Abstract
The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms.
Highlights
Interest in small unmanned aerial vehicles (SUAVs) has continually been increasing due to their utility in numerous applications ranging from scientific data acquisition to military surveillance
The second case shows a neurocontroller evolved using a typical commercial SUAV model to demonstrate the Neuroevolution of Augmenting Topologies (NEAT)-based training approach’s applicability to aerial vehicles, and the third test case presents an instance of SUAV thermal soaring
This paper presented a method of evolving neurocontrollers for autonomous soaring based on the neuroevolution of augmenting topologies (NEAT) algorithm
Summary
Interest in small unmanned aerial vehicles (SUAVs) has continually been increasing due to their utility in numerous applications ranging from scientific data acquisition to military surveillance. When considering the stringent energy management required for sustained soaring, this susceptibility of the approach becomes a major obstacle towards successful implementation Due to these considerations, in addition to modern improvements in the accessibility of neural network training algorithms, the research in dynamic soaring implementation has seen a recent interest in the field of neurocontrol. This paper, building upon a previous work by Perez et al [20] introducing the applicability of the NEAT algorithm to optimal dynamic soaring, presents a combined trajectory planning and control strategy that can generate sustainable, energy-efficient soaring trajectories with simple and computationally inexpensive neural networks. Aerospace 2021, 8, 267 dynamic soaring to thermal soaring, illustrates the similarities in energy-efficiency between the resulting neurocontrol trajectories and those of biological albatross birds, and provides a comparison of the simulated results against numerical trajectory optimization, as well as other neural network training methods.
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