H* Algorithm: Enhancing A* for Smoother and More Feasible Robot Navigation

This paper presents <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{H}^{*}$</tex>, an enhanced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{*}$</tex> algorithm designed to improve the realism and efficiency of pathfinding for real-world agents, especially in robotics. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H^{*}$</tex> uses hexagonal grid decomposition and an improved geometric heuristic to model traversable space more effectively. By incorporating the agent's velocity and turning radius, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{H}^{*}$</tex> generates smoother, more realistic paths and detects sharp turns tailored to kinematic constraints. These enhancements aim to reduce travel time while maintaining feasibility for physical agents. Experimental results demonstrate the algorithm's practical benefits in realistic scenarios.

Authors

Institutions

Publication Details

Published
2025-06-30
DOI
https://doi.org/10.1109/ur65550.2025.11078090
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

H* Algorithm: Enhancing A* for Smoother and More Feasible Robot Navigation

Ashkan Izadi, Sergei Gabrielov
Robotic Path Planning Algorithms
article

H* Algorithm: Enhancing A* for Smoother and More Feasible Robot Navigation

Ashkan Izadi, Sergei Gabrielov
article en

Abstract

This paper presents <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{H}^{*}$</tex>, an enhanced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{*}$</tex> algorithm designed to improve the realism and efficiency of pathfinding for real-world agents, especially in robotics. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H^{*}$</tex> uses hexagonal grid decomposition and an improved geometric heuristic to model traversable space more effectively. By incorporating the agent's velocity and turning radius, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{H}^{*}$</tex> generates smoother, more realistic paths and detects sharp turns tailored to kinematic constraints. These enhancements aim to reduce travel time while maintaining feasibility for physical agents. Experimental results demonstrate the algorithm's practical benefits in realistic scenarios.

University of Houston - Downtown (US)
Openalex Percentile: Top 82%
Robotic Path Planning Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.