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.