Outputs (61)

On the movement of the honeybee queen in the hive (2025)
Journal Article
Blaha, J., Stefanec, M., Janota, J., Hofstadler, D. N., Rouček, T., Ulrich, J., Fedotoff, L. A., Broughton, G., Vintr, T., Arvin, F., Schmickl, T., & Krajník, T. (2025). On the movement of the honeybee queen in the hive. Scientific Reports, 15, Article 20708. https://doi.org/10.1038/s41598-025-07093-4

A honeybee colony is a complex and dynamic system that emerges out of the interactions of thousands of individuals within a seemingly chaotic and heterogeneous environment. At the figurative core of this system is the honeybee queen, responsible for... Read More about On the movement of the honeybee queen in the hive.

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.

Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation (2025)
Journal Article
Alsayed, A., Bana, F., Arvin, F., Quinn, M. K., & Nabawy, M. R. A. (2025). Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation. Aerospace, 12(3), Article 189. https://doi.org/10.3390/aerospace12030189

This study examines the application of low-cost 1D LiDAR sensors in drone-based stockpile volume estimation, with a focus on indoor environments. Three approaches were experimentally investigated: (i) a multi-drone system equipped with static, downwa... Read More about Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation.

Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots (2025)
Presentation / Conference Contribution
Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (2025, May). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots c... Read More about Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots.

A Leader-Follower Collective Motion in Robotic Swarms (2024)
Presentation / Conference Contribution
Bahaidarah, M., Marjanovic, O., Rekabi-bana, F., & Arvin, F. (2024, August). A Leader-Follower Collective Motion in Robotic Swarms. Presented at TAROS 2024: Towards Autonomous Robotic Systems, London, UK

Collective Motion (CM) is a basic phenomenon observed in nature, such as in birds, insects, and schooling fish. In swarm robotics, virtual links among the swarm members generate attractive and repulsive forces to attain self-organised CM behaviour. H... Read More about A Leader-Follower Collective Motion in Robotic Swarms.

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.

Robust least squares twin bounded support vector machine with a generalized correntropy-induced metric (2024)
Journal Article
Yuan, C., Zhou, C., Pan, H., Arvin, F., Peng, J., & Li, H. (2025). Robust least squares twin bounded support vector machine with a generalized correntropy-induced metric. Information Sciences, 699, Article 121798. https://doi.org/10.1016/j.ins.2024.121798

The least squares twin support vector machine (LSTSVM), which aims to seek nonparallel hyperplanes by solving two linear equations, has received extensive attention in statistical theory as a powerful and widely used method for addressing classificat... Read More about Robust least squares twin bounded support vector machine with a generalized correntropy-induced metric.