International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
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A Survey of Autonomous Robotic Approaches with Focus on LoRaEnabled Q-Learning Navigation Mahima M Hebbar1, Neha N2, K U Dikshitha3, Kiran K N4 1Department of Electronics and Communication Engineering, BNMIT, Bengaluru, India
2Department of Electronics and Communication Engineering, BNMIT, Bengaluru, India 3Department of Electronics and Communication Engineering, BNMIT, Bengaluru, India
4Assitant Professor,Department of Electronics and Communication Engineering, BNMIT, Bengaluru, India
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Abstract - Oil spills pose significant ecological and
support low-power, long-distance communication has recently sparked interest in robotics. In maritime or offshore spill scenarios, where conventional systems like Wi-Fi or Bluetooth are unreliable, LoRa enables continuous connectivity between robots and monitoring stations. Its scalability and resilience make it particularly well-suited for coordinating fleets of autonomous surface or underwater robots across large spill zones.
economic threats, necessitating rapid and efficient detection and containment strategies. Advances in autonomous marine robotics, deep learning, and low-power communication have enabled intelligent and adaptive monitoring systems. Deep learning models, such as DeepLabv3+ with synthetic aperture radar (SAR) imagery and U-Net CNNs on unmanned aerial vehicles (UAVs), provide high-accuracy detection, while reinforcement learning techniques like Q-Learning enable adaptive navigation in dynamic marine environments. Communication protocols such as LoRa offer long-range, lowpower connectivity, though bandwidth limitations restrict high-resolution data transmission. Despite these advances, fully integrated systems addressing scalability, multi-robot coordination, and environmental robustness remain limited. This survey highlights recent developments, identifies key research gaps, and proposes a LoRa-enabled autonomous robot with Q-Learning-based navigation as a promising framework for real-time, energy-efficient, and scalable oil spill detection and containment.
Reinforcement learning (RL) has also transformed robotic decision-making. Q-Learning, a model-free RL algorithm, is particularly suited to marine environments with uncertain conditions such as fluctuating currents, floating debris, or changing spill boundaries. Without requiring predefined models of the operating space, Q-Learning enables robots to adapt dynamically and make effective navigation decisions by learning policies through interaction with the environment. While autonomous navigation, LoRa-based communication, and reinforcement learning have each been widely studied, no survey has examined their combined potential for realtime environmental hazard response—particularly oil spill detection and containment—where adaptive navigation and dependable communication are essential.
Key Words: Oil spill detection, autonomous marine robotics, LoRa communication, Environmental Monitoring, Q-learning ,Deep Learning, Reinforcement Learning
This survey addresses that gap by reviewing autonomous robotic approaches, emphasizing the integration of LoRa into robotic systems, and analyzing Q-Learning-based navigation in the context of environmental monitoring. In doing so, it highlights current constraints, open challenges, and prospective avenues for future research on LoRa-enabled robotic frameworks for maritime applications.
1.INTRODUCTION Autonomous robotic navigation has become an important field of research due to applications in smart cities, healthcare, disaster management, industrial automation, and environmental monitoring. Among these, oil spill containment and marine environmental monitoring have gained urgency because of their substantial ecological and economic impacts.
The rest of this paper is organized as follows: Section 2 presents a detailed literature survey covering autonomous navigation methods, LoRa-enabled robotic communication, and Q-Learning approaches with emphasis on marine monitoring and oil spill scenarios. Section 3 concludes with key insights and directions for future research.
Autonomous robots with reliable, long-term operation aim to play a critical role in detecting spills, monitoring affected areas, and implementing containment strategies in dynamic ocean environments. These applications demand navigation approaches that balance adaptability, wide-area coverage, and energy efficiency.
2. RELATED WORK This section reviews research across five major domains related to autonomous oil spill detection and containment:
Long Range (LoRa) technology was developed primarily for low-power wide-area networks (LPWANs), but its ability to
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