During a semester-long Introduction to Robotics course (MEAM 5200), I utilized a 7-degree-of-freedom robotic arm for a pick-and-place challenge, building a strong foundation in forward and inverse kinematics, RRT (Rapidly-exploring Random Tree) planning, potential fields planning, Denavit-Hartenberg parameters, and Jacobians.
The course culminated in a final project: a pick-and-place
challenge requiring manipulation of both static and dynamic
blocks to build the tallest possible tower. For this project, my
team implemented a custom multithreaded RRT* path
planning algorithm alongside inverse kinematics, achieving
performance that was ~4x faster than a baseline RRT
implementation. After iterative refinement through both
simulation and hardware testing, our final algorithm achieved a real-world pick-and-place success rate of ~88%, reliably stacking blocks under dynamic conditions.
challenge requiring manipulation of both static and dynamic
blocks to build the tallest possible tower. For this project, my
team implemented a custom multithreaded RRT* path
planning algorithm alongside inverse kinematics, achieving
performance that was ~4x faster than a baseline RRT
implementation. After iterative refinement through both
simulation and hardware testing, our final algorithm achieved a real-world pick-and-place success rate of ~88%, reliably stacking blocks under dynamic conditions.
