2019-07-23 –, Elm B
Trajectory optimization is a fundamental tool for controlling robots with complex, nonlinear dynamics. TrajectoryOptimization.jl is devoted to providing a unified testbed for developing, comparing, and deploying algorithms for trajectory optimization.
Trajectory optimization is a powerful tool for motion planning, enabling the synthesis of dynamic motion for complex underactuated robotic systems. This general framework can be applied to robots with nonlinear dynamics and constraints where other motion planning paradigms---such as sample-based planning, inverse dynamics, or differential flatness---are impractical or ineffective.
TrajectoryOptimization.jl has been developed for the purpose of collecting and developing state-of-the-art algorithms for trajectory optimization under a single, unified platform that offers the user state-of-the-art performance, an intuitive interface, and versatility. Initial results using a novel algorithm written in Julia already beat previous methods leveraging NLP solvers such as Ipopt and Snopt.
I am a PhD student in the Mechanical Engineering department at Stanford University, advised by Dr. Zachary Manchester. My research focuses on developing algorithms to control robots with complex, nonlinear, and under-actuated dynamics. I am one of the two primary developers of TrajectoryOptimization.jl.