IR-QLA: Machine Learning-Based Q-Learning Algorithm Optimization for UAVs Faster Trajectory Planning by Instructed- Reinforcement Learning

Abstract

Trajectory planning for unmanned aerial vehicles (UAVs) presents a formidable challenge, particularly in environments where details about the terrain and objectives are not known beforehand. This uncertainty has propelled the adoption of UAVs across diverse sectors such as logistics, weather forecasting, security surveillance, and autonomous driving, where their ability to navigate autonomously is crucial. Recent advancements have leveraged deep learning for robust object detection and tracking, significantly enhancing data acquisition from aerial imagery and UAV-based applications. Despite numerous innovations, the critical issue of optimizing UAV trajectories in unknown environments remains largely unaddressed. This paper introduces a novel approach, the Instructed Reinforcement Q-Learning Algorithm (IR-QLA), which utilizes Received Signal Strength (RSS) as a dynamic reward metric to refine trajectory planning continuously. By integrating a unique instructed reinforcement principle, this method significantly accelerates the learning process, improving the UAVs’ navigational efficiencies. Our findings demonstrate that IR-QLA not only surpasses traditional algorithms like Ant Colony and Particle Swarm Optimization in terms of convergence speed but also minimizes the path length and iteration counts needed for trajectory optimization. Future studies will explore alternative reward sources and advanced algorithms to further enhance the precision and speed of UAV trajectory planning, aiming to establish new benchmarks in the field.

Keywords

• Machine learning
• reinforcement learning
• trajectory planning
• Q-learning