Abstract: This project is motivated by the possibility of a small number of autonomous vehicles (AVs) that may soon be present on our roads, and how these vehicles will influence the traffic flow. Specifically, driver assist technologies (often referred to as Society of Automotive Engineers (SAE) level 1 AVs) are already becoming standard features on many vehicles. However, it is still unclear to what extent driver assist features such as adaptive cruise control (ACC) will influence traffic stability.

We study the problem of optimal placement of energy storage devices in a distribution network to minimize network losses. A continuous tree with power flow equations is developed to model the distribution network. We analyze structural properties of the optimal solution when all loads have the same shape. We prove that it is optimal to place storage devices near the leaves of the network away from the substation, and that the scaled storage capacity monotonically increases towards the leaves.

This poster presents a method for detecting and correcting malicious data corruptions in PMU measurements. Detection of malicious injections is formulated as a compressed sensing problem and the actual signals are recovered using an online robust PCA-based algorithm. Different patterns of malicious injection attacks on PMU data are considered and the effect of corruption and reconstruction using the algorithm is analyzed on wide-area mode metering application. The performance of the proposed algorithm has been evaluated with different subspace selection.

Good algorithmic foundations for flight planning on the scale required for managing dense urban drone traffic we can expect to see in the future are currently still missing. This project provides prototype algorithms for managing this dense drone traffic. The project develops a concept for a coordination system that is able to find collision-free paths for a large number of flying unmanned air vehicles of different size and capability. It uses a hierarchical approach, combining centralized and local coordination, to manage complexity for a largescale problem.

In this ongoing study, we are exploring the suitability of a novel technique based on the estimation of muscle synergies to control a hand prosthesis. The objective of the study is to predict thirteen distinct hand grasp movements using EMG data. The method relies on projecting the EMG data using a basis derived via analysis of the muscle synergies. Five healthy subjects performed a set of thirteen distinct hand grasp movements while sEMG data from seven muscles have been recorded.

With electronic devices increasingly interacting with each other and with the physical world around them, device behavior is becoming more and more like human behavior, namely reliant on experiences and not just data. Hence, there’s a disconnect between how most devices have been engineered and what what practitioners are expecting them to do, and because of this fact, there’s also a disconnect between the common cyber-security protections that protect data and some of the desirable proofs and verifications that can be highly valuable to cyber-physical devices.

Recent years have seen a rapid influx of autonomy in critical agricultural sectors. Many applications of autonomy in agriculture are restricted to passive data collection and remote monitoring. At the same time, there are fundamentally important problems that remain relatively untreated by recent thrusts in autonomous precision agriculture. This project lies precisely at the intersection of important theoretical challenges in robotic planning and perception, and critical frontiers in agriculture.