Staff profile

Farshad Arvin is a Professor of Robotics in the Department of Computer Science at Durham University.

He received his BSc degree in Computer Engineering in 2004, an MSc degree in Computer Systems Engineering in 2010, and a PhD in Computer Science in 2015.

Background

Before joining Durham in 2022, Farshad was a Lecturer in Robotics (2018-2021) and a Senior Lecturer in Robotics (2021-2022) in the Department of Electrical & Electronic Engineering at The University of Manchester.

He joined the University of Manchester in 2015 as a Post-Doctoral Research Associate in the Robotics for Extreme Environments group at the Department of Electrical & Electronic Engineering. He was a Research Assistant at the Lincoln Centre for Autonomous Systems (L-CAS) at the University of Lincoln, UK (2012 to 2015). He was awarded a Marie Skłodowska-Curie fellowship for being involved in the FP7-EYE2E and LIVCODE EU projects during his PhD study.

Experience

Farshad's research interests include Biohybrid Robotics, Bio-inspired Swarm Robotics, and Autonomous Multi-agent Systems.

He visited several leading institutes, including Artificial Life Laboratory in Karl-Franzens University of Graz, Austria, in 2018; the Italian Institute of Technology (iit) in Genoa, Italy, in 2017; Institute of Rehabilitation and Medical Robotics in Huazhong University of Science and Technology (HUST), Wuhan, China, in 2014; and the Institute of Microelectronics at Tsinghua University in Beijing, China, in 2013 and 2012. 

Research Team and Projects

Farshad is the founding director of Swarm & Computation Intelligence Laboratory (SwaCIL), formed in 2018. The lab hosts 4 Post-Doctoral Research Associates, an Electronics technician and 6 PGR students. It has received more than £4M in direct funding from the EU, EPSRC, InnovateUK and industry. Farshad coordinates several research projects, including a large EU project H2020-FET RoboRoyale (2021-2026, €3.27M), PI in Horizon Europe Pathfiner Sensorbees (2024-2029, €3.2M), PI in Horizon Europe  BioDiMoBot (2025-2030, €8M) and H2020-FET Robocoenosis (2020-2025, €3M).

• Swarm Robotics
• Bio-inspired Swarms
• Multi-agent Systems
• Bio-hybrid Systems

Chapter in book

• Optimization of a Self-organized Collective Motion in a Robotic Swarm
  
  Bahaidarah, M., Bana, F. R., Turgut, A. E., Marjanovic, O., & Arvin, F. (in press). Optimization of a Self-organized Collective Motion in a Robotic Swarm. In Swarm Intelligence.
• A Novel Time-of-Flight Range and Bearing Sensor System for Micro Air Vehicle Swarms
  
  Bilaloğlu, C., Şahin, M., Arvin, F., Şahin, E., & Turgut, A. E. (in press). A Novel Time-of-Flight Range and Bearing Sensor System for Micro Air Vehicle Swarms. In Swarm Intelligence.

Conference Paper

• Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
  
  Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (in press). 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.
• Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions
  
  Pan, H., Wang, H., Arvin, F., & Hu, J. (in press). Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions. In Proceedings of the 2025 IFAC Symposium on Robotics.
• A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments
  
  Zhang, K., Zahmatkesh, M., Stefanec, M., Arvin, F., & Hu, J. (in press). A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments. Presented at 2025 IEEE International Conference on Industrial Technology, China.
• Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles
  
  Aihaiti, A., Arvin, F., & Hu, J. (2025). Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles. In 2025 IEEE International Conference on Industrial Technology (ICIT). IEEE. https://doi.org/10.1109/ICIT63637.2025.10965305
• Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics
  
  Watson, M., Ren, H., Arvin, F., & Hu, J. (2025). Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics. Lecture Notes in Computer Science, 15052 LNAI, 320-332. https://doi.org/10.1007/978-3-031-72062-8_28
• A Leader-Follower Collective Motion in Robotic Swarms
  
  Bahaidarah, M., Marjanovic, O., Rekabi-bana, F., & Arvin, F. (2024). A Leader-Follower Collective Motion in Robotic Swarms. In Towards Autonomous Robotic Systems: 25th Annual Conference, TAROS 2024, London, UK, August 21–23, 2024, Proceedings, Part II (pp. 281-293). Springer. https://doi.org/10.1007/978-3-031-72062-8_25
• Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning
  
  Champagnie, K., Chen, B., Arvin, F., & Hu, J. (2024). Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning. In 2024 IEEE International Conference on Automation Science and Engineering (CASE) (pp. 1000-1005). IEEE. https://doi.org/10.1109/CASE59546.2024.10711550
• RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems
  
  Agachi, C., Arvin, F., & Hu, J. (2024). RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems. In 2024 IEEE International Conference on Intelligent Systems (IS) (pp. 1-6). IEEE. https://doi.org/10.1109/IS61756.2024.10705259
• Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization
  
  Champagnie, K., Arvin, F., & Hu, J. (2024). Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization. In 2024 IEEE International Conference on Industrial Technology (ICIT). IEEE. https://doi.org/10.1109/ICIT58233.2024.10540869
• Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems
  
  Wu, K., Hu, J., Ding, Z., & Arvin, F. (2023). Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems. In H. Ishii, Y. Ebihara, J.- ichi Imura, & M. Yamakita (Eds.), 22nd IFAC World Congress (pp. 3447-3452). Elsevier. https://doi.org/10.1016/j.ifacol.2023.10.1496
• Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements
  
  Wu, K., Hu, J., Lennox, B., & Arvin, F. (2022). Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements. In 2022 European Control Conference (ECC). IEEE. https://doi.org/10.23919/ecc55457.2022.9838436
• Omnipotent Virtual Giant for Remote Human–Swarm Interaction
  
  Jang, I., Hu, J., Arvin, F., Carrasco, J., & Lennox, B. (2021). 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. https://doi.org/10.1109/ro-man50785.2021.9515542
• Biohybrid Entities for Environmental Monitoring
  
  Thenius, R., Rajewicz, W., Varughese, J. C., Schoenwetter-Fuchs, S., Arvin, F., Casson, A. J., Wu, C., Lennox, B., Campo, A., van Vuuren, G. J., Stefanini, C., Romano, D., & Schmickl, T. (2021). Biohybrid Entities for Environmental Monitoring. In Artificial Life Conference Proceedings (p. 33). https://doi.org/10.1162/isal_a_00366
• Φ Clust: Pheromone-Based Aggregation for Robotic Swarms
  
  Arvin, F., Turgut, A. E., Krajnik, T., Rahimi, S., Okay, I. E., Yue, S., Watson, S., & Lennox, B. (2018). Φ Clust: Pheromone-Based Aggregation for Robotic Swarms. Presented at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain. https://doi.org/10.1109/iros.2018.8593961
• COSΦ: Artificial pheromone system for robotic swarms research
  
  Arvin, F., Krajnik, T., Turgut, A. E., & Yue, S. (2015). COSΦ: Artificial pheromone system for robotic swarms research. Presented at 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2015.7353405

Journal Article

• Robust least squares twin bounded support vector machine with a generalized correntropy-induced metric
  
  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
• T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles
  
  Pan, H., Zahmatkesh, M., Rekabi-Bana, F., Arvin, F., & Hu, J. (2025). T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles. IEEE Transactions on Intelligent Transportation Systems. Advance online publication. https://doi.org/10.1109/TITS.2025.3557783
• Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation
  
  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
• Fast Collision-Free Multi-Vehicle Lane Change Motion Planning and Control Framework in Uncertain Environments
  
  Liu, T., Chai, R., Chai, S., Arvin, F., Zhang, J., & Lennox, B. (2024). Fast Collision-Free Multi-Vehicle Lane Change Motion Planning and Control Framework in Uncertain Environments. IEEE Transactions on Industrial Electronics, 71(12), 16602-16613. https://doi.org/10.1109/tie.2024.3398674
• Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots
  
  Ulrich, J., Stefanec, M., Rekabi-Bana, F., Fedotoff, L. A., Rouček, T., Gündeğer, B. Y., Saadat, M., Blaha, J., Janota, J., Hofstadler, D. N., Žampachů, K., Keyvan, E. E., Erdem, B., Şahin, E., Alemdar, H., Turgut, A. E., Arvin, F., Schmickl, T., & Krajník, T. (2024). Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots. Science Robotics, 9(95), Article eadn6848. https://doi.org/10.1126/scirobotics.adn6848
• Distributed Collision-Free Bearing Coordination of Multi-UAV Systems With Actuator Faults and Time Delays
  
  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
• Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments
  
  Rekabi Bana, F., Krajník, T., & Arvin, F. (2024). Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments. Frontiers in Robotics and AI, 11, Article 1375393. https://doi.org/10.3389/frobt.2024.1375393
• Reinforcement learning-based aggregation for robot swarms
  
  Sadeghi Amjadi, A., Bilaloğlu, C., Turgut, A. E., Na, S., Şahin, E., Krajník, T., & Arvin, F. (2024). Reinforcement learning-based aggregation for robot swarms. Adaptive Behavior, 32(3), 265-281. https://doi.org/10.1177/10597123231202593
• Swarm flocking using optimisation for a self-organised collective motion
  
  Bahaidarah, M., Rekabi-Bana, F., Marjanovic, O., & Arvin, F. (2024). Swarm flocking using optimisation for a self-organised collective motion. Swarm and Evolutionary Computation, 86, Article 101491. https://doi.org/10.1016/j.swevo.2024.101491
• Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements
  
  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
• Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment
  
  Chai, R., Niu, H., Carrasco, J., Arvin, F., Yin, H., & Lennox, B. (2024). Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 5778-5792. https://doi.org/10.1109/tnnls.2022.3209154
• Editorial: Swarm neuro-robots with the bio-inspired environmental perception.
  
  Hu, C., Arvin, F., Bellotto, N., Yue, S., & Li, H. (2024). Editorial: Swarm neuro-robots with the bio-inspired environmental perception. Frontiers in Neurorobotics, 18, Article 1386178. https://doi.org/10.3389/fnbot.2024.1386178
• Unified Robust Path Planning and Optimal Trajectory Generation for Efficient 3D Area Coverage of Quadrotor UAVs
  
  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
• Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring
  
  Rajewicz, W., Wu, C., Romano, D., Campo, A., Arvin, F., Casson, A. J., Jansen van Vuuren, G., Stefanini, C., Varughese, J. C., Lennox, B., Schönwetter-Fuchs, S., Schmickl, T., & Thenius, R. (2024). Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring. Bioinspiration & Biomimetics, 19(1), Article 015001. https://doi.org/10.1088/1748-3190/ad0c5d
• Federated Reinforcement Learning for Collective Navigation of Robotic Swarms
  
  Na, S., Roucek, T., Ulrich, J., Pikman, J., Krajnik, T., Lennox, B., & Arvin, F. (2023). Federated Reinforcement Learning for Collective Navigation of Robotic Swarms. IEEE Transactions on Cognitive and Developmental Systems, 15(4), 2122-2131. https://doi.org/10.1109/tcds.2023.3239815
• Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles using Spring Damping Energy Model
  
  Xie, S., Hu, J., Ding, Z., & Arvin, F. (2023). Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles using Spring Damping Energy Model. IEEE Transactions on Vehicular Technology, 72(3), 2974-2987. https://doi.org/10.1109/tvt.2022.3218575
• Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field
  
  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
• Robust formation control for networked robotic systems using Negative Imaginary dynamics
  
  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
• Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring
  
  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
• Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning
  
  Na, S., Niu, H., Lennox, B., & Arvin, F. (2022). Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 71(3), 2511-2526. https://doi.org/10.1109/tvt.2022.3145346
• A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees
  
  Stefanec, M., Hofstadler, D. N., Krajník, T., Turgut, A. E., Alemdar, H., Lennox, B., Şahin, E., Arvin, F., & Schmickl, T. (2022). A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees. Frontiers in Robotics and AI, 9, Article 791921. https://doi.org/10.3389/frobt.2022.791921
• Robust Formation Coordination of Robot Swarms With Nonlinear Dynamics and Unknown Disturbances: Design and Experiments
  
  Hu, J., Turgut, A. E., Lennox, B., & Arvin, F. (2022). Robust Formation Coordination of Robot Swarms With Nonlinear Dynamics and Unknown Disturbances: Design and Experiments. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(1), 114-118. https://doi.org/10.1109/tcsii.2021.3074705
• A Decentralized Cluster Formation Containment Framework for Multirobot Systems
  
  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
• Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms
  
  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
• Finite-Time Bearing-Only Formation Tracking of Heterogeneous Mobile Robots With Collision Avoidance
  
  Wu, K., Hu, J., Lennox, B., & Arvin, F. (2021). Finite-Time Bearing-Only Formation Tracking of Heterogeneous Mobile Robots With Collision Avoidance. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(10), 3316-3320. https://doi.org/10.1109/tcsii.2021.3066555
• Bio-inspired artificial pheromone system for swarm robotics applications
  
  Na, S., Qiu, Y., Turgut, A. E., Ulrich, J., Krajník, T., Yue, S., Lennox, B., & Arvin, F. (2021). Bio-inspired artificial pheromone system for swarm robotics applications. Adaptive Behavior, 29(4), 395-415. https://doi.org/10.1177/1059712320918936
• A 3-DOF piezoelectric driven nanopositioner: Design, control and experiment
  
  Li, P.-Z., Zhang, D.-F., Lennox, B., & Arvin, F. (2021). A 3-DOF piezoelectric driven nanopositioner: Design, control and experiment. Mechanical Systems and Signal Processing, 155, Article 107603. https://doi.org/10.1016/j.ymssp.2020.107603
• Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends
  
  Schranz, M., Di Caro, G. A., Schmickl, T., Elmenreich, W., Arvin, F., Şekercioğlu, A., & Sende, M. (2021). Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends. Swarm and Evolutionary Computation, 60, Article 100762. https://doi.org/10.1016/j.swevo.2020.100762
• Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation
  
  Alsayed, A., Yunusa-Kaltungo, A., Quinn, M. K., Arvin, F., & Nabawy, M. R. (2021). Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation. Remote Sensing, 13(17), Article 3356. https://doi.org/10.3390/rs13173356
• SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation
  
  Wu, K., Hu, J., Lennox, B., & Arvin, F. (2021). SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation. Journal of Intelligent & Robotic Systems, 102(1), Article 12. https://doi.org/10.1007/s10846-021-01368-4
• Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning
  
  Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2020). Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 69(12). https://doi.org/10.1109/tvt.2020.3034800
• Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks
  
  Hu, J., Turgut, A. E., Krajnik, T., Lennox, B., & Arvin, F. (2020). Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks. IEEE Transactions on Cognitive and Developmental Systems, 14(1). https://doi.org/10.1109/tcds.2020.3018549
• Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach
  
  Hu, J., Bhowmick, P., Arvin, F., Lanzon, A., & Lennox, B. (2020). Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach. IEEE Robotics and Automation Letters, 5(2), 977-984. https://doi.org/10.1109/lra.2020.2966412
• Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots
  
  Liu, Z., West, C., Lennox, B., & Arvin, F. (2020). Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots. Sensors, 20(11), Article 3308. https://doi.org/10.3390/s20113308
• Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System
  
  Li, P.-Z., Zhang, D.-F., Hu, J.-Y., Lennox, B., & Arvin, F. (2020). Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System. Sensors, 20(9), 2587-2599. https://doi.org/10.3390/s20092587
• The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required
  
  Grieve, B. D., Duckett, T., Collison, M., Boyd, L., West, J., Yin, H., Arvin, F., & Pearson, S. (2019). The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required. Global Food Security, 23, 116-124. https://doi.org/10.1016/j.gfs.2019.04.011
• Mona: an Affordable Open-Source Mobile Robot for Education and Research
  
  Arvin, F., Espinosa, J., Bird, B., West, A., Watson, S., & Lennox, B. (2019). Mona: an Affordable Open-Source Mobile Robot for Education and Research. Journal of Intelligent & Robotic Systems, 94(3-4), 761–775. https://doi.org/10.1007/s10846-018-0866-9
• Advanced motions for hexapods
  
  Cheah, W., Khalili, H. H., Arvin, F., Green, P., Watson, S., & Lennox, B. (2019). Advanced motions for hexapods. International Journal of Advanced Robotic Systems, 16(2). https://doi.org/10.1177/1729881419841537
• Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging
  
  Arvin, F., Watson, S., Turgut, A. E., Espinosa, J., Krajník, T., & Lennox, B. (2018). Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging. Journal of Intelligent & Robotic Systems, 92(3-4). https://doi.org/10.1007/s10846-017-0673-8
• Bio-Inspired Embedded Vision System for Autonomous Micro-Robots: The LGMD Case
  
  Hu, C., Arvin, F., Xiong, C., & Yue, S. (2017). Bio-Inspired Embedded Vision System for Autonomous Micro-Robots: The LGMD Case. IEEE Transactions on Cognitive and Developmental Systems, 9(3), 241-254. https://doi.org/10.1109/tcds.2016.2574624
• Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm
  
  Arvin, F., Turgut, A. E., Krajník, T., & Yue, S. (2016). Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adaptive Behavior, 24(2), 102-118. https://doi.org/10.1177/1059712316632851
• Colias: An Autonomous Micro Robot for Swarm Robotic Applications
  
  Arvin, F., Murray, J., Zhang, C., & Yue, S. (2014). Colias: An Autonomous Micro Robot for Swarm Robotic Applications. International Journal of Advanced Robotic Systems, 11(7). https://doi.org/10.5772/58730
• Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method
  
  Arvin, F., Turgut, A. E., Bazyari, F., Arikan, K. B., Bellotto, N., & Yue, S. (2014). Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adaptive Behavior, 22(3), 189-206. https://doi.org/10.1177/1059712314528009