Abstract
Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learning-based approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.
Highlights
U NMANNED aerial vehicles (UAVs) are vehicles that can fly without a human pilot onboard [1]
This paper provides a comprehensive survey of this crucial paradigm of artificial intelligence (AI) approaches covering all UAV navigation scenarios, identifying the prevailing gap in the literature that inspired the current research
CONTRIBUTION This study focuses on different AI approaches, such as deep learning, mathematical optimization methods, reinforcement learning, and transfer learning, for different types of UAV navigation
Summary
U NMANNED aerial vehicles (UAVs) are vehicles that can fly without a human pilot onboard [1]. Various navigation methods have been proposed to date and are categorized into three groups: inertial navigation, satellite navigation, and vision-based navigation [1] Neither of these approaches is perfect; it is crucial to choose the optimal technique for autonomous UAV navigation based on the mission at hand [1]. There are many challenges in applying AI in autonomous navigation, such as reducing training time, reducing computational power, reducing complexity, updating information for extended periods, and quick adaptation to new environments [25]. Pandey et al discussed different single solution-based and population-based meta-heuristic approaches in [28], including simulated annealing (SA) [48], tabu search [49], evolutionary computation, and swarm intelligence [40], [50]. They analyzed various algorithms and highlighted research gaps. Lu et al [1] surveyed vision-based methods of UAV navigation while focusing on visual localization
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