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Millimeter-Wave Robot Localization for Intelligent Material Handling by Abhishek Vashist, Ph.D. Candidate, Rochester Institute of Technology
Intelligent material handling (iMH) systems represent the next major step in the advancement of warehouse productivity and safety. Our vision is to establish a center for state-of-the-art research, application, and demonstration of iMH systems consisting of a fleet of autonomous material handling agents (forklifts, picking robots, etc.) capable of maximizing productivity while safely interacting with people and objects in a warehouse or production environment. One of the major technological challenges is the estimation of an autonomous agent’s location in a warehouse environment. To address the challenge of indoor localization, in our approach, we deploy 60 GHz based millimeterwave (mmWave) routers as Access Points (APs) on the ceiling of the warehouse and one router on the robotic agent acting as a Client. To localize the client, we use machine learning (ML) modeling to learn the complex relationship between the wireless features and the distances. The overall setup for the data collection and the test time inference is shown in Fig. 1. The robot setup with mmWave router is shown in Fig. 2.
Figure 1: Localization train and test setup
Figure 2: Robot setup in a test warehouse
The approach consists of two phases: offline phase and inference phase. In the offline phase, we construct the radio map of the warehouse aisle where the features are the Signal-to-Noise Ratio (SNR) values from the AP routers received at the Client. Before training the ML models we implement a data imputation and synthetic data augmentation on the collected training dataset. Performing augmentationbased training train our localization system to be more robust in the event of single or multiple AP failure. In our framework, we have designed a two-level ML learning approach for location prediction within the two aisles of the warehouse. The first ML model performs the aisle level classification and the second ML model takes the signal information to regress the position of the agent within the aisle. Further, the predictions from both the ML models are combined to predict the position in 2-dimension space within 18 | The ROCHESTER ENGINEER AUGUST 2022
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