Collaborative Multi-Robot Frontier-Based Mapping with Memory Constraints

Grumski, Nicholas; Hyde, Abigail

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Collaborative Multi-Robot Frontier-Based Mapping with Memory Constraints

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Simultaneous localization and mapping, or SLAM, is a problem in robotics in which a robot must track its own location while building a map of the environment at the same time. The use of multiple robots adds to the complexity of this problem, but has proven useful in many applications such as forest fires and disaster response. Previous research in Collaborative SLAM (C-SLAM) focused on coordinating robot navigation and map merging for maximum efficiency in mapping an area. However, little research exists that considers minimalistic robots with limited onboard memory. This project analyzes multiple algorithms for their performance with regard to the C-SLAM problem with a robot memory capacity constraint, such that no one robot could hold the entire map. To achieve this, the team performed a literature review to identify algorithms from similar problems. These algorithms were then adapted to a series of subproblems. Each sub-problem isolated a factor of the larger C-SLAM problem and allowed for a progression of complexity. In the sub-problem simulations, each algorithm’s performance was evaluated to find which one was best suited to the C-SLAM problem. The team took the best algorithm and implemented it into a multi-robot system to collaboratively map an environment.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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