Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles

Runqi Chai*, Kaiyuan Chen, Lingguo Cui, Senchun Chai, Gokhan Inalhan, Antonios Tsourdos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In this chapter, a fast chance-constrained trajectory generation strategy is presented that uses convex optimization and convex approximation of chance constraints to settle the problem of unmanned vehicle path planning. A path-length-optimal trajectory optimization model is developed for unmanned vehicles, taking into account pitch angle constraints, curvature radius constraints, probabilistic control actuation constraints, and probabilistic collision avoidance constraints. Afterward, the convexification technique is applied to convert the nonlinear problem into a convex form. To handle probabilistic constraints in the optimization model, convex approximation techniques are used to replace probabilistic constraints with deterministic ones while maintaining the convexity of the optimization model. The proposed approach has been proven effective and reliable through numerical results from case studies. Comparative studies have also shown that the proposed design generates more optimal flight paths and has improved computational performance compared to other chance-constrained optimization methods.

Original languageEnglish
Title of host publicationSpringer Aerospace Technology
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages34
Publication statusPublished - 2023
Externally publishedYes

Publication series

NameSpringer Aerospace Technology
VolumePart F1477
ISSN (Print)1869-1730
ISSN (Electronic)1869-1749

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.


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