Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments

Liu, Tengfei; Wang, Zihe; Hu, Jiazheng; Zeng, Shuling; Liu, Xiaoxu; Zhang, Tan

doi:10.3390/app15137551

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Open AccessArticle

Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments

by

Tengfei Liu

^†,

Zihe Wang

^†

,

Jiazheng Hu

,

Shuling Zeng

,

Xiaoxu Liu

^* and

Tan Zhang

^*

Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Appl. Sci. 2025, 15(13), 7551; https://doi.org/10.3390/app15137551 (registering DOI)

Submission received: 4 June 2025 / Revised: 27 June 2025 / Accepted: 2 July 2025 / Published: 4 July 2025

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Abstract

This paper presents a novel motion planning framework for mobile robots operating in dynamic and uncertain environments, with an emphasis on accurate trajectory prediction and safe, efficient obstacle avoidance. The proposed approach integrates search-based planning with deep learning techniques to improve both robustness and interpretability. A multi-sensor perception module is designed to classify obstacles as either static or dynamic, thereby enhancing environmental awareness and planning reliability. To address the challenge of motion prediction, we introduce the K-GRU Kalman method, which first applies K-means clustering to distinguish between high-speed and low-speed dynamic obstacles, then models their trajectories using a combination of Kalman filtering and gated recurrent units (GRUs). Compared to state-of-the-art RNN and LSTM-based predictors, the proposed method achieves superior accuracy and generalization. Extensive experiments in both simulated and real-world scenarios of varying complexity demonstrate the effectiveness of the framework. The results show an average planning success rate exceeding 60%, along with notable improvements in path safety and smoothness, validating the contribution of each module within the system.

Keywords: motion planning; dynamic obstacles; GRUs; LSTM

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MDPI and ACS Style

Liu, T.; Wang, Z.; Hu, J.; Zeng, S.; Liu, X.; Zhang, T. Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments. Appl. Sci. 2025, 15, 7551. https://doi.org/10.3390/app15137551

AMA Style

Liu T, Wang Z, Hu J, Zeng S, Liu X, Zhang T. Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments. Applied Sciences. 2025; 15(13):7551. https://doi.org/10.3390/app15137551

Chicago/Turabian Style

Liu, Tengfei, Zihe Wang, Jiazheng Hu, Shuling Zeng, Xiaoxu Liu, and Tan Zhang. 2025. "Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments" Applied Sciences 15, no. 13: 7551. https://doi.org/10.3390/app15137551

APA Style

Liu, T., Wang, Z., Hu, J., Zeng, S., Liu, X., & Zhang, T. (2025). Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments. Applied Sciences, 15(13), 7551. https://doi.org/10.3390/app15137551

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Adaptive Motion Planning Leveraging Speed-Differentiated Prediction for Mobile Robots in Dynamic Environments

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