Mobile Robots Trajectory Planning: A Scoping Review

Background. Trajectory planning in robotics is still a current topic nowadays, even if searchers already have found traditional and intelligent algorithms for motion planning of one autonomous robot, the actual task is to find the most optimal and collision free solution for multiple robots. Because the dynamic environment of the robots is evolving constantly, study in this area has also to be updated. Data analysis of previous research is helping to find the best way to follow. Purpose. This review aims to explore the different algorithms and methods for solving trajectory planning tasks of autonomous mobile robots. In this paper, a quantified analysis is done. The part of the combined method and the type of environment for simulation in the analysed article are graphically illustrated. All these statistics are made to find the not enough explored area in mobile robot trajectory planning at the moment. This review is unique due to the quantitative approach and segmentation of the study area on important criteria: biologically inspired method, neural method and other combination method. Materials and methods. This review was conducted in accordance with the PRISMA-ScR recommendations for the search and selection of studies. The search was conducted exclusively on the popular scientific database ScienceDirect. Results. This review included 21 studies from 329 search results. In this paper a novel trend was identified, which wasn't mentioned in the last review focused on the topic of robots' development planning. The hypothesis has been proven by data analysis. Analysis has shown that the combination of several methods leads to solutions which offer the greatest number of advantages. Future research should take into account this finding. Conclusion. This scoping review shows that a number of recent studies are using geometric-based approaches, which contain various types of curves, to solve mobile robots trajectory planning tasks. Also, it was proven that using multi-methods to solve trajectory planning is the best way to find a global optimal solution. It should create more patents on robot trajectory planning in a dynamic environment using a combination of methods.