Bio-inspired flocking control for the autonomous and electric vehicles (AEVs) fleet in disaster response operations

Efficient disaster response operations demand well-coordinated and mobile resources to minimise casualties and property damage. Autonomous and electric vehicles (AEVs) have emerged as a promising solution to enhance disaster response fleets. They offer benefits such as continuous operation without fatigue, navigation in treacherous environments, renewable energy utilisation (e.g., solar power), and improved sensing capabilities and area coverage. However, controlling a fleet of AEVs in complex and dynamic disaster environments remains a significant challenge. Flocking control, inspired by collective behaviour of natural animal groups (e.g., birds and fishes), has the potential to address these complexities. This research aims to leverage this nature's wisdom and cutting-edge vehicle technologies to develop a bio-inspired flocking control system for the AEVs fleet, enhancing its coordination and navigation capabilities in disaster response operations.  

The primary objectives of this research project are as follows: 

1) Develop bio-inspired flocking control algorithms: Create advanced flocking control algorithms using machine learning (ML) techniques to enable the AEVs fleet to navigate disaster scenarios effectively. These algorithms will achieve collision avoidance, velocity consensus, and flock cohesion. 

2) Implement fault-tolerant mechanisms: Ensure the AEVs fleet’s reliability and robustness by implementing fault-tolerant mechanisms that swiftly detect and mitigate disruptions, including communication failures, sensor malfunctions, and actuator faults. 

3) Optimise fleet energy usage: Investigate energy-efficient strategies and explore the integration of renewable energy sources, particularly solar power, to enhance the operational duration of the AEVs fleet when conventional charging infrastructure is disrupted. 

4) Assess communication topology impact: Evaluate different communication topologies to enhance coordination and information exchange among AEVs during disaster response operations. 

This research project represents a fusion of natural principles and state-of-the-art vehicle technologies, offering the potential to transform disaster response operations. Its goal is to enhance the AEVs fleet’s coordination, resilience, and energy efficiency, ultimately contributing to more effective disaster response efforts. 

Open to both UK and international candidates.

Up to 30% of our UKRI-funded studentships can be awarded to candidates paying international rate fees. Find out more about eligibility.

You will need to meet the minimum entry requirements for our PhD programme.

We are seeking an enthusiastic and motivated candidate to join our ground-breaking research project, which focuses on leveraging bio-inspired flocking control for the autonomous and electric vehicles (AEVs) fleet engaged in disaster response operations. The ideal candidate should possess a diverse skill set encompassing automotive, robotics, machine learning (ML), and multi-agent control. This is a unique opportunity to contribute to cutting-edge research that bridges the gap between nature-inspired algorithms and advanced vehicle/robotics technologies. 

Qualifications and skills: 

1) A bachelor's or master's degree (or equivalent) in fields such as Robotics, Automotive Engineering, Control Engineering, Computer Science, or related disciplines. 

2) Proficiency in programming languages like Python and hands-on experience with ML libraries (e.g., TensorFlow, PyTorch). 

3) Familiarity with multi-agent systems and control algorithms. 

4) Competence in working with complex datasets, including tasks such as data collection, preprocessing, and analysis. 

5) Exceptional problem-solving skills, coupled with a creative and adaptable mindset. 

6) Outstanding communication and teamwork abilities to facilitate effective collaboration within an interdisciplinary research team. 

Responsibilities: 

1) Collaborate closely with research partners to develop and implement bio-inspired flocking control algorithms for the AEVs fleet involved in disaster response operations. 

2) Perform simulations and in-lab testing to evaluate the performance and reliability of flocking control in disaster response scenarios. 

3) Collect, preprocess, and analyse data, continuously refining flocking control strategies. 

4) Contribute to research publications, presentations, and potentially patent applications to share and disseminate findings. 

Join our team and be part of ground-breaking research that has the potential to revolutionise disaster response operations and enhance the overall efficiency and effectiveness of the AEVs fleet. Your contributions will play a crucial role in mitigating human suffering and property damage in disaster-stricken areas.