A review: On path planning strategies for navigation of mobile robot

By B. K. Patle, G. Babu L., Anish Pandey, D. Parhi, A. Jagadeesh. Defence Technology, 2019

796 citations recorded by the source corpus.

Abstract This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.

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

View Paper: https://www.semanticscholar.org/paper/e22cdb0e9f093e8b230f6b8fe4f9e593283c4971

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Alfonso J. García-Cerezo
2. Carlos Jesús Pérez-del-Pulgar
3. J. Sánchez-Ibáñez

In Computer Vision and Pattern Recognition

1. Alfonso J. García-Cerezo
2. Carlos Jesús Pérez-del-Pulgar
3. J. Sánchez-Ibáñez

In Control and Systems Engineering

1. Alfonso J. García-Cerezo
2. Carlos Jesús Pérez-del-Pulgar
3. J. Sánchez-Ibáñez

Citations in the corpus

• Path Planning for Autonomous Mobile Robots: A Review (2021, methodology): “The cells or the nodes from these graphs can store information regarding the surface at their location, in the form of static or dynamic elements [10].”