to extend and teamwork becomes hard. Therefore, it is desired to use different nodes for the maze challenge.

Maze strategy

A choice is made to analyze two different approaches to solve the maze challenge. The maze can be solved by using different nodes and design each node from scratch, or by using existing ros packages. If the first method will be chosen, the different nodes and flow chart can be used as stated below. First, different ros packages are analyzed. The packages GMapping in combination with Move_base, and as an alternative the package Exploration are analyzed.

GMapping & Move_base

GMapping

GMapping is being investigated as SLAM algorithm. The pico can, with this simultaneous localization and mapping (SLAM), make a map of an unknown environment and localize itself inside this map. The strategy is to combine the map and the position of the pico with a navigation stack and a goal planner. GMapping subscribes on /pico/laser and /tf, and publishes on /tf and /map.

In order to run GMapping correctly, a launch file gmap.launch is setup which initializes the correct settings. Here, the parameter base_frame is set as /pico/base_link, map_frame as /map and odom_frame as /pico/odom. Also, scan is remapped to /pico/laser.

Move_base

As navigation stack, move_base is used. Move_base provides an action that will attempt to reach a given goal with a mobile base. It makes use of a local and global planner in order to solve a global navigation task. Move_base also maintains a costmap for the global planner and a costmap for the local planner. These costmaps are used to accomplish navigation tasks. A global overview of the move_base stack is given in the figure on the right.

A choice is made to use the base_local_planner inside the move_base stack. From incoming sensor data, angular and linear velocities of the pico are chosen properly by the local planner to traverse the segment of the global path.

To launch the move_base stack, again a launch file is created which will initialize the correct settings. The move_base.launch file consist of the files listed below. It also performs a remap from /cmd_vel to /pico/cmd_vel.

costmap_common_params.yaml
global_costmap_params.yaml
local_costmap_params.yaml

base_local_planner_params.yaml

The contents of these files are:

costmap_common_params.yaml:	global_costmap_params.yaml:	local_costmap_params.yaml:
obstacle_range: 2.5 raytrace_range: 3.0 inflation_radius: 0.35 robot_radius: 0.30 observation_sources: laser_scan_sensor laser_scan_sensor: {sensor_frame: pico/laser, data_type: LaserScan, topic: /pico/laser, marking: true, clearing: true}	global_costmap: global_frame: /map robot_base_frame: /pico/base_link update_frequency: 5.0 static_map: true	local_costmap: global_frame: /pico/odom robot_base_frame: /pico/base_link update_frequency: 5.0 publish_frequency: 2.0 static_map: false rolling_window: true width: 3.0 height: 3.0 resolution: 0.05

base_local_planner_params.yaml

TrajectoryPlannerROS:
  acc_lim_x: 2.5
  acc_lim_y: 2.5
  acc_lim_theta: 3.2  
  max_vel_x: 0.2
  min_vel_x: 0.0
  max_vel_theta: 1.0
  min_vel_theta: -1.0
  min_in_place_vel_theta: 1.0

  escape_vel: -0.2  
  holomonic_robot: true
  y_vels: [-0.2, -0.0, 0.0, 0.2]
  yaw_goal_tolerance: 0.05
  xy_goal_tolerance: 0.10
  latch_xy_goal_tolerance: false

  sim_time: 2.0
  sim_granularity: 0.005
  angular_sim_granularity: 0.025

  vx_samples: 5
  vtheta_samples: 10
 
  meter_scoring: true
  pdist_scale: 2.0
  gdist_scale: 1.0
  occdist_scale: 0.05
  heading_lookahead: 0.325
  heading_scoring: false
  heading_scoring_timestep: 0.8
  dwa: false
  publish_cost_grid_pc: false
  global_frame_id: /pico/odom

While GMapping and move_base are running a tf tree can be made to see how the different frames are linked. Viewing a tf tree can be done by:

$ rosrun tf view_frames

For correct settings of GMapping and Move_base this results in:

Runnig Gmapping and Move_base for simulations:

1. Start Gazebo:

$ gazebo 

2. Using another terminal, spawn the maze:

$ rosrun gazebo_map_spawner spawn_maze 

3. Launch PICO:

$ roslaunch pico_gazebo pico.launch 

4. Start RViz to visualize how PICO perceives the world through its sensors.

$ rosrun pico_visualization rviz 

5. Launch the gmapping plugin for PICO

$ roslaunch pico_2ndnav gmap.launch 

6. Launch the move_base package for PICO to navigate through the maze with the preferred settings.

$ roslaunch pico_2ndnav move_base.launch 

Using rqt_graph, the following graph is given:

Exploration

Explore_stage

An alternative for GMapping and Move_base, is the explore_stage package. This package is an algorithm that uses a map to find exits and tries to navigate to it. Which is useful in our case. In order to start this package, again a launch file is made. The same local and global costmap settings have been used as in the previous packages. To use explore_stage:

1. Get in the right directory:

$ roscd explore_stage 

2. Launch the explore_stage launch file:

$ roslaunch explore_slam.launch 

The following problems occur: when using RVIZ, it can be seen that a path is planned to an exit, but mose of the time the pico seems stuck. Using different settings for the inflation radius and the radius of pico won't solve this problem. It looks like there is a problem with move inside the explore_stage package. Also, when defining a new goal manually by RVIZ, it repeats saying that a node is added on the coordinates: X.X and X.X but the pico does nothing. It also repeats the following warning: Entropy has not been updated. Loop closure opportunities may be ignored.

Goal planning - OpenCV

In some cases, your script will not find the required include:

#include <move_base_msgs/MoveBaseActionFeedback.h> // Contains the location of pico 

The solution (ubuntu command):

sudo cp -a /opt/ros/groovy/stacks/navigation/move_base_msgs/msg_gen/cpp/include/. /opt/ros/groovy/include/

Arrow recognition - OpenCV

The OpenCV C++ files can be compiled using one of the given commands below. For the g++ command, the packages need to be specified manually.

$ g++ demo_opencv.cpp `pkg-config opencv --cflags --libs` -o demo_opencv 

Or you can make have ROS find the libaries for you:

$ rosmake demo_opencv 

Executing the program:

$ ./demo_opencv <argcount> <arg(s)> 

Troubleshooting:

There have been problems with the OpenCV and Highgui libraries (static libraries). This problem can be solved by replacing the following code:

// #include "cv.h" // (commented out)
#include "opencv2/opencv.hpp"
// #include <highgui.h> // (commented out)
#include <opencv/highgui.h>

The program might not always respond to input, this is can be due to a subscription to the wrong topic. Make sure you are streaming the images and check the ros-topic with:

  rostopic list  

Then the output gives "/pico/camera", "/pico/asusxtion/rgb/image_color", or a different name. Replace the referece in the following line of code:

  ros::Subscriber cam_img_sub = nh.subscribe("/pico/asusxtion/rgb/image_color", 1, &imageCallback); 

Runnig OpenCV on an image stream from a bag file:

In three separate terminals:

roscore
rosbag play <location of bag file>
./demo_opencv

Arrow recognition strategy:

The image is cropped using:

cv::Rect myROI(120,220,400,200);  // Rect_(x,y,width,height)
cv::Mat croppedRef(img_rgb, myROI); // The window is 480 rows and 640 cols

The image is converted from red-green-blue (RGB) to hue-saturation-value (HSV). The red arrow is printed on a white background. With the pico camera, this gives a transient (green) edge colour which is very useful for arrow recognition. So a binary (two-colour) image is created by iterating over every pixel and changing the color to white if it has the specific green colour. All other pixels remain black, this is called the treshold image. This image is then blurred and an edge detection algorithm gives an output of the edges that it can find, they look as follows: Vec4i line[x1,y1,x2,y2].

The list of edges is the basis for the arrow recognition, all lines are defined from left to right (so: x2 > x1). The lines are filtered, and the ones that remain are lines with a slope of +/-30 with a +/-margin. All lines with a negative slope are compared with all lines that have a positive slope. Both the +30 [deg] and the -30 [deg] line have a beginning and end point. If the left (begin) points are close enough to eachother, the output is "LEFT ARROW", and vice versa.

The image below shows the +/-30 [deg] lines which this algorithm focusses on:

Time Table

Overview of time spent per group member

	Lectures	Group meetings	Mastering ROS and C++	Preparing midterm assignment	Preparing final assignment	Wiki progress report	Other activities
Week 1
Pim	2
Roel	2
Maurice	2
Wesley	2
Glenn	2
Week 2
Pim	2	2	4				3
Roel	0	2	10				6
Maurice	2	2	6				3
Wesley	2	2	4				2
Glenn	2	2	5				2
Week 3
Pim	2	1	12	2
Roel	2	4	10	2
Maurice	2	3	10	4
Wesley	2	3	10	1
Glenn	2	2	13	4
Week 4
Pim	2	5	1	7		3
Roel	2	5
Maurice	2	5		15
Wesley	2	5	9	1
Glenn	2	5		6			1
Week 5
Pim	2	2.5			7	0.5
Roel	2	2.5			6		1
Maurice	2	2.5			8	0.5
Wesley	2	5	7		3
Glenn	2	2.5			7
Week 6
Pim		4		6
Roel		4			6
Maurice		4			8
Wesley		4	8		14
Glenn		4			8	1
Week 7
Pim	2	2.5			10
Roel	2	4			8		3
Maurice	2	4			9	2
Wesley	2	4	2		12
Glenn	2	2			8	5
Week 8
Pim		0.5			21
Roel
Maurice
Wesley
Glenn