研究目的
To propose a state-of-the-art algorithm to detect transparent obstacles by analyzing the pattern of the re?ected noise generated when a laser meets a transparent obstacle, enabling mobile robots to avoid transparent obstacles while moving towards the destination.
研究成果
The proposed algorithm successfully detects transparent obstacles by analyzing reflection noise patterns, enabling mobile robots to avoid them while navigating. The environment map generated by the algorithm shows improved performance compared to using only data collected from LRF, with an average error rate reduction of 11.62%.
研究不足
The algorithm assumes transparent obstacles are in the form of a straight line, no opaque obstacle exists adjacent to the transparent obstacle on the opposite side of the LRF, and all obstacles are fixed in their positions. The algorithm may not remove all reflection noise, leaving some obstacle candidates on the opposite side of transparent obstacles.
1：Experimental Design and Method Selection:
The study uses a laser range finder (LRF) to detect transparent obstacles by analyzing reflection noise patterns. The algorithm processes reflection noises to distinguish them from common noise and identifies the boundaries of transparent obstacles.
2：Sample Selection and Data Sources:
The experiment uses a Pioneer P3DX mobile robot equipped with an LMS100-10000 laser range finder from SICK AG in environments with transparent obstacles.
3：List of Experimental Equipment and Materials:
Pioneer P3DX mobile robot, LMS100-10000 laser range finder, transparent obstacles (glass sheets with 0.5 cm thickness).
4：5 cm thickness).
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The robot drives alongside or towards transparent obstacles, collecting data with the LRF. The algorithm processes this data to generate an environment map and perform path planning.
5：Data Analysis Methods:
The algorithm analyzes the reflection noise pattern to detect transparent obstacles and updates the environment map accordingly. Path planning is performed using the Wavefront-propagation algorithm.
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