mlsanityData to 7 Oct 2026

TD3 Based Collision Free Motion Planning for Robot Navigation

arXiv 2405.15460 ↗First posted 24 May 2024cs.ROcited by 18

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Abstract (arXiv)

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we propose the TD3-DWA algorithm, an innovative fusion of the traditional Dynamic Window Approach (DWA) with the Twin Delayed Deep Deterministic Policy Gradient (TD3). This hybrid algorithm enhances the efficiency of robotic path planning by optimizing the sampling interval parameters of DWA to effectively navigate around both static and dynamic obstacles. The performance of the TD3-DWA algorithm is validated through various simulation experiments, demonstrating its potential to significantly improve the reliability and safety of autonomous navigation systems.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2024.

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Citing papers per year: 2024: 15; 2025: 2; 2026: 1.

18 papers.

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  11. 2024Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clusteringcited by 2
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  4. 2024Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Modelscited by 0
  5. 2024Construction and optimization of health behavior prediction model for the elderly in smart elderly carecited by 0
  6. 2026Optimizing Path Planning using Deep Reinforcement Learning for UGVs in Precision Agriculturecited by 0