Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Abstract

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasks
in a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demanding
operations in order to unload the robots that are users of cloud services in this architecture. For path planning
on global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In order
to introduce smart cost map for further improvement of path planning in complex and crowded environment, implementation
of fuzzy inference system and learning algorithm is proposed. The results indicate the possibility of
applying a similar concept in different real-world robotics applications, in order to reduce the total paths length,
as well as to minimize the risk in path planning related to the human-robot interactions.