Skip to main content

Research Repository

Advanced Search

All Outputs (24)

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.

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.

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.

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.

Distributed Collision-Free Bearing Coordination of Multi-UAV Systems With Actuator Faults and Time Delays (2024)
Journal Article
Wu, K., Hu, J., Li, Z., Ding, Z., & Arvin, F. (2024). Distributed Collision-Free Bearing Coordination of Multi-UAV Systems With Actuator Faults and Time Delays. IEEE Transactions on Intelligent Transportation Systems, 25(9), 11768 - 11781. https://doi.org/10.1109/tits.2024.3364356

Coordination of unmanned aerial vehicle (UAV) systems has received great attention from robotics and control communities. In this paper, we investigate the distributed formation tracking problem in heterogeneous nonlinear multi-UAV networks via beari... Read More about Distributed Collision-Free Bearing Coordination of Multi-UAV Systems With Actuator Faults and Time Delays.

Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems (2023)
Presentation / Conference Contribution
Wu, K., Hu, J., Ding, Z., & Arvin, F. (2023, July). Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems

This paper addresses the bearing-only formation tracking problem for heterogeneous nonlinear multi-robot systems. In contrast to position and distance-based formation algorithms, the robots can only measure the bearing information from their neighbor... Read More about Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems.

Unified Robust Path Planning and Optimal Trajectory Generation for Efficient 3D Area Coverage of Quadrotor UAVs (2023)
Journal Article
Rekabi-Bana, F., Hu, J., Krajník, T., & Arvin, F. (2024). Unified Robust Path Planning and Optimal Trajectory Generation for Efficient 3D Area Coverage of Quadrotor UAVs. IEEE Transactions on Intelligent Transportation Systems, 25(3), 2492-2507. https://doi.org/10.1109/tits.2023.3320049

Area coverage is an important problem in robotics applications, which has been widely used in search and rescue, offshore industrial inspection, and smart agriculture. This paper demonstrates a novel unified robust path planning, optimal trajectory g... Read More about Unified Robust Path Planning and Optimal Trajectory Generation for Efficient 3D Area Coverage of Quadrotor UAVs.

Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements (2023)
Journal Article
Wu, K., Hu, J., Ding, Z., & Arvin, F. (2024). Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements. IEEE Transactions on Automation Science and Engineering, 21(2), 1346-1357. https://doi.org/10.1109/tase.2023.3239748

This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of... Read More about Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements.

Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements (2022)
Presentation / Conference Contribution
Wu, K., Hu, J., Lennox, B., & Arvin, F. (2022, July). Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements. Presented at 2022 European Control Conference (ECC), London, United Kingdom

Inspired by natural swarm collective behaviors such as colonies of bees and schools of fish, coordination strategies in swarm robotics have received significant attention in recent years. In this paper, a mixed formation control design based on edge... Read More about Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements.

Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field (2022)
Journal Article
Xie, S., Hu, J., Bhowmick, P., Ding, Z., & Arvin, F. (2022). Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field. IEEE Transactions on Intelligent Transportation Systems, 23(11), 21531- 21547. https://doi.org/10.1109/tits.2022.3189741

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper propo... Read More about Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field.

Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring (2022)
Journal Article
Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2022). Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring. Aerospace Science and Technology, 123, Article 107494. https://doi.org/10.1016/j.ast.2022.107494

Coordination of unmanned aerial vehicle (UAV) swarms has received significant attention due to its wide practical applications including search and rescue, cooperative exploration and target surveillance. Motivated by the flexibility of the UAVs and... Read More about Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring.

Robust formation control for networked robotic systems using Negative Imaginary dynamics (2022)
Journal Article
Hu, J., Lennox, B., & Arvin, F. (2022). Robust formation control for networked robotic systems using Negative Imaginary dynamics. Automatica, 140, Article 110235. https://doi.org/10.1016/j.automatica.2022.110235

This paper proposes a consensus-based formation tracking scheme for multi-robot systems utilizing the Negative Imaginary (NI) theory. The proposed scheme applies to a class of networked robotic systems that can be modelled as a group of single integr... Read More about Robust formation control for networked robotic systems using Negative Imaginary dynamics.

Omnipotent Virtual Giant for Remote Human–Swarm Interaction (2021)
Presentation / Conference Contribution
Jang, I., Hu, J., Arvin, F., Carrasco, J., & Lennox, B. (2021, August). Omnipotent Virtual Giant for Remote Human–Swarm Interaction. Presented at 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Vancouver, BC, Canada

This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environments as if we were omnipotent relative to the ants. In virtual reality... Read More about Omnipotent Virtual Giant for Remote Human–Swarm Interaction.

Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms (2021)
Journal Article
Ban, Z., Hu, J., Lennox, B., & Arvin, F. (2021). Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms. Mobile Networks and Applications, 26(6), 2461–2471. https://doi.org/10.1007/s11036-021-01785-7

Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocki... Read More about Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms.

A Decentralized Cluster Formation Containment Framework for Multirobot Systems (2021)
Journal Article
Hu, J., Bhowmick, P., Jang, I., Arvin, F., & Lanzon, A. (2021). A Decentralized Cluster Formation Containment Framework for Multirobot Systems. IEEE Transactions on Robotics, 37(6), 1936-1955. https://doi.org/10.1109/tro.2021.3071615

Cooperative control of multirobot systems (MRSs) has earned significant research interest over the past two decades due to its potential applications in multidisciplinary engineering problems. In contrast to a single specialized robot, the MRS can be... Read More about A Decentralized Cluster Formation Containment Framework for Multirobot Systems.