Autonomous Vehicles & Artificial Intelligence Laboratory (AVAILab)

Multidisciplinary laboratory focused on developments and applications within the fields of Mathematical Modelling, Optimisation, Soft & Natural Computing, Self-Organisation & Swarm Robotics, Autonomous Navigation, Computer Vision, and Positioning Systems.

Selected Publications

Below are selected publications since the AVAILAB’s foundation in 2016.
The full list of publications by Dr Mauro S. Innocente is available here.

Journal Papers

  1. I. PapagiannisM.S. Innocente, & E.I. Gkanas (2022). Synthesis and Characterisation of Iron Oxide Nanoparticles with Tunable Sizes by Hydrothermal Method. In Materials Science Forum, 1053, 176–181, Trans Tech Publications Ltd.
    DOI: 10.4028/p-0so8ha
  2. D. Rajput, J.M. Herreros, M.S. Innocente, J. Schaub, & A.M. Dizqah (2021). Electrified Powertrain with Multiple Planetary Gears and Corresponding Energy Management Strategy. Vehicles, 3, 341–356, MDPI.
    DOI: 10.3390/vehicles3030021
  3. P. Grasso, & M.S. Innocente (2020). Physics-based model of wildfire propagation towards faster-than-real-time simulations. Computers and Mathematics with Applications, 80, 790-808, Elsevier.
    DOI: 10.1016/j.camwa.2020.05.009
  4. A.M. Dizqah, B.L. Ballard, M.V. Blundell, S. Kanarachos, & M.S. Innocente (2020). A Non-Convex Control Allocation Strategy as Energy-Efficient Torque Distributors for On-Road and Off-Road Vehicles. Control Engineering Practice, 95, Elsevier.
    DOI: 10.1016/j.conengprac.2019.104256
  5. M.S. Innocente, & P. Grasso (2019). Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems. Journal of Computational Science, 34, 80-101, Elsevier.
    DOI: 10.1016/j.jocs.2019.04.009

Book Chapters

  1. P. Grasso, & M.S. Innocente (2022). Stigmergy-based collision-avoidance algorithm for autonomous firefighting drone swarms. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1420. Springer, Singapore.
    DOI: 10.1007/978-981-16-9573-5_19
  2. J.J. Tai, M.S. Innocente, & O. Mehmood (2022). FasteNet: A Fast Railway Fastener Detector. In: Yang XS., Sherratt S., Dey N., Joshi A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore.
    DOI: 10.1007/978-981-16-2377-6_71.
  3. M.S. Innocente (2021). Particle Swarms Reformulated towards a Unified and Flexible Framework. In: Tan Y., Shi Y. (eds.) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science, vol 12689. Springer, Cham.
    DOI: 10.1007/978-3-030-78743-1_25
  4. P. Grasso, & M.S. Innocente (2018). A two-dimensional reaction-advection-diffusion model of the spread of fire in wildlands. In Advances in Forest Fire Research 2018 (pp. 334-342). Imprensa da Universidade de Coimbra.
    DOI: 10.14195/978-989-26-16-506_36

Conference Papers

  1. P. Grasso, & M.S. Innocente (2020). Debiasing of position estimations of UWB-based TDoA indoor positioning system. In Proceedings of the 2020 UK-RAS Conference: ‘Robots into the Real World’, Lincoln, UK (virtual), 2020.
    DOI: 10.31256/Ua2Vp3X
  2. P. Grasso, & M.S. Innocente (2020). Theoretical study of signal and geometrical properties of two-dimensional UWB-based indoor positioning systems using TDoA. In Proceedings of the 6th International Conference on Mechatronics and Robotics Engineering, Barcelona, Spain, 2020.
    DOI: 10.1109/ICMRE49073.2020.9065121
  3. M.S. Innocente, & P. Grasso (2018). Swarm of autonomous drones self-organised to fight the spread of wildfires. In Proceedings of the GEOSAFE Workshop on Robust Solutions for Fire Fighting (Vol. 2146), L’Aquila, Italy, 2018. CEUR.
  4. M.S. Innocente, & P. Grasso (2018). Proof-of-Concept Swarm of Self-Organising Drones Aimed at Fighting Wildfires. In Proceedings of the 2017 UK-RAS Conference: ‘Robots Working for and among Us’, pp. 102–105, Bristol, UK, 2017.

Conference Posters

  1. D. Rajput, A.M. Dizqah, J.M. Herreros, & M.S. Innocente (2020). Effect of number of planetary gears on fuel consumption and vehicle performance in the hybrid powertrain of a passenger car. Poster session presented at the Future Powertrain Conference, Birmingham, UK.
  2. M.S. Innocente, & D.J. Rogers (2017). Thermal Modelling and Design Optimisation of DC-DC Converters. Poster session presented at EPSRC Centre for Power Electronics Annual Conference, Loughborough, UK.

MSc Theses

  1. Luke J. Smith (2019). Microscopic Traffic Modelling Using Cellular Automata.
    SupervisorDr Mauro S. Innocente (Coventry University)
  2. Thomas Staite (2019). Development of a Convolutional Neural Network for the Detection of Railway Track Maintenance.
    SupervisorDr Mauro S. Innocente (Coventry University)
  3. Paul E. Esugo (2018). Railway track following using Gazebo simulation and Erle-copter model.
    SupervisorDr Mauro S. Innocente (Coventry University)
  4. Matthew Sumner (2018). Development of a Convolutional ANN for Image Recognition to Detect Maintenance Issues in Railway Networks by Means of Autonomous Drones.
    SupervisorsDr Mauro S. Innocente & Magesh Nagarajan (Coventry University)
  5. Ryan Barnes-Batterbee (2018). Designing and Implementing a Differential Evolution Algorithm to Train Artificial Neural Networks.
    SupervisorDr Mauro S. Innocente (Coventry University)
  6. Paolo Grasso (2017). Mathematical modelling of spread of fire underpinning algorithmic developments for autonomous swarms of firefighting drones.
    SupervisorDr Mauro S. Innocente (Coventry University)
  7. Brandon L. Ballard (2016). Simulated Firefighting Fleet of Autonomous Drones.
    SupervisorDr Mauro S. Innocente (Coventry University)

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