Accomplishing and Optimizing Robotic Self-Navigation Intelligence for Warehouse Logistics through Sensor Fusion and Antenna Localization

Abstract

Logistics automation has been the subject of many developments linking robotic hardware with improved quality and safety of work for warehouse employees. It is also a field where robotic navigation, route optimization and precise maneuvering are crucial to the competitiveness of the engineering product on the market. This research demonstrates a mathematical modeling method through which robots can navigate warehouse floors efficiently, using indoor positioning of high accuracy. Moreover, the paper also describes the hardware considerations taken into account when measuring the accuracy of the robot from achieving its destination on the floor plan. A prototype is creating, showing the sensor fusion interlinks for obstacle avoidance and distance mapping. Finally, the prototype and the algorithm are tested against eight static, as well as dynamic performance tests, in order to validate the performance of the system in static and dynamic interference environments. The goal of this paper is to present the engineering perspectives of optimizing a self-driving warehouse robot through utilization of the emerging technologies related to indoor positioning. This is presented through a prototype development of an Automated Guided Vehicle which will perform indoor positioning of itself across a warehouse floor plan and be able to avoid obstacles while driving towards its destination. Both the hardware description and the algorithmic modeling will be presented, accompanied by an extensive set of testing experiments, presented to determine the feasibility of our proposed developments in this field.