Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

Full item page

Publication Search Results

Now showing 1 - 10 of 17

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.
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.
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.
Generation and use of a discrete robotic controls alphabet for high-level tasks

(Georgia Institute of Technology, 2012-04-06) Gargas , Eugene Frank, III

The objective of this thesis is to generate a discrete alphabet of low-level robotic controllers rich enough to mimic the actions of high-level users using the robot for a specific task. This alphabet will be built through the analysis of various user data sets in a modified version of the motion description language, MDLe. It can then be used to mimic the actions of a future user attempting to perform the task by calling scaled versions of the controls in the alphabet, potentially reducing the amount of data required to be transmitted to the robot, with minimal error. In this thesis, theory is developed that will allow the construction of such an alphabet, as well as its use to mimic new actions. A MATLAB algorithm is then built to implement the theory. This is followed by an experiment in which various users drive a Khepera robot through different courses with a joystick. The thesis concludes by presenting results which suggest that a relatively small group of users can generate an alphabet capable of mimicking the actions of other users, while drastically reducing bandwidth.
Control of multi-agent networks: from network design to decentralized coordination

(Georgia Institute of Technology, 2012-04-04) Twu, Philip Y.

This dissertation presents a suite of design tools for multi-agent systems that address three main areas: network design, decentralized controller generation, and the synthesis of decentralized control strategies by combining individual decentralized controllers. First, a new metric for quantifying heterogeneity in multi-agent systems is presented based on combining different notions of entropy, and is shown to overcome the drawbacks associated with existing diversity metrics in various scientific fields. Moreover, a new method of controlling multi-agent networks through the single-leader network paradigm is presented where by directly exploiting the homogeneity of agent capabilities, a network which is not completely controllable can be driven closer to a desired target configuration than by using traditional control techniques. An algorithm is presented for generating decentralized control laws that allow for agents to best satisfy a desired global objective, while taking into account network topological constraints and limitations on how agents can compute their control signals. Then, a scripting tool is developed to aid in specifying sequences of decentralized controllers to be executed consecutively, while helping ensure that the required network topological requirements needed for each controller to execute properly are maintained throughout mode switches. Finally, the underlying concepts behind the developed tools are showcased in three example applications: distributed merging and spacing for heterogeneous aircraft during terminal approaches, collaborative multi-UAV convoy protection in dynamic environments, and an educational tool used to teach a graduate-level networked controls course at the Georgia Institute of Technology.
Multi-agent coordination: fluid-inspired and optimal control approaches

(Georgia Institute of Technology, 2012-04-03) Kingston, Peter

Multiagent coordination problems arise in a variety of applications, from satellite constellations and formation flight, to air traffic control and unmanned vehicle teams. We investigate the coordination of mobile agents using two kinds of approaches. In the first, which takes its inspiration from fluid dynamics and algebraic topology, control authority is split between mobile agents and a network of static infrastructure nodes - like wireless base stations or air traffic control towers - and controllers are developed that distribute their computation throughout this network. In the second, we look at networks of interconnected mechanical systems, and develop novel optimal control algorithms, which involve the computation of optimal deformations of time- and output- spaces, to achieve approximate formation tracking. Finally, we investigate algorithms that optimize these controllers to meet subjective criteria of humans.
Human-in-the-loop control for cooperative human-robot tasks

(Georgia Institute of Technology, 2012-03-29) Chipalkatty, Rahul

Even with the advance of autonomous robotics and automation, many automated tasks still require human intervention or guidance to mediate uncertainties in the environment or to execute the complexities of a task that autonomous robots are not yet equipped to handle. As such, robot controllers are needed that utilize the strengths of both autonomous agents, adept at handling lower level control tasks, and humans, superior at handling higher-level cognitive tasks. To address this need, we develop a control theoretic framework that seeks to incorporate user commands such that user intention is preserved while an automated task is carried out by the controller. This is a novel approach in that system theoretic tools allow for analytic guarantees of feasibility and convergence to goal states which naturally lead to varying levels of autonomy. We develop a model predictive controller that takes human input, infers human intent, then applies a control that minimizes deviations from the intended human control while ensuring that the lower-level automated task is being completed. This control framework is then evaluated in a human operator study involving a shared control task with human guidance of a mobile robot for navigation. These theoretical and experimental results lay the foundation for applying this control method for human-robot cooperative control to actual human-robot tasks. Specifically, the control is applied to a Urban Search and Rescue robot task where the shared control of a quadruped rescue robot is needed to ensure static stability during human-guided leg placements in uneven terrain. This control framework is also extended to a multiple user and multiple agent system where the human operators control multiple agents such that the agents maintain a formation while allowing the human operators to manipulate the shape of the formation. User studies are also conducted to evaluate the control in multiple operator scenarios.