Person:

Egerstedt, Magnus B.

Permanent Link

https://hdl.handle.net/1853/72204

Associated Organization(s)

Organizational Unit

School of Electrical and Computer Engineering

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Now showing 1 - 10 of 16

Probabilistic Life Time Maximization of Sensor Networks

(Georgia Institute of Technology, 2013-02) Jaleel, Hassan ; Rahmani, Amir R. ; Egerstedt, Magnus B.

The design of power-aware lifetime maximization algorithms for sensor networks is an active area of research. However, the standard assumption is that the performance of the sensors remains the same throughout the network’s lifetime, which is not alwaystrue. In this paper, we study the effects of power decay on the performance of individual sensors as well as of the entire network. In particular, we examine networks with decaying footprints, akin to those of RF or radar-based sensors and relate the performance of a sensor to its available power. Moreover, we propose probabilistic scheduling controllers that compensate for the effects of the decrease in power while maintaining an adequate probability of event detection under two sensing models; Boolean and non-Boolean. We simulate the performance of the proposed controllers to establish that the desired performance levels are indeed maintained throughout the lifetime of the network.
Merging and Spacing of Heterogeneous Aircraft in Support of NextGen

(Georgia Institute of Technology, 2012-09) Chipalkatty, Rahul ; Twu, Philip Y. ; Rahmani, Amir R. ; Egerstedt, Magnus B.

FAA’s NextGen program aims to increase the capacity of the national airspace, while ensuring the safety of aircraft. This paper provides a distributed merging and spacing algorithm that maximizes the throughput at the terminal phase of flight, using infor- mation communicated between neighboring aircraft through the ADS-B framework. Aircraft belonging to a mixed fleet negotiate with each other and use dual decomposi- tion to reach an agreement on optimal merging times, with respect to a pairwise cost, while ensuring proper inter-aircraft spacing for the respective aircraft types. A set of sufficient conditions on the geometry and operating conditions of merging forks are provided to identify when proper inter-aircraft spacing can always be achieved using the proposed algorithm for any combination of merging aircraft. Also, optimal de- centralized controllers are derived for merging air traffic when operating under such conditions. The performance of the presented algorithm is verified through computer simulations.
Optimization of Foraging Multi-Agent System Front: A Flux-Based Curve Evolution Method

(Georgia Institute of Technology, 2011-12) Haque, Musad A. ; Rahmani, Amir R. ; Egerstedt, Magnus B. ; Yezzi, Anthony

Numerous social foragers form a foraging front that sweeps through the aggregation of prey. Based on this strategy, and using variational arguments, we develop an algorithm to provide a group-level specification of the shape of the sweeping front for a foraging multi-robot system. The presented flux-based algorithm has the desired property of generating more regular shapes than previously introduced algorithms.
Pilot decision aids for multi-vehicle ops

(Georgia Institute of Technology, 2011-09-01) Egerstedt, Magnus B. ; Chipalkatty, Rahul ; Twu, Philip Y. ; Rahmani, Amir R.
Biologically Motivated Shape Optimization of Foraging Fronts

(Georgia Institute of Technology, 2011-06) Haque, Musad A. ; Rahmani, Amir R. ; Egerstedt, Magnus B. ; Yezzi, Anthony

Social animals often form a predator front to charge through an aggregation of prey. It is observed that the nature of the feeding strategy dictates the geometric shape of these charging fronts. Inspired by this observation, we model foraging multi-robot fronts as a curve moving through a prey density. We optimize the shape of the curve using variational arguments and simulate the results to illustrate the operation of the proposed curve optimization algorithm.
Duty Cycle Scheduling in Dynamic Sensor Networks for Controlling Event Detection Probabilities

(Georgia Institute of Technology, 2011-06) Jaleel, Hassan ; Rahmani, Amir R. ; Egerstedt, Magnus B.

A sensor network comprising of RF or radar-based sensors has a deteriorating performance in that the effective sensor footprint shrinks as the power level decreases. Power is typically only drawn from the sensor nodes when they are turned on, and as a consequence, the power consumption can be controlled by controlling the duty cycle of the sensors. In this paper, we provide a probabilistic scheduling of the duty cycles in a sensor network deployed in an area of interest based on a Poisson distribution which ensures that a performance measure, e.g., the probability of event detection, is achieved throughout the lifetime of the network. Upper bounds on the performance of the network are given in terms of the decay rates, the spatial distribution intensity, and the desired performance of the network.
Geometric Foraging Strategies in Multi-Agent Systems Based on Biological Models

(Georgia Institute of Technology, 2010-12) Haque, Musad A. ; Rahmani, Amir R. ; Egerstedt, Magnus B.

In nature, communal hunting is often performed by predators by charging through an aggregation of prey. However, it has been noticed that variations exist in the geometric shape of the charging front; in addition, distinct differences arise between the shapes depending on the particulars of the feeding strategy. For example, each member of a dolphin foraging group must contribute to the hunt and will only be able to eat what it catches. On the other hand, some lions earn a "free lunch" by feigning help and later feasting on the prey caught by the more skilled hunters in the foraging group. We model the charging front of the predators as a curve moving through a prey density modeled as a reaction-diffusion process and we optimize the shape of the charging front in both the free lunch and no-free-lunch cases. These different situations are simulated under a number of varied types of predator-prey interaction models, and connections are made to multi-agent robot systems.
Distributed Scheduling for Air Traffic Throughput Maximization During the Terminal Phase of Flight

(Georgia Institute of Technology, 2010-12) Chipalkatty, Rahul ; Rahmani, Amir R. ; Egerstedt, Magnus B. ; Twu, Philip Y.

FAA’s NextGen program aims at increasing the capacity of the national airspace, while ensuring the safety of aircraft. This paper provides a distributed merging and spacing algorithm that maximizes the throughput at the terminal phase of flight using the information provided through the ADS-B framework. Using dual decomposition, aircraft negotiate with each other and reach an agreement on optimal merging times, with respect to an associated cost, that ensures proper inter-aircraft spacing. We provide a feasibility analysis that gives sufficient conditions to guarantee that proper spacing is achievable and derive maximum throughput controllers based on the air traffic characteristics of the merging flight paths
Air Traffic Maximization for the Terminal Phase of Flight Under FAA's NextGen Framework

(Georgia Institute of Technology, 2010-10) Chipalkatty, Rahul ; Rahmani, Amir R. ; Egerstedt, Magnus B. ; Young, R. ; Twu, Philip Y.

The NextGen program is the FAA's response to the ever increasing air traffic, that provides tools to increase the capacity of national airspace, while ensuring the safety of aircraft. In support of this vision, this paper provides a decentralized algorithm based on dual decomposition for safe merging and spacing of aircraft at the terminal phase of the flight. Aircraft negotiate optimal merging times that ensure safety, while penalizing deviations from the nominal path. We provide feasibility conditions for the safe merging of all incoming legs of flight and put the viability of the proposed algorithm to the test through simulations.
Dynamic Spectral Clustering

(Georgia Institute of Technology, 2010-07) LaViers, Amy ; Rahmani, Amir R. ; Egerstedt, Magnus B.

Clustering is a powerful tool for data classification; however, its application has been limited to analysis of static snapshots of data which may be time-evolving. This work presents a clustering algorithm that employs a fixed time interval and a time-aggregated similarity measure to determine classification. The fixed time interval and a weighting parameter are tuned to the system’s dynamics; otherwise the algorithm proceeds automatically finding the optimal cluster number and appropriate clusters at each time point in the dataset. The viability and contribution of the method is shown through simulation