Arvid Enliden
Papers
1
Total Citations
7
H-Index
1
About
Arvid Enliden is at the forefront of intelligent robotic navigation, specializing in the intersection of deep reinforcement learning and model predictive control. His most-cited work, "Collision-Free Trajectory Planning of Mobile Robots by Integrating Deep Reinforcement Learning and Model Predictive Control" (2023), introduces a groundbreaking framework that marries the adaptive decision-making of reinforcement learning with the precision of model predictive control. This hybrid approach enables mobile robots to achieve real-time, collision-free navigation while maintaining remarkable computational efficiency—a critical advancement for autonomous systems operating in dynamic environments. By training a preliminary policy that balances safety and speed, Enliden’s methodology addresses a long-standing challenge in robotics: how to ensure robust obstacle avoidance without sacrificing performance. Though early in his career, his work has already garnered 7 citations, signaling growing recognition from the robotics community. His contributions hold promise for applications ranging from warehouse automation to autonomous vehicles, positioning him as an emerging voice in the field of motion planning and control.
Research Focus
Key Achievements
Top Papers
- 1