## 6 Conclusions  
In this study, we presented a framework for autonomous navigation that integrates radar-based obstacle detection with BiGRU-enabled deep reinforcement learning. The research addresses the challenge of enhancing dynamic obstacle detection accuracy using radar sensors and improving navigation systems' decision-making by incorporating historical environmental data. The proposed method utilizes radar clustering and filtering techniques to accurately identify and track obstacles. The tracking system demonstrated effective performance across three experimental scenarios designed to simulate real-world conditions, including free motion, circular motion with overlaps, and inclined motion. The system achieved an average MOTA of 82.69% at 30 s, with particularly high accuracy during the initial 10 s, averaging 94.39%. While performance decreased over time due to increased complexity and occlusions, the results confirm the system's capability to reliably track individuals in dynamic and cluttered environments. Additionally, a BiGRU-enabled DRL framework was implemented to process sequential data, enabling the robot to leverage historical information for more effective and context-aware decision-making. An extensive simulation validation demonstrated the system's robustness and adaptability, showcasing superior performance over existing methods. The model achieved a 92% success rate, a 1% collision rate, and a time out 7% rate in scenarios with static obstacles. In dynamic  
Figure 10: Experimental scenario and trajectory of the robot during the test.  
environments, the system demonstrated superior performance with a 94% success rate and 6% collision rate, without any timeouts. In environments with static and dynamic obstacles, the proposed method achieved a success rate of 86% with a 14% collision rate, showcasing its ability to handle even more challenging scenarios. In the current implementation, the radar provides a 120-degree field of view, which limits the robot's ability to detect objects behind it. Future work will focus on enhancing sensor coverage through multi-radar configurations or by incorporating complementary sensors such as lidar and cameras. This approach aims to improve the robot's perception capabilities, enabling it to handle more diverse and complex scenarios effectively.  
Formal Analysis, N.P. and H.M.; Investigation, N.P., M.V. and E.M.; Methodology, N.P., E.M., E.A. and H.M.; Software, N.P., E.M. and M.V.; Supervision, H.M.; Validation, J.S., M.D., E.A., S.-H.P. and H.M.; Visualization, J.M.E.A., E.A., M.D. and H.M.; Writing--Original Draft, N.P., E.M. and M.V.; Writing--Review and Editing, N.P. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-RS-2020-II201460) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.