  The problem of optimizing a sequence of tasks for a robot, also known as
multi-point manufacturing, is a well-studied problem. Many of these solutions
use a variant of the Traveling Salesman Problem (TSP) and seek to find the
minimum distance or time solution. Optimal solution methods struggle to run in
real-time and scale for larger problems. In online planning applications where
the tasks being executed are fast, the computational time to optimize the
ordering can dominate the total execution time. The optimal solution in this
application is defined as the computational time for planning plus the
execution time. Therefore, the algorithm presented here balances the quality of
the solution with the total execution time by finding a locally optimal
sequence. The algorithm is comprised of waypoint generation, spatial
clustering, and waypoint optimization. Significant improvements in time
reduction were seen and validated against a base case algorithm in simulation
and on a real UR5 robot.
