Machine Learning Paradigms for UAV Path Planning: Review and Challenges

Abstract

Path planning is a crucial step in robotic navigation to satisfy: tasks safety, efficiency requirements and adapt to the complexity of environments. Path planning problem is particularly critical for Unmanned Aerial Vehicles (UAV), being increasingly involved within important tasks in diverse military and civil fields such as: inspection, search and rescue and communication, taking advantage of their high flexibility, maneuverability and cost-effective solutions. This continuous growth made the solution of UAV path planning problem an interesting research topic in recent years. In this scope, machine learning algorithms were a promising tool due to their continuous data-driven selfimprovement to adapt with the high dynamicity of environments where conventional programming fails. This paper provides a review on recent developments in machine learning-based UAV path planning issued from credible databases like: IEEE, Elsevier, Springer Links and MDPI. The main contribution of this paper is to delve through these recent works providing a taxonomy of algorithms into the fundamental paradigms: supervised, unsupervised and reinforcement, evaluating their efficiency and limitations under distinct scenarios. Despite the relative generalization of deep reinforcement learning to different environments, this study highlighted some active challenges about computational cost and real-time applications that remain open.