Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems
Abbreviated Journal Title
constrained trajectory planning; bio-inspired method; optimal trajectory; planning; VIRTUAL MOTION CAMOUFLAGE; COSTATE ESTIMATION; OPTIMIZATION; CONVERGENCE; COLLOCATION; STRATEGY; PURSUIT; STATE; PREY; Engineering, Multidisciplinary; Materials Science, Biomaterials; Robotics
Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.
Bioinspiration & Biomimetics
"Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems" (2014). Faculty Bibliography 2010s. 6321.