Abstract
Path planning can be considered the most vital task of the autonomous robot. In this task, selecting an optimal route from the starting to the target position becomes an important problem that must be addressed when multiple competing optimization priorities are considered. Thus, a novel route assessment strategy based on a multi-criteria decision-making approach is proposed. The m-generalized q-neutrosophic PROMETHEE (PROMETHEE-mGqNS) method is applied to aggregate the competing route assessment requirements and choose an optimal route. A case study is investigated to explain the proposed strategy for path planning in a typical environment and indicates the method stability when incomplete input data characteristics are present.
Highlights
Most of the proposals mentioned above take into account only the navigation space characteristics and do not consider the other aspects that are of utmost importance for the maintenance of the mobile robots or other organizational management aspects. These aspects can be utilized in the path planning problem applying multi-criteria decision making (MCDM)
The application of autonomous robots is increasingly growing in many real-world areas, including monitoring and inspection tasks
A novel route assessment and selection strategy was proposed for the autonomous inspection robot, which implements the m-generalized q-neutrosophic PROMETHEE MCDM method, namely
Summary
The impressive progress of mobile robots can be perceived throughout industry. Most of the proposals mentioned above take into account only the navigation space characteristics and do not consider the other aspects that are of utmost importance for the maintenance of the mobile robots or other organizational management aspects These aspects can be utilized in the path planning problem applying multi-criteria decision making (MCDM). A new extension of the well-known MCDM method, WASPAS-SVNS, was applied to deal with the vague and uncertain initial information, which was modelled by neutrosophic sets Another aspect that can be considered of utmost importance is the possibility to model incomplete and uncertain information that arises in modern, real-life applications of pathfinding problems of autonomous mobile robots.
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