Abstract
This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: the travel distance to the target, collision safety with obstacles, and the rotation of the robot to face the target. Alternative solutions are evaluated by quantifying the relative importance of the objectives. As the FAHP algorithm is insufficient for multi-robot navigation, cooperative game theory is added to improve it. The performance of the proposed multi-robot navigation algorithm is tested with up to 12 mobile robots in several simulation conditions, altering factors such as the number of operating robots and the warehouse layout.
Highlights
Many research works have been undertaken and have focused on multi-robot systems due to their advantages such as complex task accomplishment, faster task completion based on parallelism, and their redundancy-based increase of robustness [1]
A light detection and ranging (LiDAR)-equipped small AGV model with a radius of 1.1 m is assumed to cover an area of a commercial AGV; i.e., KUKA KMP 1500, 2000 ×800
The main advantage of Fuzzy Analytic Hierarchy Process (FAHP) is that decisions are made through the relative importance of the considerations
Summary
Many research works have been undertaken and have focused on multi-robot systems due to their advantages such as complex task accomplishment, faster task completion based on parallelism, and their redundancy-based increase of robustness [1]. The application of mobile robots in various fields aims to improve safety as well as work efficiency. To maximize the efficiency of mobile robot-based services, the systems must have collision-free navigation capability to ensure safety. In a standardized or unchangeable environment, the production efficiency can be increased by taking the shortest distance and with the high-speed driving of the mobile robot. It is necessary to consider various objectives simultaneously in mobile robot path planning. To this end, many path planning research works have focused on multi-objective optimization problems, taking into account various aspects instead of focusing only on driving distance or collision safety with obstacles. Castillo et al [2] suggested a genetic algorithm to optimize the travel distance and travel difficulty of the path simultaneously
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