Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4350
Title: Multi-Feature Enhancement of Standard and Modified A-star and RRT Algorithms
Authors: Warrier, Arjun Rajeev
Nedunghat, Pranav
Bera, Manas Kumar
Kumar, Manas
Keywords: Path planning
Autonomous navigation
Mobile robot navigation
Obstacle avoidance
Issue Date: Jan-2024
Citation: 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC), University of Calcutta, India, 25-27 January 2024
Abstract: This manuscript suggests improvements in offline path planning algorithms: A-star (A*) and Rapidly Exploring Random Trees (RRT)- well-celebrated algorithms for robot path planning. The conventional algorithm of A* is reliable for path length efficiency, while RRT focuses on relaxing computation complexity. Specifically, this work aims to introduce three feature enhancements: bi-directionality, obstacle padding, and smoothing—to improve the capability of traditional algorithms. These enhancements have been applied to A*, a modified version of RRT, and the jump-point search (JPS) variant of A*. A comparative study is presented for the path-planning of a nonholonomic autonomous mobile robot to assess the effectiveness of these modified algorithms with augmented features. The simulations were conducted using MATLAB, where a configuration space with multiple obstacles is considered. Finally, insights into the advantages offered by these enhancements and their impact on the performance of the algorithms have been discussed in detail
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4350
Appears in Collections:Conference Papers

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