Uploaded June 2017 | Updated September 2026, 19 minutes ago
This talk was given by undergraduate Jonathan Lynn during the 11th Annual Computer Science Undergraduate Research Symposium in 2016. Jonathan‘s research was supervised by Dr. Ron Alterovitz.
"Evaluation of the Performance and Cost of Cloud-Based Robot Motion Planning"
Prior work has shown that the computation of a robot’s motion plan can be split between the robot’s own low-powered processor and a cloud-based high-powered compute-optimized server. We evaluate the empirical performance of this method and the new cost model accompanying it with the compute service offered by Amazon Elastic Compute Cloud (Amazon EC2) for a robot using 8 degrees of freedom to complete a task in a dynamic environment, comparing across different data centers and server instances with an investigation of the roadmap complexity, the network time, and the total time required to complete the task.
Jonathan Lynn is a senior Computer Science and Global Studies double-major from Chapel Hill, NC and a member of the Computational Robotics Group. He hopes to further his interests in robotics and machine learning by pursuing a PhD in Computer Science after taking a gap year upon graduation.
cs.unc.edu/academics/undergraduate/symposium/symposium-2017/
This talk was given by undergraduate Jonathan Lynn during the 11th Annual Computer Science Undergraduate Research Symposium in 2016. Jonathan‘s research was supervised by Dr. Ron Alterovitz.
"Evaluation of the Performance and Cost of Cloud-Based Robot Motion Planning"
Prior work has shown that the computation of a robot’s motion plan can be split between the robot’s own low-powered processor and a cloud-based high-powered compute-optimized server. We evaluate the empirical performance of this method and the new cost model accompanying it with the compute service offered by Amazon Elastic Compute Cloud (Amazon EC2) for a robot using 8 degrees of freedom to complete a task in a dynamic environment, comparing across different data centers and server instances with an investigation of the roadmap complexity, the network time, and the total time required to complete the task.
Jonathan Lynn is a senior Computer Science and Global Studies double-major from Chapel Hill, NC and a member of the Computational Robotics Group. He hopes to further his interests in robotics and machine learning by pursuing a PhD in Computer Science after taking a gap year upon graduation.
cs.unc.edu/academics/undergraduate/symposium/symposium-2017/
