Trajectory optimization with obstacles avoidance via strong duality equivalent and hp‐pseudospectral sequential convex programming

A trajectory optimization algorithm is developed in this article to compute a trajectory that avoids obstacles for an unmanned aerial vehicle and minimize the load factor. Quadratic and polyhedron obstacles are both considered in this article. To satisfy the safety distance requirements in engineeri...

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Vydáno v:Optimal control applications & methods Ročník 43; číslo 2; s. 566 - 587
Hlavní autoři: Xia, Weibo, Wang, Weihong, Gao, Chuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Glasgow Wiley Subscription Services, Inc 01.03.2022
Témata:
Algorithms
Computational geometry
Convergence
Convexity
Equivalence
hp‐pseudospectral method
Mathematical programming
Obstacle avoidance
Optimization
Safety
sequential convex programming
strong duality
Trajectory optimization
Unmanned aerial vehicles
ISSN:0143-2087, 1099-1514
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Shrnutí:A trajectory optimization algorithm is developed in this article to compute a trajectory that avoids obstacles for an unmanned aerial vehicle and minimize the load factor. Quadratic and polyhedron obstacles are both considered in this article. To satisfy the safety distance requirements in engineering practice, a distance‐based obstacle avoidance constraint is formulated, which restricts the minimum distance that a vehicle can approach the obstacle. Strong duality equivalent converts the constraints into equivalent forms without introducing binary variables. Subsequently, the trajectory optimization problem is numerically solved via a hp‐pseudospectral sequential convex programming method, which iteratively solves a sequence of second‐order cone programming subproblems until convergence is attained. Furthermore, a modified trust‐region constraint and a customized nominal trajectory update rule are proposed to accelerate the convergence of the algorithm. A group of simulations are performed to demonstrate that the algorithm is computationally efficient and capable of generating a trajectory that satisfies safety distance requirements, preventing conservativeness in the trajectory, reducing the incidence of intersample constraints violation.
Bibliografie:Funding information
Aeronautical Science Foundation of China, 20175751028; Defense Industrial Technology Development Program of China, JCKY2018601B101
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0143-2087
1099-1514
DOI:10.1002/oca.2839