Sampling-Based Robot Motion Planning: A Review

Abstract

Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

“…Initial complete practical planners such as Road Map (RM), Potential Fields, and Cell Decomposition (CD) techniques are unable to deal with dynamic and complex high dimension problems [1,[7][8][9][10]. Computational complexity of complete planners limits their applications to low dimensional problems [11].…”

“…Sampling Based Planning (SBP) approaches are the most influential advancement in path planning [7,8]. Major advantages of Sampling Based Planning (SBP) are low computational cost, applicability to high dimensional problems and better success rate for complex problems [8,29].…”

“…Major advantages of Sampling Based Planning (SBP) are low computational cost, applicability to high dimensional problems and better success rate for complex problems [8,29]. SBPs are probabilistic complete, i.e., it finds a solution, if one exists, provided with infinite run time [4,8].…”

“…Major advantages of Sampling Based Planning (SBP) are low computational cost, applicability to high dimensional problems and better success rate for complex problems [8,29]. SBPs are probabilistic complete, i.e., it finds a solution, if one exists, provided with infinite run time [4,8]. Most popular SBP algorithms are Probabilistic Roadmap (PRM) [7,8,30], Rapidly-exploring Random Tree (RRT) [11,31] and Rapidly-exploring Random Tree Star (RRT*) [7].…”

“…SBPs are probabilistic complete, i.e., it finds a solution, if one exists, provided with infinite run time [4,8]. Most popular SBP algorithms are Probabilistic Roadmap (PRM) [7,8,30], Rapidly-exploring Random Tree (RRT) [11,31] and Rapidly-exploring Random Tree Star (RRT*) [7]. PRM based methods [7,32] are mostly used in highly structured static environment such as factory floors [11,29,33].…”