Abstract
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 language | English |
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Title of host publication | Springer Aerospace Technology |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 131-164 |
Number of pages | 34 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Publication series
Name | Springer Aerospace Technology |
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Volume | Part 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.