In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (∼ 300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

Industry 4.0 is rapidly transforming traditional manufacturing practices. Smart manufacturing technologies that automate research and development using a combination of robotic arms and domain-specific cyber-physical systems are at the core of this transformation. Unfortunately, dependence on networked communication increases the risk of security attacks, which must be mitigated using either platforms that are secure by design or intrusion detection and prevention systems. We report on an ongoing project to design and develop intrusion detection systems (IDS) for the Hein Lab, a smart manufacturing research lab in the chemical sciences domain. Designing effective IDS requires large datasets and high-quality, domain-specific benchmarks, which are difficult to obtain. To address this gap, we present the Robotic Arm Dataset (RAD), which we collected at the Hein Lab over a three-month period. We also present our non-intrusive tracing framework RATracer, which can be retrofitted onto any existing Python-based automation pipeline, and two sets of preliminary analyses based on the command and power data in RAD.

Modeling human mobility has a wide range of applications from urban planning to simulations of disease spread. It is well known that humans spend 80% of their time indoors but modeling indoor human mobility is challenging due to three main reasons: (i) the absence of easily acquirable, reliable, low-cost indoor mobility datasets, (ii) high prediction space in modeling the frequent indoor mobility, and (iii) multi-scalar periodicity and correlations in mobility. To deal with all these challenges, we propose WiFiMod, a Transformer-based, data-driven approach that models indoor human mobility at multiple spatial scales using WiFi system logs. WiFiMod takes as input enterprise WiFi system logs to extract human mobility trajectories from smartphone digital traces. Next, for each extracted trajectory, we identify the mobility features at multiple spatial scales, macro and micro, to design a multi-modal embedding Transformer that predicts user mobility for several hours to an entire day across multiple spatial granularities. Multi-modal embedding captures the mobility periodicity and correlations across various scales while Transformers capture long term mobility dependencies boosting model prediction performance. This approach significantly reduces the prediction space by first predicting macro mobility, then modeling indoor scale mobility, micro mobility, conditioned on the estimated macro mobility distribution, thereby using the topological constraint of the macro-scale. Experimental results show that WiFiMod achieves a prediction accuracy of at least 10% points higher than the current state-of-art models. Additionally, we present 3 real-world applications of WiFiMod - (i) predict high density hot pockets and space utilization for policy making decisions for COVID19 or ILI, (ii) generate a realistic simulation of indoor mobility data to simulate spread of diseases, (iii) design personal assistants.

With the current COVID-19 pandemic, contact tracing and building occupancy tracking are key components of re-opening policies and quickly containing virus outbreaks. WiFiMon is a network-centric contact tracing method that uses enterprise WiFi networks logs for tracking devices and inferring building occupancy and building contact tracing reports in office and campus settings.