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Everything about Trajectory optimization

Process of developing trajectory performance

Trajectory optimization is the process of designing a trajectory that minimizes some measure of performance while satisfying a set of constraints. Generally speaking, trajectory optimization is a technique for computing an open-loop solution to an optimal control problem. It is often used for systems where computing the full closed-loop solution is not required, impractical or impossible. If a trajectory optimization problem can be solved at a rate given by the inverse of the Lipschitz constant, then it can be used iteratively to generate a closed-loop solution in the sense of Caratheodory. If only the first step of the trajectory is executed for an infinite-horizon problem, then this is known as Model Predictive Control (MPC). Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Trajectory optimizationA 5-minute orientation
  2. Skim a recent paper: Geometrically Constrained Trajectory Optimization for MulticoptersInfluential work since 2022
  3. Pick up a classic book: Spacecraft trajectory optimizationThe most-reprinted book on the subject
  4. Read the landmark paper: Single-Train Trajectory OptimizationCited 322 times
  5. Go sideways: Pseudospectral optimal controlA closely related topic
  6. Go sideways: GPOPS-IIA closely related topic

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🗂️ The subject at a glance

  1. History
  2. Applications
  3. Terminology
  4. Trajectory optimization techniques
  5. Comparison of techniques

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics