A robust, scalable multi-robot control and coordination framework achieving high throughput for parcel sorting centers
This dissertation presents a robust, scalable multi-robot control and coordination framework for high-throughput parcel sorting centres. The research addresses the complexities of multi-robot systems in indoor settings, where homogeneous robots operate on a shared grid, focusing on enhancing path...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/7316/1/fyp_CEA_2025_CCN.pdf http://eprints.utar.edu.my/7316/ |
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| Summary: | This dissertation presents a robust, scalable multi-robot control and coordination
framework for high-throughput parcel sorting centres. The research addresses the
complexities of multi-robot systems in indoor settings, where homogeneous robots
operate on a shared grid, focusing on enhancing pathfinding, coordination, and
throughput. It begins by highlighting the need for advanced multi-robot systems in
automation, identifying the limitations of existing multi-agent pathfinding (MAPF)
solutions, such as static assumptions, computational overhead, and inflexibility in
dynamic environments and human-robot interactions. To overcome these challenges,
the research introduces a novel framework that separates the problem into path planning
and resource allocation, simplifying the MAPF problem and optimizing robot
coordination. The algorithmic model supports efficient path generation, dynamic
resource allocation, and conflict resolution, with a focus on plan satisfiability and
operational feasibility. The implementation strategy includes dynamic plan updates,
operator selection, and traffic control mechanisms to enhance efficiency. Simulation
experiments demonstrate the framework’s adaptability and effectiveness in managing
multi-robot dynamics. Unlike traditional approaches, it employs a dynamic iterative
allocation method that handles uncertainties and optimizes plans in real time,
significantly reducing computational demands. Due to its assumption of a non perfect environment, unlike traditional algorithms, the framework is easier to implement.
By balancing reactive and deliberative strategies, this framework bridges the
gap between theoretical models and practical applications in robotics and automation.
The research demonstrates significant improvements in system throughput,
with pattern-matching allocators outperforming native allocators by up to 23.52%.
The framework’s traffic control mechanism achieves a 58.24% higher throughput
compared to systems without such controls. The research’s impact extends beyond
parcel sorting centres, offering potential applications in warehouse management,
manufacturing, and smart city logistics. Furthermore, its adaptability to dynamic
environments and human-robot interactions paves the way for more seamless integration
of robotic systems in human-centric workspaces, potentially revolutionizing
collaborative robotics. |
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