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稿件标题: Adaptive Region Boosting method with biased entropy for path planning in changing environment
稿件作者: Risheng Kang, Tianwei Zhang, Hao Tang, Wenyong Zhao
关键字词: Motion planning; DRM; Biased entropy classification; Hybrid boosting strategy
文章摘要: Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant challenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting (ARB) is proposed to increase the sampling density for difficult areas with different strategies. What's more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12
收录刊物: 2016年1卷2期
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