Integrating Optimized High-Speed Autonomous Control Systems
School Name
Governor's School for Science and Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Oral Presentation Award
1st Place
Written Paper Award
1st Place
Abstract
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 a high degree of control. 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.
Recommended Citation
Johnson, James, "Integrating Optimized High-Speed Autonomous Control Systems" (2018). South Carolina Junior Academy of Science. 35.
https://scholarexchange.furman.edu/scjas/2018/all/35
Location
Neville 206
Start Date
4-14-2018 9:45 AM
Presentation Format
Oral and Written
Integrating Optimized High-Speed Autonomous Control Systems
Neville 206
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 a high degree of control. 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.
