All Outputs (4)

T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles (2025)
Journal Article
Pan, H., Zahmatkesh, M., Rekabi-Bana, F., Arvin, F., & Hu, J. (in press). T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles. IEEE Transactions on Intelligent Transportation Systems,

This paper introduces a time-optimal swarm tra-jectory planner for cooperative unmanned aerial vehicle (UAV) systems, designed to generate collision-free trajectories for flocking control in cluttered environments. To achieve this goal, model predict... Read More about T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles.

Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control (2025)
Presentation / Conference Contribution
Chen, D., Hu, J., Zhang, H., & Chen, B. (2025, June). Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control. Presented at 2025 European Control Conference (ECC), Thessaloniki, Greece

Traffic signal control is essential for managing urban traffic, reducing congestion, and minimizing environmental impact by optimizing both vehicular and pedestrian flow. This paper investigates the application of Reinforcement Learning (RL) in traff... Read More about Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control.

A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments (2025)
Presentation / Conference Contribution
Zhang, K., Zahmatkesh, M., Stefanec, M., Arvin, F., & Hu, J. (2025, March). A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments. Presented at 2025 IEEE International Conference on Industrial Technology, China

This paper proposes a real-time multi-robot navigation method that integrates the Rapidly-exploring Random Tree (RRT) algorithm with the improved Artificial Potential Field (APF) approach. Since traditional path planning methods often face problems s... Read More about A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments.

Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles (2025)
Presentation / Conference Contribution
Aihaiti, A., Arvin, F., & Hu, J. (2025, March). Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles. Presented at 2025 IEEE International Conference on Industrial Technology, Wuhan, China

The safe overtaking of autonomous vehicles has become an important focus in recent robotics and AI research. Considering the scenario of overtaking with oncoming vehicles, this paper proposes a hierarchical framework based on deep reinforcement learn... Read More about Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles.