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Toward an autonomous aerial survey and planning system for humanitarian aid and disaster response

March 7, 2020
  |
Conference Paper
Author:
Ross E. Allen
…
Published in:
IEEE Aerospace Conf., 7-14 March 2020.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

In this paper we propose an integrated system concept for autonomously surveying and planning emergency response for areas impacted by natural disasters. Referred to as AASAPS-HADR, this system is composed of a network of ground stations and autonomous aerial vehicles interconnected by an ad hoc emergency communication network. The system objectives are three-fold: to provide situational awareness of the evolving disaster event, to generate dispatch and routing plans for emergency vehicles, and to provide continuous communication networks which augment pre-existing communication infrastructure that may have been damaged or destroyed. Lacking development in previous literature, we give particular emphasis to the situational awareness objective of disaster response by proposing an autonomous aerial survey that is tasked with assessing damage to existing road networks, detecting and locating human victims, and providing a cursory assessment of casualty types that can be used to inform medical response priorities. In this paper we provide a high-level system design concept, identify existing AI perception and planning algorithms that most closely suit our purposes as well as technology gaps within those algorithms, and provide initial experimental results for non-contact health monitoring using real-time pose recognition algorithms running on a Nvidia Jetson TX2 mounted on board a quadrotor UAV. Finally we provide technology development recommendations for future phases of the AASAPS-HADR system.
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Summary

In this paper we propose an integrated system concept for autonomously surveying and planning emergency response for areas impacted by natural disasters. Referred to as AASAPS-HADR, this system is composed of a network of ground stations and autonomous aerial vehicles interconnected by an ad hoc emergency communication network. The system...

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Toward an autonomous aerial survey and planning system for humanitarian aid and disaster response

Automated discovery of cross-plane event-based vulnerabilities in software-defined networking

February 23, 2020
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Conference Paper
Author:
Benjamin E. Ujcich
…
Published in:
Network and Distributed Systems Security Symp., NDSS, 23-26 February 2020.
Topic:
cyber security
R&D area:
R&D group:

Summary

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the control plane towards unwanted states that bypass network security restrictions (i.e., cross-plane attacks). Unfortunately, because of the complex interplay among controllers, apps, and data plane inputs, at present it is difficult to systematically identify and analyze these cross-plane vulnerabilities. We present EVENTSCOPE, a vulnerability detection tool that automatically analyzes SDN control plane event usage, discovers candidate vulnerabilities based on missing event-handling routines, and validates vulnerabilities based on data plane effects. To accurately detect missing event handlers without ground truth or developer aid, we cluster apps according to similar event usage and mark inconsistencies as candidates. We create an event flow graph to observe a global view of events and control flows within the control plane and use it to validate vulnerabilities that affect the data plane. We applied EVENTSCOPE to the ONOS SDN controller and uncovered 14 new vulnerabilities.
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Summary

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the...

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Automated discovery of cross-plane event-based vulnerabilities in software-defined networking

Unified value-based feedback, optimization and risk management in complex electric energy systems

February 22, 2020
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Journal Article
Author:
Marija Ilic
…
Published in:
Optim Eng 21, 427–483 (2020)
Topic:
energy systems
R&D area:
R&D group:

Summary

The ideas in this paper are motivated by an increased need for systematic data-enabled resource management of large-scale electric energy systems. The basic control objective is to manage uncertain disturbances, power imbalances in particular, by optimizing available power resources. To that end, we start with a centralized optimal control problem formulation of system-level performance objective subject to complex interconnection constraints and constraints representing highly heterogeneous internal dynamics of system components. To manage spatial complexity, an inherent multi-layered structure is utilized by modeling interconnection constraints in terms of unifed power variables and their dynamics. Similarly, the internal dynamics of components and sub-systems (modules), including their primary automated feedback control, is modeled so that their input–output characterization is also expressed in terms of power variables. This representation is shown to be key to managing the multi-spatial complexity of the problem. In this unifying energy/ power state space, the system constraints are all fundamentally convex, resulting in the convex dynamic optimization problem, for typically utilized quadratic cost functions. Based on this, an interactive multi-layered modeling and control method is introduced. While the approach is fundamentally based on the primal–dual decomposition of the centralized problem, this is formulated for the frst time for the couple real-reactive power problem. It is also is proposed for the frst time to utilize sensitivity functions of distributed agents for solving the primal distributed problem. Iterative communication complexity typically required for convergence of pointwise information exchange is replaced by the embedded distributed optimization by the modules when creating these functions. A theoretical proof of the convergence claim is given. Notably, the inherent multi-temporal complexity is managed by performing model predictive control (MPC)-based decision making when solving distributed primal problems. The formulation enables distributed decision-makers to value uncertainties and related risks according to their preferences. Ultimately, the distributed decision making results in creating a bid function to be used at the coordinating market-clearing level. The optimization approach in this paper provides a theoretical foundation for next-generation Supervisory Control and Data Acquisition (SCADA) in support of a Dynamic Monitoring and Decision Systems (DyMonDS) for a multi-layered interactive market implementation in which the grid users follow their sub-objectives and the higher layers coordinate interconnected sub-systems and the high-level system objectives. This forms a theoretically sound basis for designing IT-enabled protocols for secure operations, planning, and markets.
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Summary

The ideas in this paper are motivated by an increased need for systematic data-enabled resource management of large-scale electric energy systems. The basic control objective is to manage uncertain disturbances, power imbalances in particular, by optimizing available power resources. To that end, we start with a centralized optimal control problem...

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Unified value-based feedback, optimization and risk management in complex electric energy systems

Wind information requirements for NextGen applications phase 7 report

February 12, 2020
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Project Report
Author:
Christopher Edwards
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Published in:
MIT Lincoln Laboratory Report ATC-444
Topic:
air traffic control
R&D area:
R&D group:

Summary

This report details the Required Time of Arrival (RTA) performance of B757 aircraft arriving at various meter fixes across a range of altitudes from 33,000' down to 3,000' above ground level (AGL). The system tested demonstrated less than ±10 second arrival error in at least 95% of flights at meter fixes down to 7,000' AGL regardless of the forecast quality provided. Below 7,000' AGL, RTA performance significantly degraded demonstrating around 80% compliance under the best forecast and operating conditions. This report also provides a comprehensive lexicon of aviation and air traffic control related "wind" terms.
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Summary

This report details the Required Time of Arrival (RTA) performance of B757 aircraft arriving at various meter fixes across a range of altitudes from 33,000' down to 3,000' above ground level (AGL). The system tested demonstrated less than ±10 second arrival error in at least 95% of flights at meter...

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Wind information requirements for NextGen applications phase 7 report

Safe predictors for enforcing input-output specifications [e-print]

January 29, 2020
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Journal Article
Author:
Stephen Mell
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Published in:
https://arxiv.org/abs/2001.11062
Topic:
machine learning
R&D area:
R&D group:

Summary

We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compatible constraints, and combining them safely via a convex combination of their predictions. We demonstrate our approach on synthetic datasets and an aircraft collision avoidance problem.
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Summary

We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compatible constraints, and combining them safely via...

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Safe predictors for enforcing input-output specifications [e-print]

AI data wrangling with associative arrays [e-print]

January 18, 2020
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Abstract
Author:
Jeremy Kepner
…
Published in:
Submitted to Northeast Database Day, NEDB 2020, https://arxiv.org/abs/2001.06731
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical representations of these data enables data translation and analysis optimization within and across steps. Associative array algebra provides a mathematical foundation that naturally describes the tabular structures and set mathematics that are the basis of databases. Likewise, the matrix operations and corresponding inference/training calculations used by neural networks are also well described by associative arrays. More surprisingly, a general denormalized form of hierarchical formats, such as XML and JSON, can be readily constructed. Finally, pivot tables, which are among the most widely used data analysis tools, naturally emerge from associative array constructors. A common foundation in associative arrays provides interoperability guarantees, proving that their operations are linear systems with rigorous mathematical properties, such as, associativity, commutativity, and distributivity that are critical to reordering optimizations.
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Summary

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical...

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AI data wrangling with associative arrays [e-print]

Beamforming with distributed arrays: FY19 RF Systems Line-Supported Program

January 7, 2020
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Project Report
Author:
Keith W. Forsythe
…
Published in:
MIT Lincoln Laboratory Report LSP-270
Topic:
antennas
R&D area:
R&D group:

Summary

Spatial beamforming using distributed arrays of RF sensors is treated. Unlike the observations from traditional RF antenna arrays, the distributed array's data can be subjected to widely varying time and frequency shifts among sensors and signals. These shifts require compensation upon reception in order to perform spatial filtering. To perform beamforming with a distributed array, the complex-valued observations from the sensors are shifted in time and frequency, weighted, and summed to form a beamformer output that is designed to mitigate interference and enhance signal energy. The appropriate time-frequency shifts required for good beamforming are studied here using several different methodologies.
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Summary

Spatial beamforming using distributed arrays of RF sensors is treated. Unlike the observations from traditional RF antenna arrays, the distributed array's data can be subjected to widely varying time and frequency shifts among sensors and signals. These shifts require compensation upon reception in order to perform spatial filtering. To perform...

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Beamforming with distributed arrays: FY19 RF Systems Line-Supported Program

Evaluating collision avoidance for small UAS using ACAS X

January 6, 2020
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Conference Paper
Author:
John Deaton
…
Published in:
AIAA SciTech Forum, 6-10 January 2020.
R&D area:
R&D group:

Summary

Small Unmanned Aircraft Systems (sUAS) offer many potential benefits to society but also pose a dangerous mid-air collision hazard. Safely integrating into shared airspace will require sUAS to perform Collision Avoidance (CA), one of the primary components of Detect and Avoid (DAA) technologies. This paper performs a Monte Carlo simulation of close encounters between sUAS and manned aircraft to evaluate the safety and alerting rates of three CA system architecture options: manned aircraft avoiding sUAS, sUAS avoiding manned aircraft, and both types of aircraft avoiding each other. Novel CA policies based on ACAS X are introduced for sUAS. These policies enable sUAS to perform escape maneuvers with far lower vertical climb capabilities than what is expected by current CA systems.
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Summary

Small Unmanned Aircraft Systems (sUAS) offer many potential benefits to society but also pose a dangerous mid-air collision hazard. Safely integrating into shared airspace will require sUAS to perform Collision Avoidance (CA), one of the primary components of Detect and Avoid (DAA) technologies. This paper performs a Monte Carlo simulation...

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Evaluating collision avoidance for small UAS using ACAS X

Representative small UAS trajectories for encounter modeling

January 6, 2020
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Conference Paper
Author:
Andrew J. Weinert
…
Published in:
AIAA SciTech Forum, 6-10 January 2020.
R&D area:
R&D group:

Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We have previously demonstrated a methodology for developing small unmanned aircraft system (sUAS) flight models that leverage open source geospatial information and map datasets to generate representative unmanned operations at low altitudes. This work expands upon previous research by evaluating the scalability and diversity of open source data to support currently needed risk assessments. We also provide considerations for pairing these trajectories with generative manned aircraft models to create encounters for Monte Carlo simulations.
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Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo...

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Representative small UAS trajectories for encounter modeling

Modeling and distributed control of microgrids: a negative feedback approach

December 11, 2019
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Conference Paper
Author:
null
…
Published in:
2019 IEEE 58th Conf. on Decision and Control, CDC, 11-13 December 2019.
Topic:
microgrids
R&D area:
R&D group:

Summary

In this paper, we first show how general microgrid can be modeled as a negative feedback configuration comprising two subsystems. The first subsystem is the interconnected microgrid grid which is affected through negative feedback by the second subsystem consisting of all single-port components. This is modeled by transforming physical state variables into energy state variables and by systematically defining input and output of system components in this transformed state space. We next draw on the fact that for this basic feedback configuration there exist several types of conditions regarding subsystem properties which ensure overall system properties. In particular, we utilize dissipativity theory to propose a subsystem nonlinear control design for heterogeneous resource components comprising microgrids so that they jointly result in a closed-loop feasible and stable dynamical system for given ranges of system disturbances.
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Summary

In this paper, we first show how general microgrid can be modeled as a negative feedback configuration comprising two subsystems. The first subsystem is the interconnected microgrid grid which is affected through negative feedback by the second subsystem consisting of all single-port components. This is modeled by transforming physical state...

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Modeling and distributed control of microgrids: a negative feedback approach
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