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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles


    Additional title:

    Springer Aerospace Techn.


    Contributors:


    Publication date :

    2023-09-28


    Size :

    34 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles

    Chai, Runqi / Tsourdos, Antonios / Savvaris, Al et al. | IEEE | 2021


    Fast trajectory planning based on in-flight waypoints for unmanned aerial vehicles

    Babaei, A.R. / Mortazavi, M. | Emerald Group Publishing | 2010



    Safe Corridor Constrained Trajectory Planning for Formation Flight of Fixed-Wing Unmanned Aerial Vehicles

    Ji, Jialin / Peng, Yiyang / Zhao, Changchun et al. | Springer Verlag | 2024