In this paper, we investigate autonomous intralogistics systems in the context of anomaly management, emphasizing that true autonomy must inherently include the capability for independent decision-making, even when faced with various anomalies. Growing interest in autonomous intralogistics systems, particularly mobile robot-based solutions known for their flexibility, requires effective management of abnormal behaviors. However, in the existing literature, a comprehensive approach that addresses all aspects of anomaly management for autonomous systems is rarely discussed, highlighting the need for a thorough understanding of the mechanisms necessary for the seamless operation of autonomous systems in the context of intralogistics. In this work, we (i) define a comprehensive framework for anomaly management in intralogistics that incorporates proactive strategies aimed at anticipating and preventing anomalies, enabling early detection and isolation, supporting adaptive response and recovery, and fostering continuous learning; (ii) present the definitions of anomaly and emergency, as well as anomaly handling and anomaly management; (iii) provide a comparison of our framework with existing solutions in the literature, highlighting their advantages and limitations; and (iv) identify essential future research directions, thereby outlining relevant topics for exploration and study. These contributions aim to pave the way for more resilient and adaptive autonomous systems in intralogistics.
This research introduces a novel approach for deterring woodpeckers to safeguard wooden structures in an environmentally-friendly way. Woodpecker drilling and drumming poses a significant threat to wooden structures, causing extensive physical and financial damage. These issues range from property damage to noise, underscoring the urgent need for an effective deterrent method. Our study proposes a laser-based deterrent mechanism, designed to be eco-friendly by minimizing effects on other wildlife and humans. This environmentally conscious approach offers a humane solution to woodpecker control, ensuring compliance with the Migratory Bird Treaty Act (MBTA). Additionally, our approach utilizes deep learning technology to chase away specific targets. Our system integrates YOLOv8 for accurate and low-power identification, creating an efficient operating solution with independent internet connectivity. This enhancement enables the system to adapt across various environments. Testing in a controlled environment has verified the system's effectiveness in deterring woodpeckers in an eco-friendly way. This approach ensures minimal impact on other wildlife and humans, adhering to legal and environmental constraints.
