Adaptive Potential guided directional-RRT

Author

Qureshi, Ahmed Hussain ; Mumtaz, Sami ; Iqbal, Khawaja Fahad ; Ali, Borhanuddin ; Ayaz, Y. ; Ahmed, Foisal ; Muhammad, Mannan Saeed ; Hasan, Osman ; Whoi Yul Kim ; Moonsoo Ra

Author_Institution

RISE Lab., NUST, Islamabad, Pakistan

fYear

2013

fDate

12-14 Dec. 2013

Firstpage

1887

Lastpage

1892

Abstract

The Rapidly Exploring Random Tree Star (RRT*) is an extension of the Rapidly Exploring Random Tree path finding algorithm. RRT* guarantees an optimal, collision free path solution but is limited by slow convergence rates and inefficient memory utilization. This paper presents APGD-RRT*, a variant of RRT* which utilizes Artificial Potential Fields to improve RRT* performance, providing relatively better convergence rates. Simulation results under different environments between the proposed APGD-RRT* and RRT* algorithms demonstrate this marked improvement under various test environments.

Keywords

collision avoidance; convergence; mobile robots; trees (mathematics); APGD-RRT*; RRT* performance improvement; adaptive potential guided directional-RRT; artificial potential fields; autonomous robots; collision free path solution; convergence rates; memory utilization; path planning; rapidly exploring random tree path finding algorithm; rapidly exploring random tree star; Algorithm design and analysis; Convergence; Educational institutions; Equations; Mathematical model; Planning; Robots; Artificial Potential Fields; Directional Sampling and Path Planning; Fast Convergence Rate; Optimal Path; RRT∗;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on

Conference_Location

Shenzhen

Type

conf

DOI

10.1109/ROBIO.2013.6739744

Filename

6739744