The Planning Gap Demo

An analysis of the traditional divide between combinatorial graph search and continuous trajectory optimization. The Planning Gap Demo turns that idea into something you can manipulate directly and watch respond.

An analysis of the traditional divide between combinatorial graph search and continuous trajectory optimization. It focuses on Combinatorial Optimization, Trajectory Optimization, Local Minima, Completeness from the “The Gap Between Discrete and Continuous Planning” section.

You can drag elements directly on the canvas; the “Reset Environment” button run actions or reset the demo. Every change updates the visualization in real time, so you can see exactly how each variable affects the outcome.

This course explores the Graphs of Convex Sets (GCS) framework, a revolutionary approach to motion planning that unifies discrete graph search with continuous trajectory optimization. It addresses the fundamental challe… This demo is the interactive piece for the “The Gap Between Discrete and Continuous Planning” section. Open the full Motion Planning with Graphs of Convex Sets course wiki at https://www.getlykke.com/explore/public/motion-planning-with-graphs-of-convex-9ef465dc-e6a7-499a-91ea-ab90b928956f for notes, flashcards, quizzes and the other demos.

You'll build intuition for Combinatorial Optimization, Trajectory Optimization, Local Minima, Completeness — and, crucially, see how they behave when you change the inputs, which is hard to get from a textbook or lecture on Motion Planning with Graphs of Convex Sets alone.