Kaio Martins Ramos

Papers

Total Citations

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About

Kaio Martins Ramos is a researcher specializing in industrial robotics, control systems, and intelligent trajectory planning. His work focuses on overcoming key limitations in automated production, particularly robot stoppages caused by inefficient path generation or collisions. Ramos’s most cited paper, “Trajectory Planning Using Artificial Potential Fields with Metaheuristics” (2020, 42 citations), introduces a hybrid approach that combines artificial potential fields with metaheuristic optimization to generate collision-free, efficient robot trajectories—directly addressing downtime in manufacturing. He further contributes to control theory with “Performance Comparison Between the PID and LQR Controllers Applied to a Robotic Manipulator Joint” (2019, 21 citations), offering a practical benchmark for selecting optimal controllers in robotic joints. More recently, Ramos explores machine learning in “Path Planning Collision Avoidance using Reinforcement Learning” (2020), advancing adaptive, real-time path generation. His cumulative work bridges classical control, optimization, and AI, providing scalable solutions for modern industrial automation. With over 66 citations across his top papers, Ramos’s research is a valuable resource for students and engineers seeking to enhance robot efficiency, safety, and autonomy in production environments.