Week 2 - Uncertainty
The goal of this assignment is to become familiar with the simulator and explore uncertainties in the sensors and motion.
Task 1 – The simulator
Download the repository containig the simulator from GitHub https://github.com/Robotics-BUT/MPC-MAP-Student, and become familiar with it (see the Simulator section).
- Explore the
private_vars,read_only_vars, andpublic_varsdata structures. - Become familiar with the sequence of the operations in the infinite simulation loop in
main.mandalgorithms/student_workspace.m. - Load different maps via
algorithms/setup.mand set different start positions. - Try various motion commands in
algorithms/motion_control/plan_motion.mand observe the robot's behaviour.
No output is required in this step.
Task 2 – Sensor uncertainty
The robot is equipped with an 8-way LiDAR and a GNSS receiver. Determine the standard deviation (std) sigma for the data from both sensors by placing the robot in a static position (zero velocities) in suitable maps and collecting data for at least 100 simulation periods. Discuss whether the std is consistent across individual LiDAR channels and both GNSS axes. Plot histograms of the measurements.
Task 3 – Covariance matrix
Use the measurements from the previous step and MATLAB's internal cov function to assemble the covariance matrix for both sensors. Verify that the resultant matrix is of size 8×8 for the LiDAR and 2×2 for the GNSS. Ensure that the values on the main diagonal are equal to sigma^2, i.e., variance=std^2`.
Task 4 – Normal distribution
Create a function norm_pdf to assemble the probability density function (pdf) of the normal distribution. The function should accept three arguments: x (values at which to evaluate the pdf), mu (mean), and sigma (standard deviation). Utilize this function along with the sigma values from Task 2 (e.g., for the 1st LiDAR channel and the X GNSS axis) to generate two pdf illustrating the noise characteristics of the robot's sensors, and plot them in a single image (use mu=0 in both cases).
Task 5 – Motion uncertainty
Uncertainty exists not only in the measurements (sensor data) but also in the motion. Load the indoor_1 map and attempt to navigate the robot to the goal location without using sensors. To achieve this, apply an appropriate sequence of motion commands inside the plan_motion.m function. Save a screenshot of a successful run and discuss the potential sources of uncertainty in the robot's motion.
Submission
To implement the tasks, use only the algorithms directory; do not modify the rest of the simulator. The solution must run without errors in a fresh instance of the simulator and must generate the graphical outputs included in the report.
Create a single A4 report to the provided template that briefly describes your solution, with a few sentences for each task and an image where applicable.
Send the report and a zip archive containing the algorithms directory to the lecturer's e-mail by Wednesday at 23:59 next week.
For those using Git for version control, you can send a link to your public GitHub repository instead of the zip file. The repository must contain the simulator with the algorithms directory with your solution. Please tag the final version with week_2 tag to ensure easy identification.