This week, I worked with the team in the final stages of integration. At the start of the week, I helped Alvin with the motion planner. I downloaded the Embotech Forces Pro license and software for the Jetson TX1 and tried to debug issues with the Forces Pro solver not respecting constraints and outputting motion plans with weird quirks (reluctance to move backwards or yaw beyond a certain angle). We tried to simplify the model, or switch to scipy.optimize.minimize, but the simplified models were very inaccurate and the minimize method took upwards of 17.5 seconds to solve a motion plan. Eventually, we decided to switch to the preliminary motion planner I had come up with two weeks ago, where we only changed the x and y position of the drone while keeping yaw and elevation fixed. This seemed to work well in simulation, and more importantly the drone’s motion was relatively stable, easing some of our concerns with safety.
I also worked on dealing with the local position issues with the rest of the team in lab. I collected local position data for a variety of test paths in order to understand how the drone’s position coordinates mapped to real world coordinates. I also helped in the camera calibration to try and debug issues in the conversion from pixel coordinates to real world distances. In the process of dealing with these issues, I helped the team reflash the drone’s flight controller with the Ardupilot firmware in efforts to make the position data more accurate by adding an optical flow camera. However, we eventually encountered issues with the GPS fix in Ardupilot and decided to switch back to Pixhawk.
The rest of the time spent with the team in lab basically involved debugging an assortment of miscellaneous issues arising with the flight controller, such as a loss of binding with the radio transmitter and a refusal to switch to auto mode mid-flight. Eventually, despite the turbulent weather, we got the drone airborne and hovering in place, but decided against running our full motion planning stack due to wild behavior of the drone. Instead, we focused our energies on getting as many metrics as we could from our actual setup (target precision and recall, video FPS, streaming latency), and getting the rest of the metrics from simulation.