Verified multi-robot planning under uncertainty

Faruq, Fatma ORCID: 0000-0001-6928-0176 (2022). Verified multi-robot planning under uncertainty. University of Birmingham. Ph.D.

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Multi-robot systems are being increasingly deployed to solve real-world problems, from warehouses to autonomous fleets for logistics, from hospitals to nuclear power plants and emergency search and rescue scenarios. These systems often need to operate in uncertain environments which can lead to robot failure, uncertain action durations or the inability to complete assigned tasks. In many scenarios, the safety or reliability of these systems is critical to their deployment. Therefore there is a need for robust multi-robot planning solutions that offer guarantees on the performance of the robot team. In this thesis we develop techniques for robust multi-robot task allocation and planning under uncertainty by building on techniques from formal verification.

We present three algorithms that solve the problem of task allocation and planning for a multi-robot team operating under uncertainty. These algorithms are able to calculate the expected maximum number of tasks the multi-robot team can achieve, considering the possibility of robot failure. They are also able to reallocate tasks when robots fail. We formalise the problem of task allocation and robust planning for a multi-robot team using Linear Temporal Logic to specify the team's mission and Markov decision processes to model the robots. Our first solution method is a sampling based approach to simultaneous task allocation and planning. Our second solution method separates task allocation and planning for the same problem using auctioning for the former. Our final solution lies midway between the first two using simultaneous task allocation and planning in a sequential team model. We evaluate all solution approaches extensively using a set of tests inspired by existing benchmarks in related fields with a focus on scalability.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
Licence: Creative Commons: Attribution-Noncommercial-Share Alike 4.0
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Computer Science
Funders: None/not applicable
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software


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