[Publications]

Journal papers

Tzoumas, G., Pitonakova, L., Salinas L., Scales, Ch., Richardson, T. & Hauert, S. (2022). Wildfire detection in large scale environments using force based control for swarms of UAVs. Swarm Intelligence, 17:89-115.

Pitonakova, L. & Bullock, S. (2020). The robustness-fidelity trade-off in Grow When Required neural networks performing continuous novelty detection. Neural Networks, 122, 183-195.
[about the project]

Pitonakova, L., Crowder, R. & Bullock, S. (2018). Information exchange design patterns for robot swarm foraging and their application in robot control algorithms. Frontiers in Robotics and AI, DOI: 10.3389/frobt.2018.00047
[about the project]

Pitonakova, L., Crowder, R. & Bullock, S. (2018). The Information-Cost-Reward framework for understanding robot swarm foraging. Swarm Intelligence, 12(1), 71-96.
[about the project]

Shamshiri R, R. ., Hameed, I. . A., Pitonakova, L., Weltzien, C., Balasundram, S. K., Yule, I. J., Grift, T. E., Chowdhary, G. (2018). Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison. International Journal Agricultural and Biological Engineering, 11(4), 15-31.

Shamshiri, R. R., Weltzien, C., Hameed, I. A., Yule, I. J., Grift, T. E., Balasundram, S. K., Pitonakova, L., Ahmad, D., Chowdhary, G. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal Agricultural and Biological Engineering, 11(4), 1–14.

Pitonakova, L., Crowder, R. & Bullock, S. (2016). Information flow principles for plasticity in foraging robot swarms. Swarm Intelligence, 10(1), 33–63.
[about the project]

Wilson, R., zu Erbach-Schoenberg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., Guthrie, S., Chamberlain, H., Brooks, C., Hughes, C., Pitonakova, L., Buckee, C., Lu, X., Wetter, E., Tatem, A. & Bengtsson, L. (2016). Rapid and near real-time assessments of population displacement using mobile phone data following disasters: The 2015 Nepal earthquake. PLOS Currents Disasters, DOI: 10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c.

Pitonakova, L. (2013). Ultrastable neuroendocrine robot controller. Adaptive Behaviour, 21(1), 47-63.
[about the project]

Conference publications (peer reviewed)

Salinas L., Tzoumas, G., Pitonakova, L. & Hauert, S. (2023). Digital twin technology for wildfire monitoring using UAV swarms. Proceedings of the 2023 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE. DOI: 10.1109/ICUAS57906.2023.10155819

Pitonakova, L., Winfield, A. F. T. & Crowder, R. (2018). Recruitment near worksites facilitates the robustness of foraging e-puck swarms to global positioning noise. Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), IEEE, 4276-4281.
[about the project]

Pitonakova, L., Crowder, R. & Bullock, S. (2018). The importance of information flow regulation in preferentially foraging robot swarms. Proceedings of the Eleventh International Conference on Swarm Intelligence (ANTS 2018), Springer, 277-289.
[about the project]

Pitonakova, L., Giuliani, M., Pipe, A., Winfield, A. (2018). Feature and performance comparison of the V-REP, Gazebo and ARGoS robot simulators. Proceedings of the 19th Towards Autonomous Robotic Systems Conference (TAROS 2018), Lecture Notes in Computer Science, vol 10965, Springer, 357-368.
[about the project]

Pitonakova, L., Crowder, R. & Bullock, S. (2017). Behaviour-Data Relations Modelling Language for multi-robot control algorithms. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), IEEE, 727-732.

Pitonakova, L., Crowder, R. & Bullock, S. (2016). Task allocation in foraging robot swarms: The role of information sharing. In Gershenson, C. et al. (eds.), Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XV), MIT Press, 306-313.

Pitonakova, L., Crowder, R. & Bullock, S. (2015). Design patterns for swarms of robot foragers. Abstract & poster at The International Conference on Intelligent Robots and Systems (IROS 2015), Hamburg, Germany.
[download the poster]

Pitonakova, L., Crowder, R. & Bullock, S. (2014). Understanding the role of recruitment in collective robot foraging. In Lipson, H. et al. (eds.), Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14), MIT Press, 264-271.

Pitonakova, L. & Bullock, S. (2013). Controlling ant-based construction. In Lio, P. et al. (eds.), Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems (ECAL 2013), MIT Press, 151-158.

Book chapters

Pitonakova, L. (2014). Afterword: Rise of the Machines. In: Amos, M. & Page, R. (eds.) Beta Life - Stories from an A-life Future. Comma Press, pp. 37-40.

Selected talks

2019: Modeling Multi-Agent Systems with Sketch BDRML: Invited talk, Swarm lunch, University of Bristol, Brustol, United Kingdom

2018: Multi-Robot Systems and Robot Swarms: Invited lecture, Robotic Systems course, University of Bristol, Brustol, United Kingdom

2018: Recruitment Near Worksites Facilitates the Robustness of Foraging E-puck Swarms to Global Positioning Noise: The International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain

2018: Designing Robot Swarms: A project overview: Invited talk at the University of Bristol, Bristol, United Kingdom

2017: Behaviour-Data Relations Modelling Language For Multi-Robot Control Algorithms: The International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada

2017: Designing Robot Swarms: A public lecture at the Southwest Futurists, Bristol.

2016: What is Life?: A public lecture at the Sceptics Cafe, Brighton.

2016: Task Allocation in Foraging Robot Swarms: The Role of Information Sharing: The Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XV)

2015: Object Oriented Software Development (In Python): A lecture for members of the Flowminder Foundation and of The World Food Programme.

2015: The Hive Mind game: University of Sussex, 2015

2015: Towards Design Patterns for Robot Swarms: Invited talk at the Bristol Robotics Laboratory, Bristol, United Kingdom

2014: Software Project Planning & Management: Workshop at 2014 Student Conference in Complexity Science (SCCS 2014)

2014: Understanding the Role of Recruitment in Collective Robot Foraging: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14)

2013: Controlling Ant-Based Construction: Twelfth European Conference on the Synthesis and Simulation of Living Systems (ECAL 2013)

2013: Information Exchange and Coordination in Robot Swarms: annual Agents, Interaction and Complexity meeting, University of Southampton

Selected posters

2018: The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms: The Eleventh International Conference on Swarm Intelligence (ANTS 2018), Rome, Italy

2018: Feature and Performance Comparison of the V-REP, Gazebo and ARGoS Robot Simulators.The 19th Towards Autonomous Robotic Systems Conference (TAROS 2018), Bristol, United Kingdom

2015: Design Patterns for Swarms of Robot Foragers: The International Conference on Intelligent Robots and Systems (IROS 2015), Hamburg, Germany

2015: Self-Organised Regulation of Foraging Traffic in Robotic Swarms: annual Complex Systems DTC meeting, University of Southampton

2014: Understanding the Role of Recruitment in Collective Robot Foraging: annual Complex Systems DTC meeting, University of Southampton

 

pyCreeper

The main purpose of pyCreeper is to wrap tens of lines of python code, required to produce graphs that look good for a publication, into functions. It takes away your need to understand various quirks of matplotlib and gives you back ready-to-use and well-documented code.

Novelty detection with robots using the Grow-When-Required Neural Network

The Grow-When-Required Neural Network implementation in simulated robot experiments using the ARGoS robot simulator.

Fast Data Analysis Using C++ and Python

C++ code that processes data and makes it available to Python, significantly improving the execution speed.

Designing Robot Swarms

This project looks at the challenges involved in modeling, understanding and designing of multi-robot systems.

Robustness in Foraging E-puck Swarms Through Recruitment

Swarms of five e-puck robots are used in a semi-virtual environment, facilitated by the VICON positioning system. Recruitment can make swarms more robust to noise in robot global positioning data.