Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently-Reference-Cited by-同舟云学术

Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently

Published:2022-10 Issue:5 Volume:42 Page:40-113
ISSN:1066-033X
Container-title:IEEE Control Systems
language:
Short-container-title:IEEE Control Syst.

Author:

Malyuta Danylo¹,Reynolds Taylor P.²,Szmuk Michael²,Lew Thomas³,Bonalli Riccardo⁴,Pavone Marco⁵,Açıkmeşe Behçet⁶

Affiliation:

1. SpaceX in Redmond, Washington, USA

2. Research Scientist, Amazon Prime Air, Seattle, Washington, USA

3. Aeronautics and astronautics, Stanford University, Stanford, California, USA

4. Department of Aeronautics and AstronauticsStanford University

5. Associate Professor of Aeronautics and astronautics, Stanford University, Stanford, California, USA

6. Professor, University of Washington, Seattle, Washington, USA

Funder

National Science Foundation (NSF) Cyber-Physical Systems (CPS) program

NSF CAREER

Office of Naval Research

Air Force Office of Scientific Research

NSF CPS program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Modeling and Simulation,Control and Systems Engineering,Electrical and Electronic Engineering,Modeling and Simulation,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5488303/9905409/09905530.pdf?arnumber=9905530

Reference256 articles.

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2. Approximate solution of the trust region problem by minimization over two-dimensional subspaces

3. Sequential Quadratic Programming

4. Path-constrained trajectory optimization using sparse sequential quadratic programming

5. SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization

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