An End-to-End Path Planner Combining Potential Field Method With Deep Reinforcement Learning
Author:
Affiliation:
1. Modern Sensor and Photoelectric Measurement Laboratory, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
2. Shanghai Institute of Satellite Engineering, Shanghai, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/7361/10637303/10599236.pdf?arnumber=10599236
Reference26 articles.
1. Obstacle avoidance and path planning schemes for autonomous navigation of a mobile robot: A review;Oroko
2. LSPP: A Novel Path Planning Algorithm Based on Perceiving Line Segment Feature
3. A Formal Basis for the Heuristic Determination of Minimum Cost Paths
4. Obstacle Avoidance in Multi-Agent Formation Process Based on Deep Reinforcement Learning
5. A review of motion planning algorithms for intelligent robots
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