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- Genre: Academic theses
- Creators: Arizona State University
- Creators: Das, Jnaneshwar
Description
Robotic technology is advancing to the point where it will soon be feasible to deploy massive populations, or swarms, of low-cost autonomous robots to collectively perform tasks over large domains and time scales. Many of these tasks will require the robots to allocate themselves around the boundaries of regions or features of interest and achieve target objectives that derive from their resulting spatial configurations, such as forming a connected communication network or acquiring sensor data around the entire boundary. We refer to this spatial allocation problem as boundary coverage. Possible swarm tasks that will involve boundary coverage include cooperative load manipulation for applications in construction, manufacturing, and disaster response.
In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations.
Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case.
I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication.
In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations.
Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case.
I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication.
ContributorsPeruvemba Kumar, Ganesh (Author) / Berman, Spring M (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Bazzi, Rida (Committee member) / Syrotiuk, Violet (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
Technological progress in robot sensing, design, and fabrication, and the availability of open source software frameworks such as the Robot Operating System (ROS), are advancing the applications of swarm robotics from toy problems to real-world tasks such as surveillance, precision agriculture, search-and-rescue, and infrastructure inspection. These applications will require the development of robot controllers and system architectures that scale well with the number of robots and that are robust to robot errors and failures. To achieve this, one approach is to design decentralized robot control policies that require only local sensing and local, ad-hoc communication. In particular, stochastic control policies can be designed that are agnostic to individual robot identities and do not require a priori information about the environment or sophisticated computation, sensing, navigation, or communication capabilities. This dissertation presents novel swarm control strategies with these properties for detecting and mapping static targets, which represent features of interest, in an
unknown, bounded, obstacle-free environment. The robots move on a finite spatial grid according to
the time-homogeneous transition probabilities of a Discrete-Time Discrete-State (DTDS) Markov chain model, and they exchange information with other robots within their communication range using a consensus (agreement) protocol. This dissertation extend theoretical guarantees on multi-robot consensus over fixed and time-varying communication networks with known connectivity properties to consensus over the networks that have Markovian switching dynamics and no presumed connectivity. This dissertation develops such swarm consensus strategies for detecting a single feature in the environment, tracking multiple features, and reconstructing a discrete distribution of features modeled as an occupancy grid map.
The proposed consensus approaches are validated in numerical simulations and in 3D physics-based simulations of quadrotors in Gazebo. The scalability of the proposed approaches is examined through extensive numerical simulation studies over different swarm populations and environment sizes.
ContributorsShirsat, Aniket (Author) / Berman, Spring (Thesis advisor) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Saripalli, Srikanth (Committee member) / Gharavi, Lance (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation presents a comprehensive study of modeling and control issues associated with nonholonomic differential drive mobile robots. The first part of dissertation focuses on modeling using Lagrangian mechanics. The dynamics is modeled as a two-input two-output (TITO) nonlinear model. Motor dynamics are also modeled. Trade studies are conducted to shed light on critical vehicle design parameters, and how they impact static properties, dynamic properties, directional stability, coupling and overall vehicle design. An aspect ratio based dynamic decoupling condition is also presented. The second part of dissertation addresses design of linear time-invariant (LTI), multi-input multi-ouput (MIMO) fixed-structure H∞ controllers for the inner-loop velocity (v, ω) tracking system of the robot, motivated by a practical desire to design classically structured robust controllers. The fixed-structure H∞-optimal controllers are designed using Generalized Mixed Sensitivity(GMS) methodology to systematically shape properties at distinct loop breaking points. The H∞-control problem is solved using nonsmooth optimization techniques to compute locally optimal solutions. Matlab’s Robust Control toolbox (Hinfstruct and Systune) is used to solve the nonsmooth optimization. The dissertation also addresses the design of fixed-structure MIMO gain-scheduled H∞ controllers via GMS methodology. Trade-off studies are conducted to address the effect of vehicle design parameters on frequency and time domain properties of the inner-loop control system of mobile robot. The third part of dissertation focuses on the design of outer-loop position (x, y, θ) control system of mobile robot using real-time model predictive control (MPC) algorithms. Both linear time-varying (LTV) MPC and nonlinear MPC algorithms are discussed.The outer-loop performance of mobile robot is studied for two applications - 1) single robot trajectory tracking and multi-robot coordination in presence of obstacles, 2) maximum progress maneuvering on racetrack. The dissertation specifically addresses the impact of variation of c.g. position w.r.t. wheel-axle on directional maneuverability, peak control effort required to perform aggressive maneuvers, and overall position control performance. Detailed control relevant performance trade-offs associated with outer-loop position control are demonstrated through simulations in discrete time. Optimizations packages CPLEX(convex-QP in LTV-MPC) and ACADO(NLP in nonlinear-MPC) are used to solve the OCP in real time. All simulations are performed on Robot Operating System (ROS).
ContributorsMondal, Kaustav (Author) / Rodriguez, Armando A (Thesis advisor) / Berman, Spring M (Committee member) / Si, Jenni (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2021
Description
Rock traits (grain size, shape, orientation) are fundamental indicators of geologic processes including geomorphology and active tectonics. Fault zone evolution, fault slip rates, and earthquake timing are informed by examinations of discontinuities in the displacements of the Earth surface at fault scarps. Fault scarps indicate the structure of fault zones fans, relay ramps, and double faults, as well as the surface process response to the deformation and can thus indicate the activity of the fault zone and its potential hazard. “Rocky” fault scarps are unusual because they share characteristics of bedrock and alluvial fault scarps. The Volcanic Tablelands in Bishop, CA offer a natural laboratory with an array of rocky fault scarps. Machine learning mask-Region Convolutional Neural Network segments an orthophoto to identify individual particles along a specific rocky fault scarp. The resulting rock traits for thousands of particles along the scarp are used to develop conceptual models for rocky scarp geomorphology and evolution. In addition to rocky scarp classification, these tools may be useful in many sedimentary and volcanological applications for particle mapping and characterization.
ContributorsScott, Tyler (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Committee member) / DeVecchio, Duane (Committee member) / Arizona State University (Publisher)
Created2020
Description
The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
ContributorsAnand, Harish (Author) / Das, Jnaneshwar (Thesis advisor) / Yang, Yezhou (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2020
Description
One potential application of multi-robot systems is collective transport, a task in which multiple mobile robots collaboratively transport a payload that is too large or heavy to be carried by a single robot. Numerous control schemes have been proposed for collective transport in environments where robots can localize themselves (e.g., using GPS) and communicate with one another, have information about the payload's geometric and dynamical properties, and follow predefined robot and/or payload trajectories. However, these approaches cannot be applied in uncertain environments where robots do not have reliable communication and GPS and lack information about the payload. These conditions characterize a variety of applications, including construction, mining, assembly in space and underwater, search-and-rescue, and disaster response.
Toward this end, this thesis presents decentralized control strategies for collective transport by robots that regulate their actions using only their local sensor measurements and minimal prior information. These strategies can be implemented on robots that have limited or absent localization capabilities, do not explicitly exchange information, and are not assigned predefined trajectories. The controllers are developed for collective transport over planar surfaces, but can be extended to three-dimensional environments.
This thesis addresses the above problem for two control objectives. First, decentralized controllers are proposed for velocity control of collective transport, in which the robots must transport a payload at a constant velocity through an unbounded domain that may contain strictly convex obstacles. The robots are provided only with the target transport velocity, and they do not have global localization or prior information about any obstacles in the environment. Second, decentralized controllers are proposed for position control of collective transport, in which the robots must transport a payload to a target position through a bounded or unbounded domain that may contain convex obstacles. The robots are subject to the same constraints as in the velocity control scenario, except that they are assumed to have global localization. Theoretical guarantees for successful execution of the task are derived using techniques from nonlinear control theory, and it is shown through simulations and physical robot experiments that the transport objectives are achieved with the proposed controllers.
Toward this end, this thesis presents decentralized control strategies for collective transport by robots that regulate their actions using only their local sensor measurements and minimal prior information. These strategies can be implemented on robots that have limited or absent localization capabilities, do not explicitly exchange information, and are not assigned predefined trajectories. The controllers are developed for collective transport over planar surfaces, but can be extended to three-dimensional environments.
This thesis addresses the above problem for two control objectives. First, decentralized controllers are proposed for velocity control of collective transport, in which the robots must transport a payload at a constant velocity through an unbounded domain that may contain strictly convex obstacles. The robots are provided only with the target transport velocity, and they do not have global localization or prior information about any obstacles in the environment. Second, decentralized controllers are proposed for position control of collective transport, in which the robots must transport a payload to a target position through a bounded or unbounded domain that may contain convex obstacles. The robots are subject to the same constraints as in the velocity control scenario, except that they are assumed to have global localization. Theoretical guarantees for successful execution of the task are derived using techniques from nonlinear control theory, and it is shown through simulations and physical robot experiments that the transport objectives are achieved with the proposed controllers.
ContributorsFarivarnejad, Hamed (Author) / Berman, Spring (Thesis advisor) / Mignolet, Marc (Committee member) / Tsakalis, Konstantinos (Committee member) / Artemiadis, Panagiotis (Committee member) / Gil, Stephanie (Committee member) / Arizona State University (Publisher)
Created2020