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 10

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.
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
Biologically Inspired Coalition Formation of Multi-Agent Systems

(Georgia Institute of Technology, 2010-05) Haque, Musad A. ; Rahmani, Amir R. ; Egerstedt, Magnus B.

We model the multi-level alliance forming ability of male bottlenose dolphins to develop a decentralized multi-level coalition formation algorithm for a multi-agent system. The goal is to produce a model that is rich enough to capture the biological phenomenon of forming alliances, yet remain simple so that it can be implemented on engineered systems, such as network of unmanned vehicles.
Optimal Motion Primitives for Multi-UAV Convoy Protection

(Georgia Institute of Technology, 2010-05) Rahmani, Amir R. ; Ding, Xu Chu ; Egerstedt, Magnus B.

In this paper we study the problem of controlling a number of Unmanned Aerial Vehicles (UAVs) to provide convoy protection to a group of ground vehicles. The UAVs are modeled as Dubins vehicles flying at a constant altitude with bounded turning radius. This paper first presents time-optimal paths for providing convoy protection to static ground vehicles. Then this paper addresses paths and control strategies to provide convoy protection to ground vehicles moving on a straight line. Minimum numbers of UAVs required to provide perpetual convoy protection for both cases are derived.
Optimal Multi-UAV Convoy Protection

(Georgia Institute of Technology, 2009-04) Ding, Xu Chu ; Rahmani, Amir R. ; Egerstedt, Magnus B.

In this paper, we study time-optimal trajectories for Unmanned Aerial Vehicles (UAVs) to provide convoy protection to a group of stationary ground vehicles. The UAVs are modelled as Dubins vehicles flying at a constant altitude. Due to kinematic constraints of the UAVs, it is not possible for a single UAV to provide convoy protection indefinitely. In this paper, we derive time-optimal paths for a single UAV to provide continuous ground convoy protection for the longest possible time. Furthermore, this paper provides optimal trajectories for multiple UAVs to achieve uninterrupted convoy protection. The minimum number of UAVs required to achieve this task is determined.