This review article by Tong Xu from the School of Information Technology at Jiangsu Open University provides an in-depth analysis of recent advancements in the Rapidly-exploring Random Tree (RRT) algorithm, focusing on improvements and applications from 2021 to 2023. The RRT algorithm, known for its search and random sampling properties, has shown potential in generating high-quality paths that balance global and local optimality. The paper highlights theoretical improvements such as branching strategy enhancements, sampling strategy improvements, post-processing techniques, and model-driven RRT, as well as practical applications in various robotic scenarios, including welding, assembly, search and rescue, surgical, free-floating space, and inspection robots. The theoretical improvements discussed include: 1. **Branching Strategy Improvement**: Techniques like RRT*, EP-RRT*, CPT*, and B-RRT* have been developed to enhance node selection and branching, improving convergence and path quality. 2. **Sampling Strategy Improvement**: Methods such as Informed-RRT*, Quick-RRT*, and F-RRT* have been introduced to address slow convergence and improve sampling efficiency. 3. **Post-processing**: Various techniques, including curvature-aware planning, state prediction, and curve smoothing, have been applied to optimize paths and ensure they meet kinematic and scenario constraints. 4. **Model-driven RRT**: Integrations with machine learning classifiers, such as SVM and Random Trees, have been explored to enhance path planning in complex environments. The practical applications covered include: 1. **Welding Robots**: Path planning for efficient and collision-free welding operations. 2. **Assembly Robots**: Optimizing assembly processes in manufacturing. 3. **Search and Rescue Robots**: Planning optimal routes for rescue missions. 4. **Surgical Robots**: Enhancing surgical precision and efficiency. 5. **Free-floating Space Robots**: Planning trajectories for space exploration. 6. **Inspection Robots**: Efficiently navigating and inspecting environments. Despite the advancements, the paper also identifies challenges, including the difficulty in designing hyper-parameters, hardware reliability, and the need for further optimization in large-scale, unstructured environments. Future research directions include multi-type robot collaboration, human-robot collaboration, real-time path planning, and path planning in highly dynamic environments.This review article by Tong Xu from the School of Information Technology at Jiangsu Open University provides an in-depth analysis of recent advancements in the Rapidly-exploring Random Tree (RRT) algorithm, focusing on improvements and applications from 2021 to 2023. The RRT algorithm, known for its search and random sampling properties, has shown potential in generating high-quality paths that balance global and local optimality. The paper highlights theoretical improvements such as branching strategy enhancements, sampling strategy improvements, post-processing techniques, and model-driven RRT, as well as practical applications in various robotic scenarios, including welding, assembly, search and rescue, surgical, free-floating space, and inspection robots. The theoretical improvements discussed include: 1. **Branching Strategy Improvement**: Techniques like RRT*, EP-RRT*, CPT*, and B-RRT* have been developed to enhance node selection and branching, improving convergence and path quality. 2. **Sampling Strategy Improvement**: Methods such as Informed-RRT*, Quick-RRT*, and F-RRT* have been introduced to address slow convergence and improve sampling efficiency. 3. **Post-processing**: Various techniques, including curvature-aware planning, state prediction, and curve smoothing, have been applied to optimize paths and ensure they meet kinematic and scenario constraints. 4. **Model-driven RRT**: Integrations with machine learning classifiers, such as SVM and Random Trees, have been explored to enhance path planning in complex environments. The practical applications covered include: 1. **Welding Robots**: Path planning for efficient and collision-free welding operations. 2. **Assembly Robots**: Optimizing assembly processes in manufacturing. 3. **Search and Rescue Robots**: Planning optimal routes for rescue missions. 4. **Surgical Robots**: Enhancing surgical precision and efficiency. 5. **Free-floating Space Robots**: Planning trajectories for space exploration. 6. **Inspection Robots**: Efficiently navigating and inspecting environments. Despite the advancements, the paper also identifies challenges, including the difficulty in designing hyper-parameters, hardware reliability, and the need for further optimization in large-scale, unstructured environments. Future research directions include multi-type robot collaboration, human-robot collaboration, real-time path planning, and path planning in highly dynamic environments.