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Development of Informed Rapidly-Exploring Random Tree Focused on Memory Efficient Path Planning

Published: 11 April 2022 Publication History

Abstract

Based on Rapidly-exploring random trees (RRTs) algorithms, this work demonstrates a new algorithm, iRRT* FN. The algorithm is a modified version of Informed RRT*, using a memory-efficient planning approach. It runs identically as Informed RRT* before the nodes in the tree reach a predetermined limit of nodes. Then the algorithm releases the memory load by pruning nodes in the tree, which are irrelevant to finding the goal. We test the algorithm on 2D models compared to RRT*. Not only do iRRT* FN has a higher convergence rate finding the optimal path, but the algorithm is also proven to have a higher memory efficiency. Especially in the real-world motion planning scenario, which has a large sample space, limiting the number of nodes in the tree can immensely improve the efficiency of RRTs.

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            cover image ACM Other conferences
            CIIS '21: Proceedings of the 2021 4th International Conference on Computational Intelligence and Intelligent Systems
            November 2021
            95 pages
            ISBN:9781450385930
            DOI:10.1145/3507623
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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            Published: 11 April 2022

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            Author Tags

            1. Path planning
            2. motion planning
            3. rapidly-exploring random trees
            4. sampling-based methods

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