An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Abstract

Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

A new heuristic with a multi-threaded implementation of a modified Firefly Algorithm

“…• α, β 0 , γ: the randomization factor, the attractiveness at the source, and the light absorption coefficient used for the Firefly Algorithm (Algorithm 2). 4…”

“…A widely developed category of heuristic methods is nature-inspired algorithms [3,4]. For these algorithms, researchers try to formulate algorithms that simulate Swarm-Intelligent based algorithms are inspired in the collective behavior of insects and animals that, despite individually they are not good on finding objectives, the whole swarm has an organization to achieve their objective.…”

Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

“…In recent years, mobile robot path planning research has been an active research area gaining considerable attention [6,26,35]. Despite the immense research in the area, achieving complete autonomous navigation in complex environment remains a challenge [2,17]. Roadmap path planning methods are gaining popular-Information Technology and Control 2019/2/48 180 ity in addressing mobile robot path planning problems [20].…”

Analysis of the Hybrid Global Path Planning Algorithm for Different Environments

“…In their research Lee et al (2011) indicated that ants carry out the exploration task relatively well around the starting point of the global path, however, they have a strong tendency not to explore and most of them follow the path found in the beginning of the search. To take advantage of the strengths of some approaches while reducing the effects of their drawbacks, some researchers have combined two or more algorithms in their investigations to provide efficient hybrid algorithms for path planning and control of AGVs (Ayawli et al, 2018). Elhoseny et al (2018) introduced a Bezier curve based global path planning, while using a modified genetic algorithm.…”

“…The analysis of the algorithm demonstrated that the algorithm performs reliably and convergences efficiently. In an overview work by Ayawli et al (2018) it is accented that even though there are challenges while using the hybrid algorithms, due to the reason that the hybrid algorithms seem to possess the strengths of the algorithms integrated while reducing their drawbacks, it can be considered to obtain better techniques for path planning of AGVs. Taking into consideration the assumption that the development and application of the hybrid path planning algorithms is a key factor to achieve safe and efficient path planning algorithms for AGVs and respectively, to increase traffic safety, the aim of this work is to further investigate and evaluate practical application possibilities of the hybrid path planning algorithm, recently developed by the authors of this work.…”