Explainable Artificial intelligence for Autonomous UAV Navigation

Abstract

Unmanned Aerial Vehicles (UAVs) with limited computational, perception and power resources face significant challenges when navigating autonomously in unfamiliar environments. While artificial intelligence (AI)-assisted algorithms have been used to address these limitations, transparency of the underlying AI models remains a concern, hindering user trust. To address this limitation, this research study proposes a novel, explainable AI-based navigation approach for UAVs to navigate them through unknown environments autonomously. The soft actor-critic (SAC) algorithm and multilayer perceptron (MLP) policies integrated deep reinforcement learning algorithm is developed to derive control actions. This controller is integrated with a novel moving-window gradient-based explainable artificial intelligence (XAI) framework to shed light on the UAV’s decision-making process. The proposed XAI algorithm provides granular insights into how various factors, such as image segments and UAV state features, influence the UAV’s actions. It lays the groundwork for a novel visual explanation approach that segments input depth images to highlight critical navigational cues, augmented by a dynamic color map for precise obstacle identification. Additionally, the study introduces comprehensive textual explanations to provide an in-depth understanding of the UAV’s decision processes, thereby improving the model’s transparency and explainability. The simulation results indicate that the proposed DRL model achieves over 95% success rate. Moreover, evaluations conducted in two distinct environments demonstrate the model’s capability to generate effective and reliable explanations.