Journal
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
Volume 1, Issue 2, Pages 179-+Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1016/j.trit.2016.08.004
Keywords
Motion planning; DRM; Biased entropy classification; Hybrid boosting strategy
Categories
Funding
- National Natural Science Foundation of China (NSFC) [60875050, 60675025, 61340046]
- National High Technology Research and Development Program of China (863 Program) [2006AA04Z247]
- Science and Technology Innovation Commission of Shenzhen Municipality [201005280682A, JCYJ20120614152234873]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130001110011]
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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 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.
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