Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree

Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree Andreas Orthey, Benjamin Fr esz, Marc Toussaint Abstract -- We present an algorithm to visualize local minima in a motion planning problem, which we call the motion planning explorer . The input to the explorer is a planning problem, a sequence of lower-dimensional projections of the configuration space, a cost functional and an optimization method. The output is a local-minima tree, which is interactively grown based on user input. We show the motion planning explorer to faithfully capture the structure of four real-world scenarios. I NTRODUCTION In motion planning, we develop algorithms to move robots from an initial configuration to a desired goal configuration. Such algorithms are essential for manufacturing, autonomous flight, computer animation or protein folding [14]. Most motion planning algorithms are black-box algorithms 1 . A user inputs a goal configuration and the algorithm returns a motion. In real-world scenarios, however, black-box algorithms are problematic. There is no way to guide or prevent motions. Humans users cannot visualize the internal mechanism of the algorithm.