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An Optimized Probabilistic Roadmap Algorithm for Path Planning of Mobile Robots in Complex Environments with Narrow
Lijun Qiao1, Xiao Luo2, Qingsheng Luo1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|November 26, 2022
Summary
This study introduces an improved path planning algorithm for mobile robots navigating complex environments with narrow passages. The enhanced Probabilistic Roadmaps (PRM) method increases sampling density and optimizes path queries for efficient, collision-free navigation.
Area of Science:
- Robotics
- Artificial Intelligence
- Computer Science
Background:
- Autonomous mobile robots require efficient path planning in complex environments.
- Existing path planning algorithms struggle with environments featuring multiple narrow channels.
Purpose of the Study:
- To develop a novel path planning algorithm for mobile robots in complex environments with multiple narrow channels.
- To improve the efficiency and safety of autonomous navigation.
Main Methods:
- An improved Probabilistic Roadmaps (PRM) algorithm is proposed, combining PRM with the Artificial Potential Field (APF) algorithm.
- Sampling point density and distribution in narrow channels are optimized.
- The query process is optimized using a bidirectional A* algorithm and path pruning with a potential energy function.
Main Results:
- The enhanced PRM algorithm increases the density of free sampling points in narrow spaces.
- The algorithm achieves optimized, collision-free paths in complex environments.
- Reduced path length and required time for navigation were observed.
Conclusions:
- The proposed PRM-based path planning method effectively addresses the challenges of navigating complex environments with narrow channels.
- The algorithm offers a viable solution for autonomous mobile robot navigation, improving efficiency and safety.
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