LAB 9

Lab 9

Write Up:

This lab involves creating a mapping of the obstacle layout based off of measurements from the front TOF! This lab involves 3 major parts: in place rotation, data recording, data interpretation.

IN PLACE ROTATION

This part of the lab took a while to execute since figuring out constant rotation with the prior PID logic, especially since the gyroscope readings seemed to drift. From the Arduino perspective, the PID code from lab 6 and gyro readings from lab 3 were fused into a new method called pid_map(). In order to utilize values from the python perspective, the distance, the time, and the rotation speed was stored in an array to be sent back through the SEND_DATA command (documented under Lab 6). To record more data for better accuracy, the interval between consecutive data collections was set to 250 ms. Another major edit made from Lab 6 PID was the PWM set Speed. As seen below, rather than including a potential reversal of PWM direction, only single direction PWM is maintinaed to avoid constant togggling bakc and forth during the turn to limit noisy and shifting data. The setpoint angle is approximately 35, though it never actually turns out to reach that particualr rotation speed.

The final product of the turn is included below: Turning Video

Notes about the turn: As can be seen from the video, there is a slight displacement that occurs during the turn. This seemed unavoidable, despite adding tape to lower the static friction. However, the translation is relatively minimal in real-time, leading to minimal impact on the final data as can be seen in the final mapping images.

DATA RECORDING

The data was sent back to jupyter lab in a similar fashion as Lab 6. The dataq was also parsed and appropriately assinged to the proper arrays. Data was collected at 4 different points: (5,-3), (5,3), (0,3), and (-3,-2). After each trial, the data was stored in a particular array and then stored in an appropriately named text file as seen by the snippet below:

While recording the data, the robot was alwasy started in the same orientation, pointing along the positive x-axis. Additionally, all the data was taken along the -z axis, meaning the robot would turn in the counter-clockwise direction for achieving the postive 25 DPS.

DATA INTERPRETATION

This part of the lab happened entirely from jupyter lab. The first step in interpreting the data involved attaining the various angles the robot achieved during the turn. Since the stopping of the robot during the turn was manual, there were certain moments of overshoot that persisted. The script for achieving the angle is included below:

THe angle at each step is calculated by find the delta_T based on current time and previous time and multiplying it by the gyro’s rotational speed reading at that time step. The angle is accumulated throughout rotation to better represent the 360 degree rotation.

Based on these angles, the following polar plots are plotted to provide a better representation of the measurements during the rotation

As can be seen the polar plots are relatively hard to comprehend and as such to get a better view, the polar coordinates are converted rectangular coordinates to better represent each perspective:

From the 4 cartesian coordinate perspectives, all four corners are significantly clearer in comparison to the polar graph. Additionally, the sides of the bigger box are also clear, though the sides are slanted due to the positional drift during the turn analyzed before. However, the major issue with these four cartesian plots is the lack of clarity pertaining to the smaller box. This occurs since not all four rotation spots are equidistant from the box. As such, the corrdinate that provides the best perspective of the side(s) of the smaller box was the point (-3,-2) since it was relatively closer to the smaller obstacle. The uncertainty surround this paraticular obstacle is more exaggerated and apparents when the box’s dimensions are superimposed upon the TOF based mapping. The process through which the plar coordiantes were converted to rectangular coordinates is included in the code snippet below:

To incorporate the data recorded at each rotation point to provice a more cohesive picture of the map, several linear translatations had to be incorporated. The first aspect included converting feet to mm using the conversion factor of 304.8 mm/ft. For each point data point, the values were shifted appropriately to make the center of the whole world 0,0. For instance, as seen in the image below, if the data point was recorded at (5, 3), the point is appropriately shifted up in the x_axis by (5 * 304.8) and in the y_axis by (3 * 304.8). This allows placing these set of points in the upper right hand corner of the overall graph. Each of these translational components are appropriately included bleow to build the total array of x & y points. For reference x_1 & y_1 correspond to data from (5-,3), x_3 & y_3 correspond to data from (5,3), x_4 & y_4 correspond to data from (0, 3), and x_5 & y_5 correspond to data from (-3,-2).

The final combined plot with color coding is included at the very end.

GROUND TRUTH COMPARISON

The final aspect of the lab involved comparing the measured data with the groun truth. Utilizing this would help understand which objects were properly detected and the potential error of the TOF sensor. The ground truth was determined by measureing the key corners of the map. The three sets that were utilized were the x & y corners of the overoutlines, the box’s outlines, and the smaller box’s outline. The points list for the obstacles are included in the snippet below:

The superimposed ground truth and the combined measurements plot is included below:

There are two plots included here. The second is the color-coded plot to help signify the various properties of the map that each rotation point helps pick-up. The first plot helps identify the discrepancy between the measured values and the ground truth. THe corners are extremely close to that of what was measured, excpet for minor offsets. The bigger box, as expected, has seemingly slanted sides due to the aforementioned positional drift during the in-place turn. The most glaring discrepancy is the lack of clarity for the smaller box. The ground truth is in a different location comparied to the measruement, and the measurements do not provide a proper definition.

ACKNOWLEDGEMENTS Swapnil Barot (Lab partner for this particular lab)

Important notes: An instance were the robot turns twice and measures data was taken. However, the data seemed too noisy and so this method was not used to measure data.