Reinforcement learning based mobile robot self-navigation with static obstacle avoidance
In this project, we explore the application of reinforcement learning for enhancing mobile robot self-navigation capabilities, specifically focusing on the challenge of static obstacle avoidance. Utilizing the Gazebo simulation environment integrated with the Robot Operating System (ROS), we impleme...
Saved in:
Main Author: | Yang, Shaobo |
---|---|
Other Authors: | Jiang Xudong |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/176676 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Modelling, control and simulation of a 4-legged robot for obstacle avoidance
by: Cham, Xun Yong
Published: (2024) -
Depth-based obstacle avoidance through deep reinforcement learning
by: Wu, Keyu, et al.
Published: (2020) -
Reinforcement learning based algorithm design for mobile robot static obstacle avoidance
by: Li, Zongrui
Published: (2021) -
Obstacle avoidance path design and simulation of in-space capsule service robotic arm based on YOLOv8
by: Jiang, Zhouhao
Published: (2024) -
Reinforcement learning for robot assembly
by: Vuong Quoc Nghia
Published: (2024)