Person:

Egerstedt, Magnus B.

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

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Organizational Unit

School of Electrical and Computer Engineering

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Now showing 1 - 10 of 34

Persistent Environmental Monitoring: Robots That Seemingly Do Nothing Most of the Time

(Georgia Institute of Technology, 2017-02-08) Egerstedt, Magnus B.

By now, we have a fairly good understanding of how to design coordinated control strategies for making teams of mobile robots achieve geometric objectives in a distributed manner, such as assembling shapes or covering areas. But, the mapping from high-level tasks to these geometric objectives is not at all straightforward. In this talk, we investigate this topic in the context of persistent autonomy, i.e., we consider teams of robots, deployed in an environment over a sustained period of time, that can be recruited to perform a number of different tasks in a distributed and safe, yet provably correct manner. This development will involve the composition of multiple barrier certificates for encoding the tasks and safety constraints, as well as a detour into ecology as a way of understanding how persistent environmental monitoring can be achieved by studying animals with low-energy lifestyles, such as the three-toed sloth.
Safety Barrier Certificates for Heterogeneous Multi-Robot Systems

(Georgia Institute of Technology, 2016-07) Wang, Li ; Ames, Aaron ; Egerstedt, Magnus B.

This paper presents a formal framework for collision avoidance in multi-robot systems, wherein an existing controller is modified in a minimally invasive fashion to ensure safety. We build this framework through the use of control barrier functions (CBFs) which guarantee forward invariance of a safe set; these yield safety barrier certificates in the context of heterogeneous robot dynamics subject to acceleration bounds. Moreover, safety barrier certificates are extended to a distributed control framework, wherein neighboring agent dynamics are unknown, through local parameter identification. The end result is an optimization-based controller that formally guarantees collision free behavior in heterogeneous multi-agent systems by minimally modifying the desired controller via safety barrier constraints. This formal result is verified in simulation on a multi-robot system consisting of both “sluggish” and “agile” robots.
Self-reconfigurable multi-robot systems

(Georgia Institute of Technology, 2016-04-12) Pickem, Daniel

Self-reconfigurable robotic systems are variable-morphology machines capable of changing their overall structure by rearranging the modules they are composed of. Individual modules are capable of connecting and disconnecting to and from one another, which allows the robot to adapt to changing environments. Optimally reconfiguring such systems is computationally prohibitive and thus in general self-reconfiguration approaches aim at approximating optimal solutions. Nonetheless, even for approximate solutions, centralized methods scale poorly in the number of modules. Therefore, the objective of this research is the development of decentralized self-reconfiguration methods for modular robotic systems. Building on completeness results of the centralized algorithms in this work, decentralized methods are developed that guarantee stochastic convergence to a given target shape. A game-theoretic approach lays the theoretical foundation of a novel potential game-based formulation of the self-reconfiguration problem. Furthermore, two extensions to the basic game-theoretic algorithm are proposed that enable agents to modify the algorithms' parameters during runtime and improve convergence times. The flexibility in the choice of utility functions together with runtime adaptability makes the presented approach and the underlying theory suitable for a range of problems that rely on decentralized local control to guarantee global, emerging properties. The experimental evaluation of the presented algorithms relies on a newly developed multi-robotic testbed called the "Robotarium" that is equipped with custom-designed miniature robots, the "GRITSBots". The Robotarium provides hardware validation of self-reconfiguration on robots but more importantly introduces a novel paradigm for remote accessibility of multi-agent testbeds with the goal of lowering the barrier to entrance into the field of multi-robot research and education.
A control theoretic perspective on learning in robotics

(Georgia Institute of Technology, 2015-12-16) O'Flaherty, Rowland Wilde

For robotic systems to continue to move towards ubiquity, robots need to be more autonomous. More autonomy dictates that robots need to be able to make better decisions. Control theory and machine learning are fields of robotics that focus on the decision making process. However, each of these fields implements decision making at different levels of abstraction and at different time scales. Control theory defines low-level decisions at high rates, while machine learning defines high-level decision at low rates. The objective of this research is to integrate tools from both machine leaning and control theory to solve higher dimensional, complex problems, and to optimize the decision making process. Throughout this research, multiple algorithms were created that use concepts from both control theory and machine learning, which provide new tools for robots to make better decisions. One algorithm enables a robot to learn how to optimally explore an unknown space, and autonomously decide when to explore for new information or exploit its current information. Another algorithm enables a robot to learn how to locomote with complex dynamics. These algorithms are evaluated both in simulation and on real robots. The results and analysis of these experiments are presented, which demonstrate the utility of the algorithms introduced in this work. Additionally, a new notion of “learnability” is introduced to define and determine when a given dynamical system has the ability to gain knowledge to optimize a given objective function.
Temporal Heterogeneity and the Value of Slowness in Robotic Systems

(Georgia Institute of Technology, 2015-12) Arkin, Ronald C. ; Egerstedt, Magnus B.

Robot teaming is a well-studied area, but little research to date has been conducted on the fundamental benefits of heterogeneous teams and virtually none on temporal heterogeneity, where timescales of the various platforms are radically different. This paper explores this aspect of robot ecosystems consisting of fast and slow robots (SlowBots) working together, including the bio-inspiration for such systems.
Spatio-temporal multi-robot routing

(Georgia Institute of Technology, 2015-04-02) Chopra, Smriti

We analyze spatio-temporal routing under various constraints specific to multi-robot applications. Spatio-temporal routing requires multiple robots to visit spatial locations at specified time instants, while optimizing certain criteria like the total distance traveled, or the total energy consumed. Such a spatio-temporal concept is intuitively demonstrable through music (e.g. a musician routes multiple fingers to play a series of notes on an instrument at specified time instants). As such, we showcase much of our work on routing through this medium. Particular to robotic applications, we analyze constraints like maximum velocities that the robots cannot exceed, and information-exchange networks that must remain connected. Furthermore, we consider a notion of heterogeneity where robots and spatial locations are associated with multiple skills, and a robot can visit a location only if it has at least one skill in common with the skill set of that location. To extend the scope of our work, we analyze spatio-temporal routing in the context of a distributed framework, and a dynamic environment.
Characterizing and facilitating human interactions with swarms of mobile robots

(Georgia Institute of Technology, 2015-02-20) De la Croix, Jean-Pierre

Since humans and robots often share workspaces and interact with each other to complete tasks cooperatively, as is the case, for example, in automated warehouses and assembly lines, much of the focus has been centered on supporting human interactions with one or a few robots. As the number of robots involved in a task grows large, scalable abstractions are needed to support interactions with larger numbers of robots. Consequently, there has been a growing effort to understand human-swarm interactions (HSIs) and devise abstractions that are amenable to having humans interact with swarms of robots easily and effectively. In this dissertation, we investigate what it means to impose a control structure on a swarm of robots for the purpose of supporting a specific HSI, when such a control structure is suitable for allowing a user to solve a particular task with a swarm of robots, how one can evaluate attention and effort required to interact with a swarm of robots through a particular control structure, how well attention and effort scale as the number of robots in the swarm increases, why some swarms of robots are easier to interact with than others under the same type of control structure, how to select an appropriate swarm size, and how to design new input controllers for interacting with swarm of mobile robots. Consequently, this dissertation provides a comprehensive framework for characterizing, understanding, and designing the control structures of new abstractions that will be amenable to humans interacting with swarms of networked mobile robots, as well as, a number of examples of such old and new abstractions investigated under this framework.
Control of Multi-Robot Systems

( 2014-10-01) Egerstedt, Magnus B.

The last few years have seen significant progress in our understanding of how one should structure multi-robot systems. New control, coordination, and communication strategies have emerged and, in this talk, we discuss some of these developments. In particular, we will show how one can go from global, geometric, team-level specifications to local coordination rules for achieving and maintaining formations, area coverage, and swarming behaviors. One aspect of this concerns how users can interact with networks of mobile robots in order to inject new, global information and objectives. We will also investigate what global objectives are fundamentally implementable in a distributed manner on a collection of spatially distributed and locally interacting agents.
Choreographic abstractions for style-based robotic motion

(Georgia Institute of Technology, 2013-05-16) LaViers, Amy

What does it mean to do the disco? Or perform a cheerleading routine? Or move in a style appropriate for a given mode of human interaction? Answering these questions requires an interpretation of what differentiates two distinct movement styles and a method for parsing this difference into quantitative parameters. Furthermore, such an understanding of principles of style has applications in control, robotics, and dance theory. This thesis present a definition for “style of motion” that is rooted in dance theory, a framework for stylistic motion generation that separates basic movement ordering from its precise trajectory, and an inverse optimal control method for extracting these stylistic parameters from real data. On the part of generation, the processes of sequencing and scaling are modulated by the stylistic parameters enumerated: an automation that lists basic primary movements, sets which determine the final structure of the state machine that encodes allowable sequences, and weights in an optimal control problem that generates motions of the desired quality. This generation framework is demonstrated on a humanoid robotic platform for two distinct case studies – disco dancing and cheerleading. In order to extract the parameters that comprise the stylistic definition put forth, two inverse optimal control problems are posed and solved -- one to classify individual movements and one to segment longer movement sequences into smaller motion primitives. The motion of a real human leg (recorded via motion capture) is classified in an example. Thus, the contents of the thesis comprise a tool to produce and understand stylistic motion.
Multi-robot platooning in hostile environments

(Georgia Institute of Technology, 2012-04-09) Shively, Jeremy

The purpose of this thesis is to develop a testing environment for mobile robot experiments, to examine methods for multi-robot platooning through hostile environments, and test these algorithms on mobile robots. Such a system will allow us to rapidly address and test problems that arise concerning robot swarms and consequent interactions. In order to create this hardware simulation environment a test bed will be created using ROS or Robot Operating System. This platform is highly modular and extensible for future development. Trajectory generation for the robots will use smoothing splines, B-splines, and A* search. Each method has distinct properties which will be analyzed and rated with respect to its effectiveness with regards to robotic platooning. A few issues to be considered include: Is the optimal path taken with respect to distance and threats? Is the formation of the robots maintained or compromised during traversal of the path? And finally, what sorts of compromises or additions are needed to make each method effective? This work will be helpful for choosing route planning methods in future work and will provide a large code base for rapid prototyping.