Sampling-Based Robot Motion Planning: A Review

By Mohamed Elbanhawi, M. Simic. 2014

770 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “A detailed overview of Sampling Based Planning (SBP) methods can be found in [262].”
Path Planning for Autonomous Mobile Robots: A Review (2021, methodology): “Elbanhawi and Simic [12] (2014) Only mentions No Only mentions Only mentions Yes Only mentions No No”
An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve (2021, neutral): “The purpose of path planning is to generate a collision-free route from the starting point to the destination, and the corresponding research has been ongoing ever since the inception and the implementation of mobile robots [1], [2].”
A Review of Motion Planning Techniques for Automated Vehicles (2016, methodology): “The development of intelligent planning algorithms is one of the key issues when designing robotic architectures [20], [21].”
Deep reinforcement learning based mobile robot navigation: A review (2021, methodology): “Commonly used methods include the A-star, ant colony optimization, and rapid-exploration random tree [6] , which rely on known static maps and are therefore difﬁcult to use in dynamic environments.”
Heuristic approaches in robot path planning: A survey (2016, background): “Despite their effectiveness, they provide no theoretical guarantees for reaching an optimal solution [24].”
Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions (2016, background): “, it finds a solution, if one exists, provided with infinite run time [4, 8].”