Machine Learning and Intelligent Optimization Approaches for Robotic Manipulator Kinematic Modeling and Motion Planning: A Comprehensive Systematic Review

Kinematic modeling and path planning are critical problems in intelligent automation that directly affect the precision, adaptability, and safety of robotic arms. The ever-growing complexity of manipulators, including redundant DOFs, unique kinematic architectures, and environmental dynamics, has highlighted the drawbacks of classic analytical and Jacobian approaches in handling singularities, computational costs, and generalizability. Despite many reviews addressing particular problems such as inverse kinematics or reinforcement learning, an extensive overview of methodological development is yet to be provided. The sources for the literature review were mainly obtained from Google Scholar, while the publishing details for the selected articles were then cross-checked using the individual publishers’ and journal sites. The sources are divided into the following nine categories: analytical and hybrid inverse kinematics, neural networks, optimization, deep reinforcement learning, sampling- and learning-based motion planning, diffusion models, trajectory optimization, obstacle avoidance, and imitation learning. From the review, it is evident that there is a shift from traditional approaches such as analysis and optimization to AI-based methods like neural networks, deep reinforcement learning, and diffusion-based systems, with 105 studies published between 2022 and 2026. Some of the findings include MLP-based inverse kinematics (R2 > 0.99), meta-heuristic optimization resulting in a 4.75 × 10−4 cm position error, a 99.4% path-planning success rate through reinforcement learning, diffusion-based visuomotor control, and challenges facing intelligent robotic manipulation research.