Papers
10
Total Citations
150
H-Index
5
About
Josias G. Batista is a robotics researcher whose work focuses on the critical intersection of trajectory planning, system identification, and advanced control for industrial robotic manipulators. His primary contributions lie in developing intelligent, collision-free path planning algorithms and high-performance control strategies that address real-world production limitations, such as robot stops and accidents. Batista’s most influential work, “Trajectory Planning Using Artificial Potential Fields with Metaheuristics” (42 citations), pioneers the fusion of classical potential field methods with metaheuristic optimization to generate efficient, safe robot motions. He has also made significant strides in system identification, notably with his “Identification by Recursive Least Squares With Kalman Filter (RLS-KF) Applied to a Robotic Manipulator” (41 citations), which enhances model accuracy for improved control. His comparative studies of PID and LQR controllers (21 citations) and novel PSO-tuned PID designs (20 citations) provide practical benchmarks for the robotics community. Batista’s research extends to topological path planning for collision avoidance and reinforcement learning-based navigation, demonstrating a comprehensive approach to autonomous manipulation. With over 150 total citations, his work is increasingly recognized as foundational for developing more efficient, safer industrial robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Trajectory Planning Using Artificial Potential Fields with Metaheuristics42 citations · 2020
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- 4PID controller with novel PSO applied to a joint of a robotic manipulator20 citations · 2021
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- 8Path Planning Collision Avoidance using Reinforcement Learning3 citations · 2020
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