Recurrent neural networks for ornithopter trajectory optimization

Abstract

Path planning is a widely studied subject due to its vast number of applications, specially for robots and unmanned vehicles. Strategies to solve it can be cate gorised as classical methods and heuristic methods, each one with its own advan tages and disadvantages. Generally speaking, analytical methods are very complex for actual applications, whereas the heuristic methods are penalized by the size of the search space. For the case of unmanned aerial vehicles this penalization cannot be afforded, since due to weight and reaction time constrains, paths should be computed on line with fast and computationally light algorithms. In this work the use recurrent neuronal networks to contour this problem is proposed. The neuronal network is tasked with learning the underlying optimal trajectory flight dynamics, which are in turn numerically estimated by a time consuming heuristic method. More precisely, a recent heuristic method (OSPA) is used to compute a set of optimal trajectories for the ornithopter and then, the neuronal network is tasked with learning the underlying function from it. The goal is to obtain similar performances to the heuristic method with much faster computation times. The effectiveness and efficiency of the proposed algorithm are demonstrated through numerical simulations on validation data sets. In addition, far from blindly ap plying a recurrent neuronal network, a mathematical framework will be developed in other to justify the choices made and the resulting performance. Such frame work will be supported by the universal approximation theorem, the algebraic feedforward neuronal network equations and the maximum likelihood method.