Context-Dependent Search for Generating Paths for Redundant Manipulators in Cluttered Environments

被引:0
|
作者
Rajendran, Pradeep [1 ]
Thakar, Shantanu [1 ]
Kabir, Ariyan M. [1 ]
Shah, Brual C. [1 ]
Gupta, Satyandra K. [1 ]
机构
[1] Univ Southern Calif, Ctr Adv Mfg, Viterbi Sch Engn, Los Angeles, CA 90007 USA
来源
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2019年
基金
美国国家科学基金会;
关键词
D O I
10.1109/iros40897.2019.8967865
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a context-dependent bi-directional tree-search framework for point-to-point path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes high-quality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15-fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.
引用
收藏
页码:5573 / 5579
页数:7
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