Murillo Ferreira
Universidade Estadual Paulista (Unesp), Universidade de Sorocaba
Papers
2
Total Citations
56
H-Index
2
About
Murillo Ferreira is a researcher at the forefront of intelligent aerial robotics, specializing in the intersection of reinforcement learning, evolutionary algorithms, and autonomous drone control. His most impactful work, "Intelligent Control of a Quadrotor with Proximal Policy Optimization Reinforcement Learning" (2018, 52 citations), demonstrates how model-free reinforcement learning can stabilize inherently unstable quadrotor systems—a significant departure from classical control methods. This contribution has been widely recognized for advancing adaptive flight control in dynamic environments. Ferreira further extends his expertise in "An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges" (2020), where he applies multi-objective planning and machine learning to derive robust, optimized flight paths for complex aerial missions. His work bridges the gap between theoretical machine learning and practical robotics, offering scalable solutions for autonomous navigation. By combining reinforcement learning with evolutionary optimization, Ferreira is shaping the next generation of intelligent drones capable of operating in unpredictable real-world conditions, making his research essential for students and engineers in robotics, control systems, and AI.
Research Focus
Key Achievements
Top Papers
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- 2