Max Robotics — Global Service Robot Directory + US Certification

About

Raja Jarray is a rising researcher in autonomous robotics, specializing in path planning for unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) in complex, three-dimensional environments. His work systematically evaluates and advances artificial intelligence and reinforcement learning techniques to solve critical navigation challenges. Jarray’s most cited paper, “Comparative Study of Q-Learning and SARSA Algorithms for UAV Path Planning in 3D Environments” (2024, 7 citations), provides a foundational comparison of model-free reinforcement learning methods, establishing benchmarks for obstacle-dense aerial navigation. He further contributes to the field with “Comparison of A* and D* Algorithms for 3D Path Planning of Unmanned Aerial Vehicles” (2023, 6 citations), analyzing classical heuristic search algorithms for real-time trajectory optimization. His research extends to underwater robotics, as seen in his performance comparison of rapidly-exploring random tree algorithms for AUVs in complex environments (2024). Most recently, Jarray introduced a dynamic reward-based deep reinforcement learning algorithm (2025) that overcomes the limitations of traditional Q-learning in large-scale UAV missions, demonstrating his ability to integrate deep learning for scalable, adaptive navigation. With a growing citation record and a focus on comparative algorithm analysis, Jarray is establishing himself as a key contributor to intelligent autonomous systems.