Machine Learning-Driven Optimization of Mobile Robot Obstacle Avoidance: DQN-Based Simulation Data Analysis and Visualization

Hongyu Cao; Weixiang Gan

doi:10.47297/taposatWSP2633-456908.20260708

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Machine Learning-Driven Optimization of Mobile Robot Obstacle Avoidance: DQN-Based Simulation Data Analysis and Visualization

Volume 7, Issue 8

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Machine Learning-Driven Optimization of Mobile Robot Obstacle Avoidance: DQN-Based Simulation Data Analysis and Visualization
Machine Learning-Driven Optimization of Mobile Robot Obstacle Avoidance: DQN-Based Simulation Data Analysis and Visualization
1.Langfang Zhenguigu Technology Co., Ltd., Langfang, Hebei, 065000, China
2.Lincoln University College, Petaling Jaya, Selangor Darul Ehsan, 47301, Malaysia
Vol. 7, Issue 8, Pages: 41-45(2026)
Published：30 August 2026
DOI：10.47297/taposatWSP2633-456908.20260708
Accepted：

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Cao Hongyu,Gan Weixiang.Machine Learning-Driven Optimization of Mobile Robot Obstacle Avoidance: DQN-Based Simulation Data Analysis and Visualization[J].Theory and Practice of Science and Technology,2026,07(08):41-45.
DOI：

Cao Hongyu,Gan Weixiang.Machine Learning-Driven Optimization of Mobile Robot Obstacle Avoidance: DQN-Based Simulation Data Analysis and Visualization[J].Theory and Practice of Science and Technology,2026,07(08):41-45. DOI： 10.47297/taposatWSP2633-456908.20260708.

摘要

Abstract

This paper models mobile robot obstacle avoidance as a Markov decision process and trains a deep Q-network (DQN) on a 29-dimensional state of 24 lidar ranges and the relative goal pose. Across 600 random scenarios at three obstacle densities

the DQN reaches 90.5% success against 83.7% for the artificial potential field method (APF) and 84.0% for the dynamic window approach (DWA) under identical sensing (χ² = 11.85 and 10.82

p ≤ 0.001); it never times out

and its median travel time of 23.4 s is two thirds of DWA's 35.0 s. A reward ablation prices each term: the sparse reward yields 0% success

progress shaping 63.5%

a proximity penalty 92.0%

lifting the median margin from 0.447 m to 0.755 m. Seven figures trace each difference to its mechanism

and 143 of 151 collisions (94.7%) involve dynamic obstacles.

关键词

Keywords

references

Chen, Y. F., Liu, M., Everett, M., & How, J. P. (2017). Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. 2017 IEEE ICRA , 285-292.

Everett, M., Chen, Y. F., & How, J. P. (2018). Motion planning among dynamic, decision-making agents with deep reinforcement learning. 2018 IEEE/RSJ IROS , 3052-3059.

Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine , 4(1), 23-33.

Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics , 4(2), 100-107.

Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research , 5(1), 90-98.

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