## 5 Conclusions and Future Work  
A comprehensive framework for dynamic obstacle avoidance and trajectory prediction is proposed, applicable to both simulated and real-world environments. The method begins by dynamically classifying obstacles, followed by accurate prediction of high- and low-speed motion patterns of dynamic obstacles. Overall motion planning efficiency  
\begin{table}
\begin{tabular}{l l l l} \hline
**Environment** & **Algorithm** & **ADE** & **FDE** \\ \hline \multirow{4}{*}{Scene1} & ours & 0.024 & 0.053 \\  & LSTM [29] & 0.067 & 0.292 \\  & Kalman [19] & 1.250 & 2.033 \\  & Particle filter [30] & 1.250 & 2.033 \\  & UKF [31] & 2.281 & 4.360 \\ \hline \multirow{4}{*}{Scene2} & ours & 0.021 & 0.020 \\  & LSTM [29] & 0.096 & 0.108 \\  & Kalman [19] & 1.182 & 2.375 \\  & Particle filter [30] & 1.166 & 2.375 \\  & UKF [31] & 2.150 & 5.466 \\ \hline \multirow{4}{*}{Scene3} & ours & 0.026 & 0.058 \\  & LSTM [29] & 0.110 & 0.298 \\ \cline{1-1}  & Kalman [19] & 1.278 & 2.033 \\ \cline{1-1}  & Particle filter [30] & 1.277 & 2.033 \\ \cline{1-1}  & UKF [31] & 2.208 & 5.784 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of different algorithms on ADE and FDE metrics across various scenes.  
Figure 5: Dynamic obstacle prediction in real-world environments using the K-GRU Kalman framework with dynamic bounding boxes. (**a**) perspective generation for robots in static environments, (**b**) single obstacle detection and (**c**) detection of multi-dynamic obstacles  
is enhanced through optimization of node connectivity strategies. Experimental results from both simulation and real-world scenarios demonstrate that the proposed framework significantly improves motion safety in dynamic environments. Future work will focus on addressing motion planning challenges under perception-constrained conditions such as occlusion. Moreover, further improvements in motion safety and trajectory planning robustness are anticipated through optimization of multimodal perception fusion and dynamic risk assessment mechanisms.  
Methodology, validation, formal analysis, and writing, T.L. and Z.W.; conceptualization, supervision, and funding acquisition, T.Z. and X.L.; writing--review and editing, S.Z.; software, J.H.; investigation, Z.W. All authors have read and agreed to the published version of the manuscript.  
This paper was supported in part by Guangdong University Featured Innovation Program Project (2024 KTSCX057), Shenzhen International Science and Technology Cooperation Project (GJHZ20220913143204009), and University and Industry Cooperation (20221061030014).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author sincerely acknowledges the support of Shenzhen Technology University for providing access to the experimental facilities essential for this research. Special thanks are extended to the Sino-German Institute of Intelligent Manufacturing for the provision of key experimental equipment. The author also wishes to thank the laboratory colleagues for their valuable technical assistance and support in the preparation of this paper.  
The authors declare no conflicts of interest.