Improving Situation Awareness and Self-Adaptation in Autonomous Wheelchair-Drone Systems through Floor Surface Anomaly Detection

Autonomous wheelchair-drone systems represent a promising advancement in assistive mobility, enabling enhanced navigation in complex and dynamic environments. However, floor surface anomalies—such as uneven terrain, obstacles, and hazardous floor conditions—pose significant challenges to safe and efficient operation. This paper presents a novel approach improving situation awareness and self-adaptation by integrating floor surface-anomaly detection in autonomous wheelchair-drone systems. A specific architecture for situation-awareness is proposed, combining machine learning-based anomaly detection with adaptive motion planning, to enhance the system's resilience and responsiveness. Experimental results in simulated scenarios using Yolo-based architecture on real-world datasets demonstrate improved anomaly detection performances compared to the state-of-the-art, reducing the risk of instability and improving user safety. Experiments show a mAP50 of 0.764 and a F1 of 0.742 using a YoloV11s architecture.The research presented in this paper has been developed within the European project named REXASI-PRO, which aims to develop trustworthy AI solutions to assist individuals with reduced mobility.