Car-like mobile robot navigation: A survey

Abstract

Car-like mobile robot navigation has been an active and challenging field both in academic research an in industry over the last few decades, and it has opened the way to build and test (recently) autonomously driven robotic cars which can negotiate the complexity and uncertainties introduced by real urban and suburban environments. In this chapter, we review the basic principles and discuss the corresponding categories in which current methods and associated algorithms for car-like vehicle autonomous navigation belong. They are used especially for outdoor activities and they have to be able to account for the constraints imposed by the non-holonomic type of movement allowable for car-like mobile robots. In addition, we present a number of projects from various application areas in the industry that are using these technologies. Our review starts with a description of a very popular and successful family of algorithms, namely the Rapidly-exploring Random Tree (RRT) planning method. After discussing the great variety and modifications proposed for the basic RRT algorithm, we turn our focus to versions which can address highly dynamic environments, especially those which become increasingly uncertain due to limited accuracy of the sensors used. We, subsequently, explore methods which use Fuzzy Logic to address the uncertainty and methods which consider navigation solutions within the holistic approach of a Simultaneous Localization and Mapping (SLAM) framework. Finally, we conclude with some remarks and thoughts about the current state of research and possible future developments.