APPLICATION OF REINFORCEMENT LEARNING IN ROBOTIC NAVIGATION AND CONTROL SYSTEMS
APPLICATION OF REINFORCEMENT LEARNING IN ROBOTIC NAVIGATION AND CONTROL SYSTEMS
Authors:
YASHVARDHAN RATHORE
Modern Education Society's D. G. Ruparel College of Arts, Science and Commerce
Matunga West, Mumbai
Abstract:
Reinforcement Learning (RL) has emerged as a transformative approach within the field of artificial intelligence, enabling autonomous agents to learn optimal decision-making strategies through interaction with dynamic environments. In the domain of robotics, RL has gained significant attention for its ability to address complex challenges in navigation and control systems, where traditional rule-based and model-driven methods often fall short due to uncertainty, non-linearity, and real-time adaptability requirements.
This research paper explores the application of reinforcement learning techniques in robotic navigation and control, focusing on both classical methods such as Q-learning and advanced approaches including Deep Reinforcement Learning (DRL) and actor-critic architectures. The study examines how RL enables robots to perform tasks such as path planning, obstacle avoidance, motion control, and environment interaction without explicit programming.
Furthermore, this paper presents a comparative analysis between RL-based approaches and conventional control techniques, highlighting improvements in adaptability, efficiency, and robustness. The integration of sensor data—including LiDAR, cameras, and GPS—within RL frameworks is also discussed, demonstrating enhanced perception and decision-making capabilities in complex and unstructured environments.
The research also addresses key challenges associated with RL implementation in robotics, such as sample inefficiency, safety concerns during training, high computational requirements, and the sim-to-real transfer problem. Methodologies involving simulation-based training, performance evaluation metrics, and experimental validation are outlined to provide a comprehensive understanding of the practical applicability of RL in robotic systems.
The findings suggest that reinforcement learning significantly improves the performance of robotic navigation and control systems, particularly in dynamic and uncertain environments. The paper concludes by identifying future research directions, including hybrid control models, safer learning algorithms, and improved generalization techniques, positioning RL as a critical component in the advancement of intelligent and autonomous robotic systems.