Paper Info
Title
Context-Dependent Search For Generating Paths For Redundant Manipulators In Cluttered Environments
Abstract
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.
Year
DOI
Venue
2019
10.1109/IROS40897.2019.8967865
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Motion planning,Type selection,Computer science,Failure rate,Control engineering,Scenario testing,Artificial intelligence,Machine learning
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Pradeep Rajendran101.69
Shantanu Thakar222.91
Ariyan M. Kabir3186.94
Brual C. Shah4154.85
Satyandra K Gupta568777.11