Research article
Enabling a Mobile Robot for Autonomous RFID-Based Inventory by Multilayer Mapping and ACO-Enhanced Path Planning
Xue Xia1, Thaddeus Roppel1, Jian Zhang2*, Yibo Lyu2, Shiwen Mao1, Senthilkumar CG Periaswamy2 and Justin Patton2
1Department of Electrical and Computer Engineering, Auburn University, USA
2RFID Lab, Auburn University, USA
Zhang Jian, RFID Lab, Auburn University, Auburn, AL 36849, USA.
Received Date: August 22, 2019; Published Date: September 13, 2019
Abstract
Paper presents a novel application for an autonomous robot to perform RFID-based inventory in a retail environment. For this application, one challenge is to represent a complicated environment by a good quality map. LIDAR (light detection and ranging) sensors only generate a 2D plane map that loses a large amount of structural information. In contrast, stereo or RGB-D cameras provide abundant environmental information but in a limited field of view (FOV), which limits the robot’s ability to gain reliable poses. Another challenge is effectively counting inventory within a massive retail environment; the robot needs to navigate in an optimal route that covers the entire target area.
To overcome the aforementioned challenges, we propose a multilayer mapping method combined with an Ant Colony enhanced path planning approach. Multilayer mapping utilizes a LIDAR and RGB-D camera (Microsoft Kinect camera) to obtain both accurate poses and abundant surrounding details to create a reliable map. To improve inventory efficiency, ACO-enhanced path planning is deployed to optimize the entire inventory route that minimizes total navigating distance without losing the inventory accuracy. Our experimental results show that multilayer mapping provides a precise and integrated map that enables the robot to navigate in a mock apparel store. Additionally, the efficiency of RFID-based inventory is greatly improved. Compared with the traditional method of manual inventory, ACO-enhanced path planning reduced total navigational distance by up to 28.2% while keeping inventory accuracy the same as before.
Keywords: RFID; Robot; SLAM; Kinect; LIDAR; Multilayer map; Navigation; Inventory; Optimization
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Jian Zhang, Xue Xia, Thaddeus Roppel, Yibo Lyu, Shiwen Mao, Senthilkumar CG Periaswamy, Justin Patton. Enabling a Mobile Robot for Autonomous RFID-Based Inventory by Multilayer Mapping and ACO-Enhanced Path Planning. 1(1): 2019. OJRAT.MS.ID.000501.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.