RoboPlanner: Towards an Autonomous Robotic Action Planning Framework for Industry 4.0

Autonomous robots are being increasingly integrated into manufacturing, supply chain and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans, that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases, that may be queried during planning and execution. In this paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we model the knowledge of world models, robotic capabilities and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses

An adaptive moments estimation technique applied to MST radar echoes

An adaptive spectral moments estimation technique has been developed for analyzing the Doppler spectra of the mesosphere-stratosphere-troposphere (MST) radar signals. The technique, implemented with the MST radar at Gadanki (13.5°N, 79°E), is based on certain criteria, set up for the Doppler window, signal-to-noise ratio (SNR), and wind shear parameters, which are used to adaptively track the signal in the range-Doppler spectral frame. Two cases of radar data, one for low and the other for high SNR conditions, have been analyzed and the results are compared with those from the conventional method based on the strongest peak detection in each range gate. The results clearly demonstrate that by using the adaptive method the height coverage can be considerably enhanced compared to the conventional method. For the low SNR case, the height coverage for the adaptive and conventional methods is about 22 and 11 km, respectively; the corresponding heights for the high SNR case are 24 and 13 km. To validate the results obtained through the adaptive method, the velocity profile is compared with global positioning system balloon sounding (GPS sonde) observations. The results of the adaptive method show excellent agreement with the GPS sonde measured wind speeds and directions throughout the height profile. To check the robustness and reliability of the adaptive algorithm, data taken over a diurnal cycle at 1-h intervals were analyzed. The results demonstrate the reliability of the algorithm in extracting wind profiles that are self-consistent in time. The adaptive method is thus found to be of considerable advantage over the conventional method in extracting information from the MST radar signal spectrum, particularly under low SNR conditions that are free from interference and ground clutter

Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots

Tracking of inventory and rearrangement of misplaced items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.Comment: 8 pages, 9 figures, 2 tables, IEEE Transactions on Systems, Man and Cybernetic