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Publication - Professor Arthur Richards

    Inter-sample avoidance in trajectory optimizers using mixed-integer linear programming

    Citation

    Richards, A & Turnbull, O, 2015, ‘Inter-sample avoidance in trajectory optimizers using mixed-integer linear programming’. International Journal of Robust and Nonlinear Control, vol 25., pp. 521-526

    Abstract

    This paper proposes an extension to trajectory optimization using mixed-integer linear programming. The purpose of the extension is to ensure that avoidance constraints are respected at all times between discrete samples, not just at the sampling times themselves. The method is very simple and involves applying the same switched constraints at adjacent time steps. This requires fewer additional constraints than the existing approach and is shown to reduce computation time. A key benefit of efficient inter-sample avoidance is the facility to reduce the number of time steps without having to compensate by enlarging the obstacles. A further extension to the principle is presented to account for curved paths between samples, proving useful in cases where narrow passageways are traversed.

    Full details in the University publications repository