AI-Based Adaptive Motion Planning for Autonomous Robotic Systems

Abstract

Autonomous robotic systems are transforming industries such as automation, healthcare, transportation, defense, and intelligent services. A major challenge in robotics is motion planning in dynamic and uncertain environments, where robots must navigate safely and efficiently. Traditional algorithms like Dijkstra’s, A*, Probabilistic Road Maps, and Rapidly Exploring Random Trees perform well in static environments but struggle with moving obstacles, sensor uncertainty, and real-time decision-making. Artificial Intelligence (AI) has improved robotic motion planning through adaptive learning, predictive decision-making, reinforcement learning, fuzzy logic, neural networks, and evolutionary optimization. These techniques enable robots to learn from their environment and optimize navigation over time. This paper reviews AI-based adaptive motion planning methods developed before 2019 and examines their role in path optimization, obstacle avoidance, localization, and decision-making. It also proposes a hybrid framework combining sensor fusion, environment mapping, fuzzy inference systems, and reinforcement learning for real-time path optimization. Simulation results demonstrate that AI-based adaptive planning achieves better navigation accuracy, obstacle avoidance, computational efficiency, and environmental adaptability compared to traditional methods.