Bio-Inspired Predator-Prey Large Spacial Search Path Planning

Xu, Jeffrey; Pichon, Thomas Le; Spiegel, Isaac A.; Hauptman, Dustin; Keshmiri, Shawn

doi:10.1109/aero47225.2020.9172365

Cited by 1 publication

(1 citation statement)

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“…Whenever the predator consumes a solution, new solutions are generated around the predator. Moreover, mutation and crossover are the main parameters that help the predators reach the optimal solution [105].…”

Section: ) Improved T -Distribution Evolution Algorithmmentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

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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 learningbased 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.

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Section: ) Improved T -Distribution Evolution Algorithmmentioning

confidence: 99%