Adaptive Multi Objective Path Planning Optimization Algorithm via Variational Inference and Reinforcement Learning

DOI

10.2991/978-2-38476-479-2_4How to use a DOI?

Keywords

Multi-objective Path Planning; Variational Inference; Reinforcement Learning; Dynamic Environments; Path Length Optimization; Energy Consumption Optimization; Time Optimization; Adaptive Weight Adjustment

Abstract

With the rapid development of autonomous driving and robotics technologies, path planning has become a critical research topic, particularly in multi-objective optimization tasks. This paper proposes an adaptive multi-objective path planning optimization algorithm that combines Variational Inference (VI) and Reinforcement Learning (RL) to address the challenge of balancing multiple optimization objectives, such as path length, energy consumption, and time, in dynamic and complex environments. By incorporating Variational Inference, the algorithm effectively estimates the policy distribution in uncertain environments, enhancing the stability and robustness of the path planning process. Meanwhile, the Reinforcement Learning framework allows the algorithm to dynamically adjust the weights of different objectives based on real-time feedback, achieving adaptive balancing between the objectives. Experimental results show that the proposed algorithm outperforms traditional methods in both static and dynamic obstacle scenarios, particularly in terms of adaptability in dynamic environments and multi-objective optimization. Compared to traditional methods, the VI & RL-based algorithm not only optimizes path length, energy consumption, and time but also significantly improves computational efficiency and convergence speed. Finally, the paper discusses the advantages, limitations, and future research directions of the proposed method.

Copyright

© 2025 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  - Kaide Wen
PY  - 2025
DA  - 2025/11/19
TI  - Adaptive Multi Objective Path Planning Optimization Algorithm via Variational Inference and Reinforcement Learning
BT  - Proceedings of the 2025 International Conference on Education Research and Training Technologies (ERTT 2025)
PB  - Atlantis Press
SP  - 26
EP  - 37
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-479-2_4
DO  - 10.2991/978-2-38476-479-2_4
ID  - Wen2025
ER  -