While autonomous vehicles are growing in popularity, there exists a tradeoff between control and speed. With control directly affecting safety of a vehicle, it is prioritized at the detriment of speed. However, with speed being essential to emergency responses, methods are required for optimizing speed while retaining high accuracy. This research project aims to convert a proportional integral derivative controller, visual servoing system, and potential field navigator into Robot Operating System (ROS) nodes, integrate them with the Rapid Autonomous Complex-Environment Competing Ackermann-steering Robot (RACECAR) developed at the Massachusetts Institute of Technology, tune them for speed, and integrate them together to complete a test course of various obstacles. Our optimized algorithm variants achieved an increase in processing speed by 20 times, but did not outcompete completely rewritten variants in the test courses. This indicates that the optimization methods provide benefits, but that algorithms themselves must be rewritten to operate at the highest efficiency. Future research will incorporate the optimization methods into the rewritten algorithms to benefit from both effects. Additional algorithms will also be considered for revision and optimization.
Johnson,, James V.; DeGennaro, Alfred; and Gregson, Kenneth
"Integrating Optimized High-Speed Autonomous Control Systems,"
Journal of the South Carolina Academy of Science: Vol. 16
, Article 4.
Available at: https://scholarcommons.sc.edu/jscas/vol16/iss2/4