The Path Planning Method of Mobile Robot Based on Fusion A* and Jps Algorithm

The traditional Jump Point Search (JPS) algorithm presents several challenges: 1. Initially, the search time for each hypotenuse angle is prolonged,which may lead to the exploration of numerous unnecessary nodes and resulting in significant computational overhead.2. The algorithm tends to generate superfluous jump points during its calculations, which can extend the robot's path unnecessarily.3. It does not account for the robot's turning, causing the computed path to lack smoothness due to inherent limitations of the jump search methodology.To address these issues, this paper proposes the AJPS* algorithm, which integrates the strengths of both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{*}$</tex> and JPS algorithms according to the JPS algorithm by splitting the oblique angle calculation into a single point and placing it in the priority queue. Additionally, we revise the weight calculation formula within JPS to allow for adjustable search speeds based on parameter definitions, thereby enhancing computational efficiency. Finally, we implement redundant jump point elimination, hazardous point inspection, and Dubins curve optimization on the searched paths to achieve shorter routes with high reliability and applicability for robotic movement optimization. Experimental results indicate that compared to traditional JPS algorithms, AJPS* demonstrates average optimization efficiencies of 7.67 %, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 9. 1 \%}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 9. 2 \%}$</tex> in maze maps, rectangular obstacle maps, and irregular obstacle maps respectively; thus confirming its superior efficiency and applicability.