CS 8803 – 001: Artificial Intelligence for Robotics Project 3 : Ice Rover Solved

35.00 $


CS 8803 – 001: Artificial Intelligence for Robotics Project 3 : Ice Rover Solved
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Project Description

The goal of this project is to give you practice implementing a SLAM module and a robot control system that uses it to navigate through a world.

Part A (worth 33%) asks you to build a SLAM system that can keep track of where your robot is located after a series of movements (using measurements to landmark beacons). Complete the SLAM class in the ice_rover.py file. The movements and measurements will have noise.

Your robot will drop via parachute onto the ice sheet. To help it navigate, a set of solar powered beacons have also been dropped onto the ice (they are flat, and the rover can roll over them). These beacons are scattered randomly, but will melt themselves into the ice upon placement, so do not move around.

Your robot should make it’s own map of the environment, using it’s starting location as the origin (0,0), and taking advantage of the signals from the beacons to maintain a good estimate of its own position relative to the initial (0,0) origin as it slips around on the ice. (The ice may introduce movement noise, in that your robot may not turn or move the exact distances you command.)

In part A, your robot will receive a set of measurements as it follows a pre- scripted set of movements, and your SLAM module will need to calculate and report your robot’s position after each movement and measurement (relative to the arbitrary (0,0) staring point).

Part B (worth 67%) asks you to navigate your robot around the environment to take a sample at each of a list of sample sites. Complete the WayPointPlanner class in the ice_rover.py file.

The science team has manually planted a set of special beacons (called “sites”) in specific locations which your robot must navigate to and sample.

Note: There is a time penalty for attempting to sample an invalid site. Your robot has a maximum turning angle and distance that it can move each turn. Movement commands that exceed these values will be cause the robot to not move.

Your robot will not have a map of the environment, but (in part B) it will receive a list of the absolute locations of these sample points. Your robot will have to discover the mapping between it’s own “relative” map that places the origin (0,0) at the point where the robot landed and the “absolute” locations where sample sites are located. Hint: Once you sample a site, it will remove itself from the “todo_sample” list, so as soon as you sample your first site, you should have a very good idea of where you are in “absolute” coordinates.

Note that your robot will never have access to the map of where the randomly scattered beacons are located, only a list of the absolute locations of the manually planted sample sites. (Each sample site is marked with a beacon that behaves in exactly the same way as the scattered beacons, but is called a “site” instead of a “beacon” to indicate that it was manually placed and marks a sample site.)


Submitting your Assignment


Your submission will consist of the ice_rover.py file (only) which will be uploaded to Canvas. Do not archive (zip,tar,etc) it. Your code must be valid python version 2.7 code, and you may use external modules such as numpy, scipy, etc that are present in the Udacity Runaway Robot auto-grader. [Try to ensure that your code is backwards compatible with numpy version ‘1.13.3’ and scipy version ‘0.19.1’]

Your python file must execute NO code when imported. We encourage you to keep any testing code in a separate file that you do not submit. Your code should also NOT display a GUI or Visualization when we import or call your function under test. If we have to manually edit your code to comment out your own testing harness or visualization you will receive a -20 point penalty.


Testing Your Code


We have provided testing suites similar to the one we’ll be using for grading the project, which you can use to ensure your code is working correctly. These testing suites are NOT complete, and you will need to develop other, more complicated, test cases to fully validate your code. We encourage you to share your test cases (only) with other students on Piazza.

You should ensure that your code consistently succeeds on each of the given test cases as well as on a wide range of other test cases of your own design, as we will only run your code once per graded test case. For each test case, your code must complete execution within the proscribed time limit (5 seconds) or it will receive no credit. Note that the grading machine is relatively low powered, so you may want to set your local time limit to 2.5 seconds to ensure that you don’t go past the CPU limit.

We are using the bonnie autograder system which allows you to upload and grade your assignment with a remote / online autograder. See the submit.py file posted as part of the assignment on Canvas for details. You are not required to use this online/autograder feature, but if you do, it will give you more assurance that your code will work correctly with our autograder when we grade the files you submit on Canvas. We may also choose to use the last grade you receive via the remote autograder as your ftnal grade at our discretion. (See the “Online Grading” section of the Syllabus.)