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Optimized spaceship trajectories using SCvx (Successive Convexification) in dynamic/static obstacle environments (planets & satellites) for precise landing. Part of the ETH Zurich’s “Planning & Decision Making for Autonomous Robots” course.

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francescobondi02/scvx-spaceship-trajectory-optimization

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PDM4AR-exercises

All the necessary instructions are on our website: https://pdm4ar.github.io/exercises/.

Highlights from the previous year

Using safety certificates Informed RRT* Navigating through an asteroids' field
example-3.mp4

But remember that the first time it is never easy...

Out of control Some seeds are tougher The Drunkard's Walk
PDM4AR-final21-staticenvironment0-PDM4AR-EpisodeVisualisation-figure1-Animation (1)
ezgif.com-gif-maker.mp4
ezgif.com-gif-maker.1.mp4

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Optimized spaceship trajectories using SCvx (Successive Convexification) in dynamic/static obstacle environments (planets & satellites) for precise landing. Part of the ETH Zurich’s “Planning & Decision Making for Autonomous Robots” course.

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