Outputs (26)

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.

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. (online). T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/TITS.2025.3557783

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.

Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions (2025)
Presentation / Conference Contribution
Pan, H., Wang, H., Arvin, F., & Hu, J. (2025, July). Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions. Presented at 2025 IFAC Symposium on Robotics, Paris, France

This paper addresses the shape formation problem for large-scale robotic swarms by proposing an optimization-based cooperative navigation method. First, the physical space is partitioned into multiple disjoint bins, and the stochastic evolution of ro... Read More about Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions.

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.

Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics (2024)
Presentation / Conference Contribution
Watson, M., Ren, H., Arvin, F., & Hu, J. (2024, August). Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics. Presented at 2024 Annual Conference Towards Autonomous Robotic Systems (TAROS), London

Coverage Path Planning (CPP) is an effective approach to let intelligent robots cover an area by finding feasible paths through the environment. In this paper, we focus on using reinforcement learning to learn about a given environment and find the m... Read More about Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics.

Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning (2024)
Presentation / Conference Contribution
Champagnie, K., Chen, B., Arvin, F., & Hu, J. (2024, August). Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning. Presented at 2024 IEEE International Conference on Automation Science and Engineering (CASE), Bari, Italy

Two promising approaches to coverage path planning are reward-based and pheromone-based methods. Reward-based methods allow heuristics to be learned automatically, often yielding a superior performance to hand-crafted rules. On the other hand, pherom... Read More about Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning.

A Multi-Agent Path Planning Strategy with Reconfigurable Topology in Unknown Environments (2024)
Presentation / Conference Contribution
Sun, H., Hu, J., Dai, L., & Chen, B. (2024, August). A Multi-Agent Path Planning Strategy with Reconfigurable Topology in Unknown Environments. Presented at 2024 IEEE International Conference on Automation Science and Engineering (CASE), Bari, Italy

Safety-guaranteed trajectories are important for multi-agent systems to work in an unknown constrained environment. To address this issue, this paper proposes a cooperative path planning strategy for a swarm of agents such that they can achieve a tar... Read More about A Multi-Agent Path Planning Strategy with Reconfigurable Topology in Unknown Environments.

RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems (2024)
Presentation / Conference Contribution
Agachi, C., Arvin, F., & Hu, J. (2024, August). RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems. Presented at 2024 IEEE International Conference on Intelligent Systems (IS), Varna, Bulgaria

Coordination of multi-agent systems has received significant attention during the past few years owing to its wide real-world applications, such as cooperative exploration, aircraft formation, and autonomous vehicle platooning. To address this issue,... Read More about RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems.

Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization (2024)
Presentation / Conference Contribution
Champagnie, K., Arvin, F., & Hu, J. (2024, March). Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization. Presented at 2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, UK

In this paper, we present GEM, a novel approach to online coverage path planning in which a swarm of homogeneous agents act to maximize the entropy of pheromone deposited within their environment. We show that entropy maximization (EM) coincides with... Read More about Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization.