Paper Info
Title
Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments
Abstract
The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments. Intelligent sample insertion.Non-greedy trees connection.Improves the convergence rate.
Year
DOI
Venue
2015
10.1016/j.robot.2015.02.007
Robotics and Autonomous Systems
Keywords
Field
DocType
Motion planning,Sampling-based algorithms,RRT,RRT*,Optimal path planning,Bidirectional trees
Motion planning,Convergence (routing),Heuristic,Simulation,Computer science,Heuristics,Sampling (statistics)
Journal
Volume
ISSN
Citations 
abs/1703.08944
Robotics and Autonomous Systems 68 (2015): 1-11
15
PageRank 
References 
Authors
0.92
19
2
Name
Order
Citations
PageRank
Ahmed Hussain Qureshi1548.83
Yasar Ayaz26311.39