A Survey of Path Planning Algorithms for Mobile Robots

Abstract

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

An Efficient and Robust Improved A* Algorithm for Path Planning

“…The shown cost function includes distance and costs. The above considerations avoid sudden turns and, in this manner, improve the path smoothness [10].…”

“…The Dijkstra algorithm [8] and its variants are commonly used in applications such as Google Maps and other traffic routing systems. To overcome Dijkstra's computational-intensity doing blind searches, A* [12] and its variants are presented as state-of-the-art algorithms for use within static environments [10].…”

“…A visibility diagram was one of the very first graph-based search algorithms used in path planning in robotics [10,31]. A hybrid A* algorithm with the visibility diagram planning algorithm is proposed in [14] to overcome the issue of high run costs to convergence.…”

“…For application scenarios such as warehousing and logistics, path planning in a static environment assumes that the robot perceives the environment and uses local path planning algorithms when the environmental information is not fully grasped. A* is used for shortest path evaluation based on the information regarding the obstacles present in the static environment [10], The shortest path evaluation for the known static environment is a two-level problem which comprises a selection of feasible node pairs and a shortest path evaluation based on the obtained feasible node pairs [11]. Both of the above-mentioned criteria are not available in a dynamic environment, which makes the algorithm inefficient and impractical in dynamic environments.…”

The EBS-A* algorithm: An improved A* algorithm for path planning

“…For application scenarios such as warehousing and logistics, path planning in a static environment assumes that the robot perceives the environment and uses local path planning algorithms when the environmental information is not fully grasped. A* is used for shortest path evaluation based on the information regarding the obstacles present in the static environment [ 17 ]. The shortest path evaluation for the known static environment is a two-level problem.…”