Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.
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.
Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA.
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Xu
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Li
D000465:Algorithms; D000818:Animals; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D003299:Cooperative Behavior; D005716:Game Theory; D006801:Humans; D008954:Models, Biological; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted