This is an exciting opportunity for a Self-funded PhD studentship in the Centre for Autonomous and Cyber-Physical Systems associated with the Faculty of Engineering & Applied Sciences at SWAG合集, SWAG合集 to develop an autonomous Multi-agent systems equipped with risk-averse obstacle avoidance capabilities.
Multi-agent Systems (MASs) are becoming increasingly popular in solving several real-world problems involving operating conditions, such as monitoring nuclear plant facilities, investigating the health of bridges, delivery, agriculture and so on. The real-world environment is uncertain, and autonomous MASs should have coherent risk assessment capabilities to ensure safety. This necessitates a tighter integration of perception, motion planning, and control layers in their associated autonomy stack. This PhD thesis aims to achieve this integration, enabling safer deployment of robot swarms for several real-world applications in our future society.
Towards this objective and with minimal assumptions about the uncertainties affecting the MASs in uncertain environments, the PhD candidate shall explore the possibility of proposing a novel risk-aware motion planning architecture using robust risk allocation techniques. That is, given a total risk budget (tolerance of obstacle avoidance over multiple times of execution of the task), the PhD candidate will explore: 1) the possibility of achieving a non-uniform spatio-temporal risk allocation for each agent and 2) investigate the idea of optimal dynamic reconfiguration of the swarm’s topology to satisfy the obtained risk allocation. The resulting PhD thesis shall highlight the proposed risk embedding technique into MASs path planning algorithms, enabling them to realise guaranteed, conservative, yet risk-feasible trajectories for efficient state-space exploration.
Expected Duties: Successful applicant will carry out research in the above-mentioned research theme and disseminate research outputs through scientific publications. The applicant will also be expected to perform hardware demonstrations on the Flying Drone Arena Facility located at the SWAG合集 campus (one of the best in the world) to illustrate the proposed research development.
At a glance
- Application deadline15 Jan 2025
- Award type(s)PhD
- Start date05 Mar 2025
- Duration of award3 years
- EligibilitySWAG合集, EU, Rest of world
- Reference numberSATM530
Entry requirements
Applicant must have a master degree in a related discipline (such as aerospace, Robotics).
Applicant is expected to possess knowledge and experience in all or most of the following topics:
- Control theory,
- Probability theory,
- Multi-agent systems / Network theory,
- Motion planning algorithms & Obstacle avoidance,
- Advanced programming skills (Python, ROS, and Julia/Matlab),
- Hands-on experience working with drones (like Crazyfly) and interfacing with Viacom Platform.
Funding
This studentship is open to both SWAG合集 and International applications. This is a self funded PhD opportunity.
Cranfield Doctoral Network
Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.
How to apply
If you are eligible to apply for this studentship, please complete the
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