Maykoll Vanegas
Papers
1
Total Citations
10
H-Index
1
About
Dr. Maykoll Vanegas is a leading researcher at the intersection of autonomous navigation, sensor fusion, and deep reinforcement learning. His work focuses on enabling robust, real-time decision-making for wheeled robots in dynamic environments. Vanegas’s most impactful contribution, "Integrating Radar-Based Obstacle Detection with Deep Reinforcement Learning for Robust Autonomous Navigation" (2024, 10 citations), pioneers a novel framework that combines radar-based dynamic obstacle detection with a Bidirectional Gated Recurrent Unit (BiGRU) deep reinforcement learning architecture. By employing filtering and tracking algorithms to cluster radar object points, his system significantly enhances a robot’s ability to navigate safely through unpredictable surroundings—a critical advance for applications in logistics, search-and-rescue, and autonomous driving. This integration of low-cost, weather-resilient radar sensors with advanced DRL methods addresses a key limitation of vision-based systems. Vanegas’s work is notable for its practical, hardware-aware approach, bridging the gap between theoretical reinforcement learning and real-world robotic deployment. His research continues to shape the future of intelligent, autonomous mobility.
Research Focus
Key Achievements
Top Papers
- 1