Approximating Reachable Sets for Neural Network based Models in Real-Time via Optimal Control

In this paper, we present a data-driven framework for real-time estimation of reachable sets for control systems where the plant is modeled using neural networks (NNs). We utilize a running example of a quadrotor model that is learned using trajectory data via NNs. The NN learned offline, can be excited online to obtain linear approximations for reachability analysis. We use a dynamic mode decomposition based approach to obtain linear liftings of the NN model. The linear models thus obtained can utilize optimal control theory to obtain polytopic approximations to the reachable sets in real-time. The polytopic approximations can be tuned to arbitrary degrees of accuracy. The proposed framework can be extended to other nonlinear models that utilize NNs to estimate plant dynamics. We demonstrate the effectiveness of the proposed framework using an illustrative simulation of quadrotor dynamics.Comment: 14 pages, 11 figures, journal paper that has been conditionally accepte

Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements

A practical challenge which arises in the operation of sensor networks is the presence of sensor faults, biases, or adversarial attacks, which can lead to significant errors incurring in the localization of the agents, thereby undermining the security and performance of the network. We consider the problem of identifying and correcting the localization errors using inter-agent measurements, such as the distances or bearings from one agent to another, which can serve as a redundant source of information about the sensor network's configuration. The problem is solved by searching for a block sparse solution to an underdetermined system of equations, where the sparsity is introduced via the fact that the number of localization errors is typically much lesser than the total number of agents. Unlike the existing works, our proposed method does not require the knowledge of the identities of the anchors, i.e., the agents that do not have localization errors. We characterize the necessary and sufficient conditions on the sensor network configuration under which a given number of localization errors can be uniquely identified and corrected using the proposed method. The applicability of our results is demonstrated numerically by processing inter-agent distance measurements using a sequential convex programming (SCP) algorithm to identify the localization errors in a sensor network

Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems

The conventional solutions for fault-detection, identification, and reconstruction (FDIR) require centralized decision-making mechanisms which are typically combinatorial in their nature, necessitating the design of an efficient distributed FDIR mechanism that is suitable for multi-agent applications. To this end, we develop a general framework for efficiently reconstructing a sparse vector being observed over a sensor network via nonlinear measurements. The proposed framework is used to design a distributed multi-agent FDIR algorithm based on a combination of the sequential convex programming (SCP) and the alternating direction method of multipliers (ADMM) optimization approaches. The proposed distributed FDIR algorithm can process a variety of inter-agent measurements (including distances, bearings, relative velocities, and subtended angles between agents) to identify the faulty agents and recover their true states. The effectiveness of the proposed distributed multi-agent FDIR approach is demonstrated by considering a numerical example in which the inter-agent distances are used to identify the faulty agents in a multi-agent configuration, as well as reconstruct their error vectors

MASTAQ: A Middleware Architecture for Sensor Applications with Statistical Quality Constraints

We present the design goals and functional components of MASTAQ, a data management middleware for pervasive applications that utilize sensor data. MASTAQ allows applications to specify their quality-of information (QoI) preferences (in terms of statistical metrics over the data) independent of the underlying network topology. It then achieves energy efficiency by adaptively activating and querying only the subset of sensor nodes needed to meet the target QoI bounds. We also present a closed-loop feedback mechanism based on broadcasting of activation probabilities, which allows MASTAQ to activate the appropriate number of sensors without requiring any inter-sensor coordination or knowledge of the actual deployment.1

Correct-by-Construction Control Design for Mixed-Invariant Systems in Lie Groups

In this paper, we use the derivative of the exponential map to derive the exact evolution of the logarithm of the tracking error for mixed-invariant systems. Following correct-by-construction software paradigm, we propose an invariant control law for mixed-invariant systems, with application to Unmanned Aerial Systems (UASs), that is designed for efficient safety verification. We derive the nonlinear distortion matrix in the transformed differential equation in the Lie algebra and express the distortion matrix in a series form for any matrix Lie group and in a closed-form for the SE(2) Lie group. Given the input distortion, we employ dynamic inversion to linearize the evolution of error dynamics and apply a linear control strategy. We employ Linear Matrix Inequalities (LMIs) to bound the tracking error given a bounded disturbance amplified by the distortion matrix and leverage the tracking error bound to create flow pipes for the creation of a Polyhedral Invariant Hybrid Automaton (PIHA) model. We demonstrate the usefulness of our method by applying it to a simplified holonomic aircraft and nonholonomic rover with polynomial-based path planning methods.Comment: 15 pages, 19 figures. Submitted to IEEE TA

CoMon: Cooperative Ambience Monitoring Platform with Continuity and Benefit Awareness

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications

Toward a Mobile Platform for Pervasive Games

Emerging pervasive games will be immersed into real-life situations and leverage new types of contextual interactions therein. For instance, a player's punching gesture, running activity, and fast heart rate conditions can be used as the game inputs. Although the contextual interaction is the core building blocks of pervasive games, individual game developers hardly utilize a rich set of interactions within a game play. Most challenging, it is significantly difficult for developers to expect dynamic availability of input devices in real life, and adapt to the situation without system-level support. Also, it is challenging to coordinate its resource use with other gaming logics or applications. To address such challenges, we propose Player Space Director (PSD), a novel mobile platform for pervasive games. PSD facilitates the game developers to incorporate diverse contextual interactions in their game without considering complications in player's real-life situations, e.g., heterogeneity, dynamics or resource scarcity of input devices. We implemented the PSD prototype on mobile devices, diverse set of sensors, and actuators. On top of PSD, we developed three exploratory applications, ULifeAvatar, Swan Boat, U-Theater, and showed the effectiveness of PSD through extensive deployment of those games.