Person:

Egerstedt, Magnus B.

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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 177

Local encounters in robot swarms: From localization to density regulation

(Georgia Institute of Technology, 2019-11-11) Mayya, Siddharth

In naturally occurring swarms---living as well as non-living---local proximity encounters among individuals or particles in the collective facilitate a broad range of emergent phenomena. In the context of robot swarms operating with limited sensing and communication capabilities, this thesis demonstrates how the systematic analysis of inter-robot encounters can enable the swarm to perform useful functions without the presence of a central coordinator. We combine ideas from stochastic geometry, statistical mechanics, and biology to develop mathematical models which characterize the nature and frequency of inter-robot encounters occurring in a robot swarm. These models allow the swarm to perform functions like localization, task allocation, and density regulation, while only requiring individual robots to measure the presence of other robots in the immediate vicinity---either via contact sensors or binary proximity detectors. Moreover, the resulting encounter-based algorithms require no communication among the robots or the presence of a central coordinator, and are robust to individual robot failures occurring in the swarm. Throughout the thesis, experiments conducted on real robot swarms vindicate the idea that inter-robot encounters can be advantageously leveraged by individuals in the swarm.
Specification composition and controller synthesis for robotic systems

(Georgia Institute of Technology, 2019-03-21) Glotfelter, Paul

From precision agriculture to autonomous-transportation systems, robotic systems have been proposed to accomplish a number of tasks. However, these systems typically require satisfaction of multiple constraints, such as safety or connectivity maintenance, while completing their primary objectives. The objective of this thesis is to endow robotic systems with a Boolean-composition and controller-synthesis framework for specifications of objectives and constraints. Barrier functions represent one method to enforce such constraints via forward set invariance, and Lyapunov functions offer a similar guarantee for set stability. This thesis focuses on building a system of Boolean logic for barrier and Lyapunov functions by using min and max operators. As these objects inherently introduce nonsmoothness, this thesis extends the theory on barrier functions to nonsmooth barrier functions and, subsequently, to controlled systems via control nonsmooth barrier functions. However, synthesizing controllers with respect to a nonsmooth function may create discontinuities; as such, this thesis develops a controller-synthesis framework that, despite creating discontinuities, still produces valid controllers (i.e., ones that satisfy the objectives and constraints). These developments have been successfully applied to a variety of robotic systems, including remotely accessible testbeds, autonomous-transportation scenarios, and leader-follower systems.
Power-aware hybrid-dynamical approach to coverage control in multi-robot systems

(Georgia Institute of Technology, 2018-04-24) Olsen, Mark Ryan

This thesis develops an algorithm which allows robots in a multi-robot team to optimize for battery power while performing coverage control so as to maximize the mission life of the multi-robot team. We envision a scenario where robots with limited battery supply are executing the well known Lloyd's algorithm in order to effectively cover a certain region. We perform a trade-off between the distance of a robot from the centroid of its Voronoi cell, and the energy required to traverse that distance. In order to execute this trade-off two different strategies are presented -- in one case, the reduction in cost due to coverage is compared against the energy required to traverse the distance to the centroid, and using a user-defined threshold, the decision is made. Then, a more sophisticated algorithm is used to perform the trade-off where the robots solves a switch-time optimization problem to decide whether it should move or it should stay.
A control theoretic perspective on social networks

(Georgia Institute of Technology, 2018-04-05) Ruf, Sebastian Felix

This thesis discusses the application of control theory to the study of complex networks, drawing inspiration from the behavior of social networks. There are three topic areas covered by the thesis. The first area considers the ability to control a dynamical system which evolves over a network. Specifically, this thesis introduces a network controllability notion known as herdability. Herdability quantifies the ability to encourage general behavioral change in a system via a set-based reachability condition, which describes a class of desirable behaviors for the application of control in a social network setting. The notion is closely related to the classical notion of controllability, however ensuring complete controllability of large complex networks is often unnecessary for certain beneficial behaviors to be achieved. The basic theory of herdability is developed in this thesis. The second area of study, which builds directly on the first, is the application of herdability to the study of complex networks. Specifically, this thesis explores how to make a network herdable, an extension of the input selection problem which is often discussed in the context of controllability. The input selection problem in this case considers which nodes to select to ensure the maximal number of nodes in the system are herdable. When there are multiple single node sets which can be used to make a system completely herdable, a herdability centrality measure is introduced to differentiate between them. The herdability centrality measure, a measure of importance with respect to the ability to herd the network with minimum energy, is compared to existing centrality measures. The third area explores modeling the spread of the adoption of a beneficial behavior or an idea, in which the spread is encouraged by the action of a social network. A novel model of awareness-coupled epidemic spread is introduced, where agents in a network are aware of a virus (here representing something which should be spread) moving through the network. If the agents have a high opinion of the virus, they are more likely to adopt it. The behavior of this viral model is considered both analytically and in simulation.
Multi-robot coordination and safe learning using barrier certificates

(Georgia Institute of Technology, 2018-04-03) Wang, Li

The objective of this research is to develop a formal safety framework for collision-free and connectivity sustained motion in multi-robot coordination and learning based control. This safety framework is designed with barrier certificates, which provably guarantee the safety of dynamical systems based on the set invariance principle. The barrier certificates are enforced on the system using an online optimization-based controller such that minimal changes to the existing control strategies are required to guarantee safety. The proposed safety barrier certificates are validated on real multi-robot systems consisting of multiple Khepera robots, Magellan Pro robot, GRITS-Bots, and Crazyflie quadrotors.
Mixed centralized/decentralized coordination protocols for multi-agent systems

(Georgia Institute of Technology, 2017-04-10) Hale, Matthew Thomas

This thesis uses a mixture of centralized and decentralized architectures and algorithms to develop coordination strategies for multi-agent systems. Conventionally, centralized and decentralized methods are viewed as belonging to distinct paradigms, each with its own features and drawbacks, and multi-agent coordination algorithms are typically classified as being exclusively one or the other. However, emerging technologies such as cloud computing make it feasible to incorporate some centralization into an otherwise decentralized system, and one may ask how to embrace this mix of centralized and decentralized information that is rapidly being integrated into various systems such as the smart power grid, swarms of robots, and cyber-physical systems. To address this question, two problem domains are considered. The first is that of asynchronous coordination, in which agents generate and share information with arbitrary timing. The second concerns private coordination, in which teams of agents must work together without revealing sensitive information. In both cases, mixing centralized and decentralized information enables successful coordination despite the challenges imposed by asynchrony and privacy, and theoretical performance guarantees are derived for each algorithm that is developed. Complementing these theoretical developments, robotic experiments are included that demonstrate the utility of these algorithms in practice.
Psychologically consistent coordinated control of multi-agent teams

(Georgia Institute of Technology, 2017-03-31) Setter, Tina M.

The objective of this research is to describe both human-robot interactions and inter-robot interactions and analyze the behavior of the resulting multi-agent systems, while drawing comparisons to psychological studies regarding human team behavior. In particular, we look at the effects of trust, energy, and manipulability on these interactions. We first address the problem of modeling trust evolution and describing how it affects the states of agents in a system - whether they be human or robot. We introduce two different types of trust models - self-centered and team-oriented - and show, through simulations and theoretical analyses, under what initial trust conditions these systems achieve their objectives. We show our models to be psychologically consistent in that they exhibit group polarization, belief polarization, and a positive trust-performance correlation. In the second part of this work, we look at the effect of energy on inter-robot interactions by solving an energy-constrained coordination problem in which robots must determine where and when to meet given differing initial battery levels to do so in the least amount of time. This is formulated as a constrained optimization problem where the constraints arise from solving for a single agent's optimal control input. Lastly, we address the effect of manipulability on human-robot interactions through a haptic human-swarm interaction user study. Manipulability, a notion describing how effective a leader robot is at controlling the follower robots, is provided as force feedback on a haptic joystick that a human operator uses to control a swarm of robots. Ten subjects complete the experiment in which they move the group of robots through a series of waypoints and different mappings between manipulability and the haptic feedback force are investigated.
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.
Assignment and pursuit in temporally heterogeneous robotic teams

(Georgia Institute of Technology, 2016-08-01) Oei, Marius Florian Bruno

Traditionally, robotic systems are built to move as fast as possible. In contrast to this, we investigate slowness and its effects on heterogeneous robotic teams inspired by biological systems. An assignment problem for static targets and a team pursuit problem for heterogeneous evaders are addressed. The value of slowness in solving these problems optimally is examined. We further assemble the optimal teams for given problems by finding a compromise between performance and energy consumption or monetary cost. The results are validated in simulation and implemented on a robotic testbed.
Optimal control of constrained hybrid dynamical systems: Theory, computation and applications

(Georgia Institute of Technology, 2016-07-29) Ali, Usman

Hybrid dynamical systems arise in a number of application areas such as power converters, autopilots, manufacturing, process control, hybrid cars, mobile and humanoid robotics etc., to name a few and as such the optimal control of these systems has been an area of active research. These systems are characterized by two components: subsystems (modes) with continuous or discrete dynamics and a switching law which determines which of these subsystems is active at a given time. While in theory, we can switch infinitely many times between different modes in a finite amount of time, physical systems need to spend some minimum time in a mode before they can switch to another mode due to mechanical reasons, power constraints, information delays, stability considerations etc and must spend some minimum amount of time in a mode before they can switch to another mode. This minimum time is known as the dwell time, a term first used in the context of stability of hybrid systems, and the optimal control of hybrid systems under these constraints is the main focus of this thesis. The presence of the dwell time constraints raises interesting theoretical and computational questions which are addressed in thesis. We consider the general hybrid optimal control problem subject to dwell time constraints thereby establishing necessary conditions for optimality and develop numerical schemes to compute solutions to these problems and prove their convergence. Any physical system that switches is subject to dwell time constraints, small or large, and thus amenable to our framework. To demonstrate, however, the generality and thus wide applicability of our results, we consider the application to an interesting problem in Precision Agriculture, namely the problem of optimal pesticide scheduling and present a case study to demonstrate the application of our methodology. In this thesis, we also consider a class of constrained hybrid optimal control problems inspired by problems in power aware mobile robotic networks that are subject to various constraints on inputs and states. In particular, we consider the problem of jointly minimizing motion and communication energy in power aware mobile robotic networks required to perform various co-ordinated tasks such as the transmission of given amount of data to a remote base station under time and resource constraints and where the robot decision variables are acceleration (continuous), for controlling the motion of the robot and spectral efficiency (discrete), catering to data transmission requirements. Framing this co-optimization problem as a constrained hybrid optimal control problem in the general setting and subsequently solving it using efficient algorithms is another main topic of this thesis. This problem, like any other hybrid optimal control problem, is also subject to dwell time constraints, signifying the importance of the dwell time problem addressed in this thesis. We present numerous application scenarios to demonstrate the utility of our framework. Finally, we propose a multiple shooting based gradient descent techniques to solve a class of complex optimal and hybrid control problems with large time horizons, which otherwise are hard to solve due to numerical problems arising from instability issues associated with the state or co-state equation. The two point boundary problem resulting from solving the optimal or hybrid optimal control problem is transformed into an equivalent optimal control problem over extended states comprising of the original state equation and the costate equation and then solved. Again, the results here are general and we demonstrate the effectiveness of our method by considering its application to solving large multi-agent co-optimization problem in power aware mobile robotic networks.