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Multi-robot Map Exploration Based on Multiple Rapidly-Exploring Randomized Trees

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Abstract

A map is necessary for tasks such as path planning or localization, which are common to mobile robot navigation. However, a map may be unavailable if the environment in which a robot navigates is unknown. Creating a map requires an exploration algorithm. Such algorithms guide robots to boundaries that separate known portions of a map from the unknown portions. Such boundaries are known as frontiers. There are image processing-based algorithms that detect frontiers. The authors’ previous work uses two, i.e., local and global, rapidly exploring random trees (RRTs) for two-dimensional exploration using a single robot. The present work applies the above to a multiagent system with three robots. A market-based strategy is used to allocate exploration tasks to the robots. Results show that the developed exploration strategy successfully explores a map in a reasonable amount of time compared to image processing-based approaches, and also reduces the map exploration cost. This strategy is readily usable for three-dimensional exploration with drones. It is also shown that the usage of a local frontier detector along with a global frontier detector helps reduce exploration time compared to simply using a global frontier detector alone. As an outline to future work, the paper also shows an extension of the proposed approach towards three-dimensional mapping using a single robot.

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Availability of Data and Materials

Any data necessary for simulation of the 2D RRT-based exploration algorithm can be generated using the code mentioned below.

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Correspondence to Shayok Mukhopadhyay.

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The exploration algorithm developed is available at https://github.com/hasauino/rrt_exploration.

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Mukhopadhyay, S., Umari, H. & Koirala, K. Multi-robot Map Exploration Based on Multiple Rapidly-Exploring Randomized Trees. SN COMPUT. SCI. 5, 31 (2024). https://doi.org/10.1007/s42979-023-02193-2

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