使用Kalman filter算法，估算行星间的运动轨迹。
Introduction
This projects asks you to track a collection of asteroids and:
- estimate their future location
- pilot a craft around them to a goal location
The asteroid field
The asteroids travel in a square with corners (-1,-1) and (1,1). Asteroids outside of the bounding box are considered “out of play” for the moment.
Time is delimited in discrete steps (t=0,1,2,. . . ).
Each asteroid’s path is specified by two parametric equations in time.
Each asteroid has a unique set of parameters ax, bx, cx, ay, by, cy, tstart.
Given this specification, each asteroid’s motion can be modeled using x, y, dx, dy, ddx, ddy.
After drifting outside of the bounding box, most asteroids never return, but a few ([5%) reemerge onto the field later.
Student submission
Your work on this project is to implement the class Pilot in the pilot.py file. The Pilot class must implement three methods:
- observe_asteroids: called once per time step, informing the pilot of the latest asteroid measurements. Measurements will include Gaussian noise. Only asteroids currently in the field are measured.
- estimate_asteroid_locs: predict the locations of asteroids in the time step after the latest observation.
- next_move: given the craft’s current state (and previously obtained asteroid observations), choose the next move for the craft to execute.
Task 1: estimation
Estimation counts for the majority of the credit in this project.
On each time step, the pilot will be asked to estimate the locations of all asteroids on the next time step.
The pilot’s estimates from the prior step will be compared with the asteroids’ current locations. Estimates within min_dist will be considered matches.
The estimation is successful if 90% of the asteroids currently active match the prior step’s estimates.
Task 2: navigation
The navigation task is intended to be tackled after completing the estimation task. Your navigation code will likely make use of your estimation code.
The task initializes a craft below the asteroid field and asks you to pilot it through the field and into a goal area above it. (y ] 1.0)
The craft (implemented as CraftState in craft.py) has the following properties:
- current position, heading, and velocity (x, y, h, v)
- performance characteristics (max_speed, speed_increment, angle_increment)
Each move by the craft is specified by:
- angle change: the craft may turn left, right, or go straight. Turns adjust the craft’s heading by angle_increment.
- speed change: the craft may accelerate, decelerate, or continue at its current velocity. Speed changes adjust the craft’s velocity by speed_increment, maxing out at max_speed.
Testing your code
Two local test scripts are provided with this project: test_all.py runs your code against all available test cases. This script closely mirrors the auto-grader we will use to grade your work. To run:
$ python test_all.py
test_one.py runs your code against a single test case, with additional display options. This script is intended to assist debugging.
$ python test_one.py --help
usage: test_one.py [-h] [--case {1,2,3,4,5,6,7}]
[--display {turtle,text,none}] {estimate,navigate}
positional arguments:
{estimate,navigate} Which method to test
optional arguments:
-h, --help show this help message and exit
--case {1,2,3,4,5,6,7}
test case number
--display {turtle,text,none}
Test cases are provided in the cases subdirectory. We will use similar but different cases to grade your code.
These testing suites are NOT complete, and you may 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.
Submitting your assignment
Your submission will consist of the pilot.py file (only) which will be uploaded to Gradescope. Do not archive (zip,tar,etc) it. Your code must be valid python version 3 code, and you may use the numpy library.
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.