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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Publication Search Results

Now showing 1 - 10 of 27

Shortest Paths Through 3-Dimensional Cluttered Environments

(Georgia Institute of Technology, 2014-06) Lu, Jun ; Diaz-Mercado, Yancy ; Egerstedt, Magnus B. ; Zhou, Haomin ; Chow, Shui-Nee

This paper investigates the problem of finding shortest paths through 3-dimensional cluttered environments. In particular, an algorithm is presented that determines the shortest path between two points in an environment with obstacles which can be implemented on robots with capabilities of detecting obstacles in the environment. As knowledge of the environment is increasing while the vehicle moves around, the algorithm provides not only the global minimizer – or shortest path – with increasing probability as time goes by, but also provides a series of local minimizers. The feasibility of the algorithm is demonstrated on a quadrotor robot flying in an environment with obstacles.
A Measure of Heterogeneity in Multi-Agent Systems

(Georgia Institute of Technology, 2014-06) Twu, Philip ; Mostofi, Yasamin ; Egerstedt, Magnus B.

Heterogeneous multi-agent systems have previously been studied and deployed to solve a number of different tasks. Despite this, we still lack a basic understanding of just what “heterogeneity” really is. For example, what makes one team of agents more heterogeneous than another? In this paper, we address this issue by proposing a measure of heterogeneity. This measure takes both the complexity and disparity of a system into account by combining different notions of entropy. The result is a formulation that is both easily computable and makes intuitive sense. An overview is given of existing metrics for diversity found in various fields such as biology, economics, as well as robotics, followed by a discussion of their relative merits and demerits. We show how our proposed measure of heterogeneity overcomes problematic issues identified across the previous metrics. Finally, we discuss how to apply the new measure of heterogeneity specifically to multi-agent systems by using the notion of a common task-space to compare agents with different capabilities.
Style-based Abstractions for Human Motion Classification

(Georgia Institute of Technology, 2014-04) LaViers, Amy ; Egerstedt, Magnus B.

This paper presents an approach to motion analysis for robotics in which a quantitative definition of "style of motion" is used to classify movements. In particular, we present a method for generating a "best match" signal for empirical data via a two stage optimal control formulation. The first stage consists of the generation of trajectories that mimic empirical data. In the second stage, an inverse problem is solved in order to obtain the "stylistic parameters" that best recreate the empirical data. This method is amenable to human motion analysis in that it not only produces a matching trajectory but, in doing so, classifies its quality. This classification allows for the production of additional trajectories, between any two endpoints, in the same style as the empirical reference data. The method not only enables robotic mimicry of human style but can also provide insights into genres of stylized movement, equipping cyberphysical systems with a deeper interpretation of human movement.
Manipulability of Leader-Follower Networks with the Rigid-Link Approximation

(Georgia Institute of Technology, 2014-03) Kawashima, Hiroaki ; Egerstedt, Magnus B.

This paper introduces the notion of manipulability to mobile, multi-agent networks as a tool to analyze the instantaneous effectiveness of injecting control inputs at certain, so-called leader nodes in the network. Effectiveness is interpreted to characterize how the movements of the leader nodes translate into responses among the remaining follower nodes. This notion of effectiveness is a function of the interaction topologies, the agent configurations, and the particular choice of inputs used to influence the network. In fact, classic manipulability is an index used in robotics to analyze the singularity and efficiency of configurations of robot-arm manipulators. To define similar notions for leader-follower networks, we use a rigid-link approximation of the follower dynamics and, under this assumption, we prove that the instantaneous follower velocities can be uniquely determined from that of the leaders’, which allows us to define a meaningful and computable manipulability index for the leader-follower networks. This paper examines the property of the proposed index in simulation and with real mobile robots, and demonstrates how the index can be used to find effective interaction topologies.
Multi-Robot Mixing Using Braids

(Georgia Institute of Technology, 2013-12) Diaz-Mercado, Yancy ; Egerstedt, Magnus B.

This paper presents a method for automatically achieving multi-robot mixing in the sense that the robots follow predefined paths in a somewhat loose sense while ensuring that their actual movements are rich enough. In particular, we focus on the mixing problem, where the robots have to interweave their movements, for example to ensure sufficiently rich pairwise interactions or to cover an area along the path. By formally specifying mixing levels through strings over the Braid Group, the resulting hybrid system can execute a geometric interpretation of these strings, where the level of mixing is dictated by the string length. The feasibility of the proposed approach is illustrated on a particular class of multi-robot systems that cooperatively have to achieve the desired mixing levels.
Energy-Efficient Data Collection in Heterogeneous Wireless Sensor and Actor Networks

(Georgia Institute of Technology, 2013-12) Abbas, Waseem ; Jaleel, Hassan ; Egerstedt, Magnus B.

In this paper, we address the issue of activity scheduling of sensors in heterogeneous wireless sensor and actor networks (WSANs), thereby proposing an energy-efficient data collection scheme in such networks. In order to extend the lifetime of heterogeneous WSANs, sensors are activated and deactivated under certain constraints throughout the network operations. Here, we propose a coordination framework in which actors exchange information with each other and decide about the availability of redundant sensors that are eventually deactivated to save energy. In particular, let there be r different types of sensors with each sensor observing a particular sensing parameter. Under the initial deployment of sensors and actors within some field of observation, if an actor v receives information regarding k different sensing parameters, either directly from sensors or through other actors, then our scheme determines a small subset of sensors that are sufficient to provide information regarding the same k sensing parameters to v.
Decentralized Degree Regularization for Multi-Agent Networks

(Georgia Institute of Technology, 2013-12) Yazıcıoğlu, A. Yasin ; Egerstedt, Magnus B. ; Shamma, Jeff

Networked multi-agent systems are widely modeled as graphs where the agents are represented as nodes and edges exist between the agents that interact directly. In this setting, the degree of a node is the number of edges incident to it. For such systems, degree regularity (uniformity of degree across the nodes) typically provides desirable properties such as robustness and fast mixing time. As such, a key task is to achieve degree regularization in a decentralized manner. In this paper, we present a locally applicable rule that achieves this task. For any connected initial graph, the proposed reconfiguration rule preserves the graph connectivity and the total number of edges in the system while minimizing the difference between the maximum and the minimum node degrees.
Deformable-Medium Affordances for Interacting with Multi-Robot Systems

(Georgia Institute of Technology, 2013-11) Diana, Matteo ; de la Croix, Jean-Pierre ; Egerstedt, Magnus B.

This paper addresses the issue of human-swarm interactions by proposing a new set of affordances that make a multi-robot system amenable to human control. In particular, we propose to use clay – a deformable medium – as the “joy- stick” for controlling the swarm, supporting such affordances as stretching, splitting and merging, shaping, and mixing. The contribution beyond the formulation of these affordances is the coupling of an image recognition framework to decentralized control laws for the individual robots, and the developed human-swarm interaction methodology is applied to a team of mobile robots.
Optimal Trajectory Generation for Next Generation Flight Management Systems

(Georgia Institute of Technology, 2013-10) Diaz-Mercado, Yancy ; Lee, Sung G. ; Egerstedt, Magnus B. ; Young, Shih-Yih

Next generation flight management systems require the compliance of temporal and spatial constraints on navigation performance. The problem of generating fuel-efficient trajectories for aircrafts that comply with the required navigation performance is approached from an optimal control framework. By deriving the necessary conditions for optimality, nominal optimal control signals can be generated in a computationally efficient manner. These control signals can be used to generate nominal trajectories for an aircraft model. Using the nominal control and nominal trajectory, a feedforward-feedback control scheme can be implemented to robustify the system’s response in the presence of uncertainty and disturbances to still achieve the required navigation performance. The feasibility of the approach is demonstrated through simulation and Monte Carlo runs.
A Separation Signal for Heterogeneous Networks

(Georgia Institute of Technology, 2013-10) de la Croix, Jean-Pierre ; Egerstedt, Magnus B.

Organizing a large-scale, heterogeneous network of agents into clusters based on the agents’ class is a useful preprocessing step for cooperative tasks, where agents with the same capabilities need to be in the same location to cooperatively solve a task. In this paper, we investigate whether it is possible to apply an exogenous control signal to a heterogeneous network of agents, such that these agents form clusters of the same class of agents. We demonstrate that if each agent belongs to one of M “weight” classes and executes a weighted, forced agreement protocol, then it is possible to apply an external input signal that separates the network into M clusters corresponding to the M classes of agents.