A* Path Planning Algorithm Optimization via Unscented Kalman Filter in Dynamic Environments

DOI

10.2991/978-94-6463-986-5_35How to use a DOI?

Keywords

Path Planning; A* algorithm; UKF; Dynamic obstacles; Obstacle avoidance

Abstract

The fast advancement of robot technology in recent years has drawn a lot of attention to path planning algorithms in dynamic situations. The classic A* path planning algorithm faces numerous hurdles because of its limited adaptation in dynamic contexts. These challenges include poor real-time performance, a high risk of planning failure, and problems with the heuristic function’s adaptability. This study proposes a method that combines the A* algorithm with the Unscented Kalman Filter (UKF). This method uses UKF for state estimation and obtaining dynamic obstacle predictions, providing prior information for path planning, and ultimately enabling the A* algorithm to reduce the expansion of unnecessary nodes. Simulation experiments show that compared with the original A* algorithm, the improved UKF-A* performs better in planning time and failure rate, reducing planning time by 10%–30% and lowering the failure rate. Preliminary results show that this method has great potential. The optimization approach put forth in this study, which combines path planning algorithms with sensor filtering techniques (UKF), expands the field of conventional path planning research by providing a fresh viewpoint on how to handle path planning difficulties in complex dynamic environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiazhe Wang
PY  - 2026
DA  - 2026/02/18
TI  - A* Path Planning Algorithm Optimization via Unscented Kalman Filter in Dynamic Environments
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 324
EP  - 333
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_35
DO  - 10.2991/978-94-6463-986-5_35
ID  - Wang2026
ER  -