| Abstract |
Navigation and trajectory planning algorithms is one of the most important issues in unmanned aerial vehicle (UAV) and robotics. Recently, UAV swarm or flying ad-hoc network which have much interest and extensive attentions from aviation industry, academia and research community, as it becomes one of the great tools for smart cities, rescue/disaster managements and military applications. UAV swarm is a scenario makes the UAVs interacted with each other. The control and communication structure in UAVs swarm require a specific decision to improve the trajectory planning and navigation operations of UAVs swarm. In addition, it requires high processing time and power with resources scarcity to efficiently operates the flights plan. Artificial intelligence (AI) is a powerful tool for optimization and accurate solutions for decision and power management issues. However, it comes with high data communication and processing. Leveraging AI with navigation and path planning it gives much adding values and great results for the system robustness. UAV industry moves toward the AI approaches in developing UAVs swarm and promising more intelligence UAV swarm interaction, according to the importance of this topic, this chapter will provide a systematic review on AI approaches and most algorithms those enable to developing the navigation and trajectory planning strategies for UAV swarm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
