Autonomous Robots

, Volume 40, Issue 6, pp 1079–1093 | Cite as

Potential functions based sampling heuristic for optimal path planning

Article

Abstract

Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.

Keywords

Motion planning Convergence rate Optimal path planning Artificial potential fields Sampling based algorithms 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Robotics and Intelligent Systems Engineering (RISE) Lab, School of Mechanical and Manufacturing Engineering (SMME)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Department of System Innovation, Graduate School of Engineering ScienceOsaka UniversityToyonakaJapan

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