Introduction to Robotic Algorithms by kmb15358

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									Introduction to Robotic Algorithms


Final Project: Interaction with the MagellanPro Robot




                                 Kris Beevers (beevek@rpi.edu)
                                 Douglas Johnston (johnsd8@rpi.edu)
                                 May, 2002
Project Description and Assumptions:
The project consists of several phases:
 a data collection program that stores sensor data generated manually (by driving the
   robot around by hand)
 a modified assignment three code to generate sonar maps with this data
 a "map editor" that uses images created by our Assignment 3 code to help in the
   creation of a polygonal representation of the region
 a probabilistic roadmap (PRM) generator that takes the output of the map editor and
   generates probabilistic roadmaps based on the configuration space of the map
 a "navigator" that takes the outputs of the map editor and the PRM generator and
   enables the robot to move throughout the region
 an implementation of the Kalman filter from assignment two that improves the robot's
   estimation of its current position
Most of the phases are made graphical for testing and end user ease of use.

Some of the assumptions and limitations of our project implementation include:
 A static environment, e.g. no moving obstacles and no unexpected obstacles (that
   weren't mapped)
 Paths through the environment can be completely built using straight-line motion and
   in-place rotation
 The environment is suitable for the robot's sensors (e.g. obstacles aren't too small to
   be detected or are otherwise impossible to detect)
 The environment and robot can be approximated completely by convex polygons
 The starting configuration of the robot is a parameter to the navigation program
 The user is able to approximate the area of interest to a reasonable degree in the map
   editor, and is able to estimate the initial position and orientation of the robot in the
   world to a reasonable level of accuracy


The Process:
1. Construct or find an area suitable for the robot's sensors and large enough to provide
   some moving room (a small enclosed area of about 3 meters square or larger with
   some obstacles, or a hallway should both work fine)
2. Position the robot inside the region of interest, and then start the 'getdata' program
3. Using the joystick or computer, move the robot throughout the area; while doing this,
   be sure to circumnavigate as many of the obstacles in the area as possible
4. Run 'makeimg' using the data generated by 'getdata' as input
5. Convert the output of 'makeimg' to a 2^n x 2^n, 24/32-bit uncompressed TGA (see
   README.ERRATA)
6. Run 'map_edit' using the TGA output of 'makeimg' as input, and create a
   polygonal map (save this to a file)
7. Run 'prmgen' using the map created in 'map_edit' as input and generate PRM's
   until a suitable one is discovered (save this to a file)



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8. Position the robot inside the region of interest (anywhere), and run the 'navigate'
   program; tell the program the location of the robot and its orientation, and then give
   the program a destination. The robot will move through the area using the PRM
   generated in 'prmgen'


Specifics:
All of the files necessary (source code and documentation) are located on
maximal.robotics.cs.rpi.edu as an archive at ~beevek/ira_final.tar.gz. To
begin, simply unpack the archive to any directory on maximal. After this:
    1. $ cd <unpack_dir>/src
    2. $ make

This creates the following executables in <unpack_dir>/bin:
   1. getdata The data retrieval program
   2. makeimg The sensor data image generator
   3. map_edit The polygonal map editor
   4. prmgen The PRM generator
   5. navigate The navigation program

Getdata:
The 'getdata' program is very simple to use; run it like so:
       $ ./getdata -robot MagellanPro > <outfile>
Terminate the program by pressing ^C after collecting data

Makeimg:
The 'makeimg' program requires X11; it can be run as follows:
        $ ./makeimg <datafile>
Once the program has started, the following commands are available:
   - 'q' Exit
   - 'd' Dump the image to 'map.ppm'
   - 'n' Process a single reading
   - 's' Process the rest of the current scan
   - 't' Process the next 10 scans
   - 'a' Process all remaining readings
   - 'e' Display 'empty' map
   - 'f' Display 'full' map
   - 'c' Display 'combined' map
For the quickest and best results, simply start the program and press 'a', 'c', 'd', 'q'

Map_edit: See README.MAPEDIT
Prmgen: See README.PRMGEN
Navigate: See README.NAVIGATE




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Detailed Description:
        The basis of our program draws off of the basic elements of all three project done
throughout the course of the semester, and expands the extends of each to provide an easy
to use interface to interact with the Magellan Pro robot in a controlled environment. In
general, the program can be used to move the robot from a known location, to another
chosen location, by using a point and click interface. Before this can be done however,
some preparation must take place.
        Firstly, the environment that the robot is to operate in must be mapped out. The
best operating environment for the robot is a flat, level surface with smooth objects at
least as high as the robot (about 1 foot), with a thickness of greater then about 4 inches.
This ensures that all objects will be detected by the sonar sensors. For added accuracy,
the objects should also reflect a significant amount of IR light. After the getdata
program is initiated, the robot should traverse its environment, so to make a good map.
        This map is essential for the next phase of operation, in which the robot operator
must use a visual representation of the sonar map to construct the world and object
boundaries. This polygonal representation of the world has two purposes. It serves as a
visual representation to the user, and as a formal world description to the robot. The latter
is important in the Kalman filtering stage. Hence, it is important to make the
representation match the real world as closely as possible.
        Next, a PRM is generated in the world map. This is also displayed visually to the
user. Since this is a probabilistic and therefore randomly generated process, there will be
instances where the roadmap generated will not adequately suit the environment that the
robot must traverse. Different coverage determinations can be used to help approximate
the usefulness of a specific instance of a rpm, but a quick look be a human is usually
more effective. The nodes generated in this step will be used to guide the robot in the
navigate program. This is an alternative method to pathfinding which was explored in
assignment one, in which approximate cell decomposition was used. The PRM method
has several advantages for mobile robotics. First, this technique allows a roadmap to be
precomputed, and accessed later. Second, when computing paths, the rpm requires less
memory then a quadtree approach. Local path planning can also be optimized, while
quadtree cell decomposition is limited by rectangularly outlined paths.
        With the aforementioned preparation complete, the robot is ready to be navigated
with the navigate program. This program provides a simple interface for the user to
place, and subsequently control, the robot using the generated PRM. While the robot is
guided through the environment, it automatically uses Kalman filtering to keep itself on
track. Successfully performing location and rotation corrections can be vital when the
robot must traverse through tight spaces, or operated for long sessions.


Algorithms:
- Sonar Mapping
Using Dempster-Schafer theory, generate empty, full and combined sonar maps of an
area given a set of sonar readings from the area, and output the sonar maps as images




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- Configuration-space Generation
Using a polygonal map (constructed by hand based on the image created during the sonar
mapping process), generate the configuration space of the map by finding the convex hull
of each world boundary segment and world obstacle

- Probabilistic Roadmap Generation
Until the desired roadmap complexity (number of nodes) is reached, add new nodes to
the roadmap as follows:
    - Choose a random point in empty space in c-space
    - Add a node to the roadmap at this point
    - Find the <n> nearest nodes to the new node
    - Attempt to directly connect each of these nodes to the new node (via straight line)
Then, enhance difficult nodes in the roadmap (denoted by the number of failed direct-
connection attempts to nearby nodes) by adding new nodes within the immediate area of
the difficult nodes. Finally, remove connected components in the roadmap that have
fewer than <p> percent of the nodes in the whole roadmap

- Path Finding
Add nodes to the PRM representing the start and end positions of the path. Then, search
the PRM graph using A* to find a path between the start and end nodes; if none exists,
indicate failure

- Path Smoothing
Starting with the start and end points as i and j:
    - Intersect the line between i and j with the c-space
    - If no intersection occurs, delete the nodes of the path between i and j and return
    - Set k = (j - i) / 2
    - Recursively smooth the path between i and k and the path between k and j

- Robot Movement
To follow a path, at each node:
    - Rotate toward the next node on the path
    - Move to the next node on the path (in a straight line)
Keep track of the robot's odometry readings to update the estimated position of the robot.
Also keep values representing movement with zero control error. When the error-free
values are some predefined amount different from the odometry-based values, attempt to
correct the error by moving from the current estimated position to the desired (perfect)
position.

- Kalman Filtering
After each motion (straight-line or angular), use the robot's infrared sensors to sense the
surrounding area. If these measurements are within some allowable range (e.g. less than
1.0m), use them in conjunction with the predicted measurement in that direction (found
by tracing a ray from the robot's location across the internal polygonal map) to run a
Kalman filter operation. Use the output from the Kalman filter to update the robot's
estimated location.


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Evaluation:
        Much of the content learned throughout the course was put into practice during
the project. Doing so on a real robot provided an easy to understand feedback system for
our methods, and it also made us aware of how using methods in a real world
environment, as opposed to a simulated one, can give very unexpected results.
        A major issue that arose involved the inaccuracy of the robot’s odometry and
sensor; this inaccuracy was higher than was expected, and in retrospect it might have
been better to spend more time on Kalman filtering rather than considering it an “nice
extra feature.”


Notes:
Further information is available in the <unpack_dir>/docs directory, in the
following files:
    - README.MAPEDIT Detailed usage information for map_edit
    - README.PRMGEN Detailed usage information for prmgen
    - README.NAVIGATE Detailed usage information for navigate
    - README.ERRATA Description of a few known bugs/problems/issues
    - PROJECT_PROPOSAL The original project proposal
    - FILE_FORMATS Some (largely useless) information about the map_edit and
       prmgen file formats




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