An Improved multi-objective a-star algorithm for path planning in a large workspace: Design, Implementation, and Evaluation

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Abstract

Improved path planning algorithms should minimize algorithm processing time, increase path smoothness, and shorten path length, all of which will be extremely beneficial for mobile robot traversal in large workspaces. As a result, an improved multi-objective A-star (IMOA-star) algorithm for mobile robot path planning in a large workspace was designed and implemented in Python 3.8.3 in this study. In four test cases, the proposed IMOA-star is evaluated in a large workspace with dimensions of 7120 cm × 9490 cm, and its performance is compared to the traditional A-star. When compared to the traditional A-star, the results showed that IMOA-star reduced the algorithm process time by 99.98%, improved path smoothness by 45%, reduced path length by 1.58%, and reduced the number of random points by 83.45%. Finally, the IMOA-star outperforms the traditional A-star in terms of algorithm processing time, path smoothness, path length, and the number of random points. As a result, it should be considered a viable alternative to the traditional A-star for mobile robot path planning in a large workspace.

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