Abstract:
This study investigates a multivariate sensor dataset collected from mobile robotic platforms to develop an AI-powered framework for autonomous systems. The dataset includes timestamped measurements of temperature, humidity, light intensity, and GPS coordinates, allowing for a multifaceted analysis centered on six primary objectives. First, mobile robot tracking is achieved through geospatial mapping, enabling precise monitoring of movement patterns across various terrains. Second, sensor fusion integrates multiple environmental inputs, enhancing the robot’s contextual awareness and decision-making accuracy. Third, environmental monitoring uses time-series analysis to detect trends, fluctuations, and anomalies in ambient conditions, providing insights into the operational environment over time. Fourth, spatiotemporal analytics connect environmental changes with specific times and locations, offering a dynamic view of how conditions evolve across both spatial and temporal dimensions. Fifth, cluster analysis is applied to identify hidden patterns and group similar environmental states or sensor behaviors. This unsupervised learning method improves interpretability and supports data-driven decisions by revealing structure in high-dimensional sensor data. Finally, predictive maintenance is facilitated by statistical anomaly detection, which flags deviations in sensor behavior that may signal underlying mechanical or system faults. This proactive approach enhances system reliability and reduces downtime. Collectively, these analytical techniques showcase the transformative potential of artificial intelligence and statistical learning in converting raw sensor data into actionable intelligence. The proposed framework enables intelligent navigation, environmental adaptability, and self-diagnostic capabilities, marking a significant step forward in the evolution of cognitive robotics. By leveraging advanced analytics, this research supports the development of autonomous systems that are not only reactive but also anticipatory and context-aware key traits for next-generation robotic platforms operating in complex, real-world dynamic environments.
Biography:
Prof. Monalisha Pattnaik is a Professor and Head of the Department of Statistics at Sambalpur University, Odisha, India. An expert in Artificial Intelligence, Supply Chain Analytics and Operations Research, she has made notable contributions to research and academia. Her interests span Robotics, AI, Advanced OR, Financial Time Series Analysis, Supply Chain Management, Optimization, and more. She has authored over 135 research papers, with more than 60 indexed in SCOPUS and Web of Science, and published 10 books, edited 5 international volumes, and contributed chapters to CRC Press, Apple Academic Press, and Springer. Prof. Pattnaik has been granted 3 patents by the UK and Indian Intellectual Property Offices. Her excellence has been recognized through several awards, including the Jyesta Acharya Award (Hyderabad, 2021–2022), Distinguished Woman Researcher in Operations Research (Chennai, 2024), Rising Women of India Award & Leading Educationist of India Award (New Delhi, 2024), Best Paper Award at RAORBA-2024 (Kolkata). Most recently, she received the Women Researcher Award at the 2nd International Award Ceremony for Women Researchers (VDGOOD, Pondicherry, Sept 2025). Prof. Pattnaik continues to inspire through her dedication to research, innovation, and academic excellence.