An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments
Path planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework,...
Saved in:
| Main Authors: | Ting Jiao, Conglin Hu, Lingxin Kong, Xihao Zhao, Zhongbao Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10856113/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Comprehensive Review of Deep Learning Techniques in Mobile Robot Path Planning: Categorization and Analysis
by: Reza Hoseinnezhad
Published: (2025-02-01) -
Narrow-Route Path Planning for Mobile Robots Using Deep Deterministic Policy Gradient Considering Turning Radius Limit
by: Naoki Motoi, et al.
Published: (2024-01-01) -
Priority-Based Reward Mechanism for Dual-Robot Path Planning in Dynamic Environments
by: Yibo Hu, et al.
Published: (2025-01-01) -
Path Planning of Mobile Robot in Dynamic Obstacle Avoidance Environment Based on Deep Reinforcement Learning
by: Qingfeng Zhang, et al.
Published: (2024-01-01) -
RL-Based Vibration-Aware Path Planning for Mobile Robots’ Health and Safety
by: Sathian Pookkuttath, et al.
Published: (2025-03-01)