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RRT-CS: A free-collision planner for capsule-like SCORBOT by iterated learning
Hung Nguyen1, Thanh Phuong Nguyen1, Song Hung Nguyen2,3
1HUTECH Institute of Engineering, HUTECH University, Ho Chi Minh, Vietnam.
Plos One
|May 19, 2025
Summary
This study introduces an enhanced Rapidly-exploring Random Trees (RRT) algorithm with visual servoing for robot navigation in unknown environments. The efficient approach demonstrates suitability for real-world robotic applications.
Area of Science:
- Robotics
- Artificial Intelligence
- Computer Vision
Background:
- Robotic navigation in unknown environments presents significant challenges.
- Accurate environment recognition and path planning are crucial for autonomous systems.
Purpose of the Study:
- To enhance the Rapidly-exploring Random Trees (RRT) algorithm with visual servoing for improved environment recognition.
- To evaluate the algorithm's effectiveness and robustness on a SCORBOT-ER-VII robotic platform.
Main Methods:
- Integration of an enhanced RRT algorithm with a visual servoing technique.
- Utilizing the SCORBOT-ER-VII robotic platform with multiple links and servo motors.
- Testing across diverse scenarios, from obstacle-free to complex configurations, via simulations and hardware experiments.
Main Results:
- Planning time correlates with environmental complexity.
- Trajectory smoothing accounts for <10% of processing time.
- RRT-based profile generation constitutes the majority (2/3) of computational effort.
Conclusions:
- The enhanced RRT algorithm with visual servoing is computationally efficient.
- The proposed method is robust and suitable for real-world robotic navigation tasks.
- The approach effectively handles environment recognition and path planning challenges.
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