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Timely fault mitigation for safer robot swarms (SEtS Doctoral Studentship)

   Department of Computer Science

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  Dr Alan Millard  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

About SEtS

This is a Studentship opportunity within the Doctoral Centre for Safe, Ethical and Secure Computing (SEtS) in the Department of Computer Science at the University of York. SEtS is a key initiative which supports our strategic vision to internationally lead education and research in the engineering of safe, ethical and secure computational systems. Find out more about SEtS.

About the Project

Robot swarms comprise many individual robots and therefore exhibit a degree of innate fault tolerance thanks to this built-in redundancy. They are robust in the sense that the complete failure of a few robots often has little detrimental effect on a swarm's overall collective performance, as they simply become obstacles in the environment. However, it has been shown that partially failed individuals (i.e. those whose sensors/motors fail, but otherwise remain operational) may cause problems due to their influence on the rest of the swarm [1]. An active approach to dealing with failed individuals is therefore required for long-term autonomous operation that does not require human intervention. This is particularly important if the partial or total failure of one or more robots could result in unsafe operation - e.g. causing human injury, or damage to property or the environment.

Previous research has investigated active approaches to fault detection [2], diagnosis [3], and recovery [4] for robot swarms, but fault mitigation has not yet been explored - i.e. reducing the impact of negative effects caused by faulty individual robots. It is imperative that this fault mitigation occurs in a timely manner, to improve safety by negating or reducing the severity of potential negative effects before they occur.

This research project will first explore centralised approaches to rapidly detecting, diagnosing, and predicting the effects of faults, which can then be used to inform a robot swarm of the appropriate mitigation action. The project will then tackle the harder problem of fault mitigation under the constraints of decentralised control and local sensing/communication, whereby individual robots must monitor their neighbours' behaviour to predict the effects of failures and intervene accordingly (e.g. through the use of internal simulation models [2, 5]).

It is anticipated that the research will begin in simulation, using a multi-robot simulator such as ARGoS [6]. The work would then be extended to physical hardware, such as a swarm of Pi-puck robots (Raspberry Pi extended e-puck robots) [7], in conjunction with an overhead camera and virtual sensors [8], with inter-robot communication implemented via Wi-Fi or XBee mesh networking.

Familiarity with one or more of the following is desired: Python/C++, Linux, robotics, embedded systems, statistical methods, machine learning.

For more details please contact Dr Alan Millard: [Email Address Removed].

Key Dates

  • Application submission period: 15 November 2022 - 15 February 2023
  • Interviews: 1 March - 20 March 2023
  • Notification of offers: 31 March 2023
  • Deadline for accepting studentship offers: 17 April 2023

Read more about the application process and follow our step-by-step guidance for applicants: How to apply

Informal enquiries

  • Project enquiries: Dr Alan Millard - [Email Address Removed]
  • Application enquiries: [Email Address Removed]


1. ​​Bjerknes, Jan Dyre, and Alan FT Winfield. "On fault tolerance and scalability of swarm robotic systems." Distributed autonomous robotic systems. Springer, Berlin, Heidelberg, 2013. 431-444.
2. Millard, Alan. Exogenous fault detection in swarm robotic systems. PhD thesis. University of York, 2016.
3. O'Keeffe, James, et al. "Adaptive online fault diagnosis in autonomous robot swarms." Frontiers in Robotics and AI 5 (2018): 131.
4. Oladiran, Oyinlola Ojuolape. Fault Recovery in Swarm Robotics Systems using Learning Algorithms. PhD thesis. University of York, 2019.
5. Blum, Christian, Alan FT Winfield, and Verena V. Hafner. "Simulation-based internal models for safer robots." Frontiers in Robotics and AI 4 (2018): 74.
6. Pinciroli, Carlo, et al. "ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems." Swarm intelligence 6.4 (2012): 271-295.
7. Allen, Jacob M., et al. "The Pi-puck ecosystem: hardware and software support for the e-puck and e-puck2." International Conference on Swarm Intelligence (ANTS), 2020.
8. Murphy, Alex, and Alan G. Millard. "Prototyping Sensors and Actuators for Robot Swarms in Mixed Reality." Annual Conference Towards Autonomous Robotic Systems.

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