Probabilistic scheduling for multi-robot systems and automation applications
- Publication Type:
- Thesis
- Issue Date:
- 2023
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Robots have significant potential across many sectors, but their widespread use is limited due to the complexity of real-world tasks. These can involve multiple robots, long durations, uncertainties, human interaction, and unclear objectives.
Current approaches for multi-robot planning under uncertainty are often limited due to their inability to scale to large, complex, real-world problems. Existing methods are either computationally intractable or impractically slow, which limits their real-time use.
This thesis proposes effective strategies for addressing real-world multi-robot problems by conceptualising them as probabilistic scheduling problems. This allows a deeper understanding of uncertainty's impact and guides the development of practical algorithms. The thesis presents solutions for four real-world scenarios, each demonstrating uncertainty in different aspects of scheduling challenges.
Through these examples, the thesis introduces a new theory of robotics scheduling that categorises four types of uncertainty: objectives, constraints, tasks, and resources. It presents novel techniques to manage these stochastic elements and demonstrates their effectiveness through analysis, simulation, and hardware trials. By solving these problems, this research opens up new fields of application with significant economic and social potential. The solutions and analytical framework will be valuable for future multi-robot planning and autonomous scheduling.
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