Matching Items (17)
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
In this dissertation, new data-driven techniques are developed to solve three problems related to generating predictive models of the immune system. These problems and their solutions are summarized as follows. The first problem is that, while cellular characteristics can be measured using flow cytometry, immune system cells are often analyzed only after they are sorted into groups by those characteristics. In Chapter 3 a method of analyzing the cellular characteristics of the immune system cells by generating Probability Density Functions (PDFs) to model the flow cytometry data is proposed. To generate a PDF to model the distribution of immune cell characteristics a new class of random variable called Sliced-Distributions (SDs) is developed. It is shown that the SDs can outperform other state-of-the-art methods on a set of benchmarks and can be used to differentiate between immune cells taken from healthy patients and those with Rheumatoid Arthritis. The second problem is that while immune system cells can be broken into different subpopulations, it is unclear which subpopulations are most significant. In Chapter 4 a new machine learning algorithm is formulated and used to identify subpopulations that can best predict disease severity or the populations of other immune cells. The proposed machine learning algorithm performs well when compared to other state-of-the-art methods and is applied to an immunological dataset to identify disease-relevant subpopulations of immune cells denoted immune states. Finally, while immunotherapies have been effectively used to treat cancer, selecting an optimal drug dose and period of treatment administration is still an open problem. In Chapter 5 a method to estimate Lyapunov functions of a system with unknown dynamics is proposed. This method is applied to generate a semialgebraic set containing immunotherapy doses and period of treatment that is predicted to eliminate a patient's tumor. The problem of selecting an optimal pulsed immunotherapy treatment from this semialgebraic set is formulated as a Global Polynomial Optimization (GPO) problem. In Chapter 6 a new method to solve GPO problems is proposed and optimal pulsed immunotherapy treatments are identified for this system.
ContributorsColbert, Brendon (Author) / Peet, Matthew M (Thesis advisor) / Acharya, Abhinav P (Committee member) / Berman, Spring M (Committee member) / Crespo, Luis G (Committee member) / Yong, Sze Z (Committee member) / Arizona State University (Publisher)
Created2021
Description
Unmanned aerial vehicles (UAVs) have reshaped the world of aviation. With the emergence of different types of UAVs, a multitude of mission critical applications, e.g., aerial photography, package delivery, grasping and manipulation, aerial reconnaissance and surveillance have been accomplished successfully. All of the aforementioned applications require the UAVs to be robust to external disturbances and safe while flying in cluttered environments and these factors are of paramount importance for task completion. In the first phase, this dissertation starts by presenting the synthesis and experimental validation of real-time low-level estimation and robust attitude and position controllers for multirotors. For the task of reliable position estimation, a hybrid low-pass de-trending filter is proposed for attenuating noise and drift in the velocity and position estimates respectively. Subsequently, a disturbance observer (DOB) approach with online Q-filter tuning is proposed for disturbance rejection and precise position control. Finally, a non-linear disturbance observer (NDOB) approach, along with a parameter optimization framework, is proposed for robust attitude control of multirotors. Multiple simulation and experimental flight tests are performed to demonstrate the efficacy of the proposed algorithms. Aerial grasping and collection is a type of mission-critical task which requires vision based sensing and robust control algorithms for successful task completion. In the second phase, this dissertation initially explores different object grasping approaches utilizing soft and rigid graspers. Additionally, vision based control paradigms are developed for object grasping and collection applications, specifically from water surfaces. Autonomous object collection from water surfaces presents a multitude of challenges: i) object drift due to propeller outwash, ii) reflection and glare from water surfaces makes object detection extremely challenging and iii) lack of reliable height sensors above water surface (for autonomous landing on water). Finally, a first of its kind aerial manipulation system, with an integrated net system and a robust vision based control structure, is proposed for floating object collection from water surfaces. Objects of different shapes and sizes are collected, through multiple experimental flight tests, with a success rate of 91.6%. To the best of the author's knowledge, this is the first work demonstrating autonomous object collection from water surfaces.
ContributorsMishra, Shatadal (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring M (Committee member) / Sugar, Thomas G (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation discusses continuous-time reinforcement learning (CT-RL) for control of affine nonlinear systems. Continuous-time nonlinear optimal control problems hold great promise in real-world applications. After decades of development, reinforcement learning (RL) has achieved some of the greatest successes as a general nonlinear control design method. Yet as RL control has developed, CT-RL results have greatly lagged their discrete-time RL (DT-RL) counterparts, especially in regards to real-world applications. Current CT-RL algorithms generally fall into two classes: adaptive dynamic programming (ADP), and actor-critic deep RL (DRL). The first school of ADP methods features elegant theoretical results stemming from adaptive and optimal control. Yet, they have not been shown effectively synthesizing meaningful controllers. The second school of DRL has shown impressive learning solutions, yet theoretical guarantees are still to be developed. A substantive analysis uncovering the quantitative causes of the fundamental gap between CT and DT remains to be conducted. Thus, this work develops a first-of-its kind quantitative evaluation framework to diagnose the performance limitations of the leading CT-RL methods. This dissertation also introduces a suite of new CT-RL algorithms which offers both theoretical and synthesis guarantees. The proposed design approach relies on three important factors. First, for physical systems that feature physically-motivated dynamical partitions into distinct loops, the proposed decentralization method breaks the optimal control problem into smaller subproblems. Second, the work introduces a new excitation framework to improve persistence of excitation (PE) and numerical conditioning via classical input/output insights. Third, the method scales the learning problem via design-motivated invertible transformations of the system state variables in order to modulate the algorithm learning regression for further increases in numerical stability. This dissertation introduces a suite of (decentralized) excitable integral reinforcement learning (EIRL) algorithms implementing these paradigms. It rigorously proves convergence, optimality, and closed-loop stability guarantees of the proposed methods, which are demonstrated in comprehensive comparative studies with the leading methods in ADP on a significant application problem of controlling an unstable, nonminimum phase hypersonic vehicle (HSV). It also conducts comprehensive comparative studies with the leading DRL methods on three state-of-the-art (SOTA) environments, revealing new performance/design insights.
ContributorsWallace, Brent Abraham (Author) / Si, Jennie (Thesis advisor) / Berman, Spring M (Committee member) / Bertsekas, Dimitri P (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2024
Description
In the past decade, real-world applications of Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAV) have increased significantly. There has been growing interest in one of these types of UAVs, called a tail-sitter UAV, due to its VTOL and cruise capabilities. This thesis presents the fabrication of a spherical tail-sitter UAV and derives a nonlinear mathematical model of its dynamics. The singularity in the attitude kinematics of the vehicle is avoided using Modified Rodrigues Parameters (MRP). The model parameters of the fabricated vehicle are calculated using the bifilar pendulum method, a motor stand, and ANSYS simulation software. Then the trim conditions at hover are calculated for the nonlinear model, and the rotational dynamics of the model are linearized around the equilibrium state with the calculated trim conditions. Robust controllers are designed to stabilize the UAV in hover using the H2 control and H-infinity control methodologies. For H2 control design, Linear Quadratic Gaussian (LQG) control is used. For the H infinity control design, Linear Matrix Inequalities (LMI) with frequency-dependent weights are derived and solved using the MATLAB toolbox YALMIP. In addition, a nonlinear controller is designed using the Sum-of-Squares (SOS) method to implement large-angle maneuvers for transitions between horizontal flight and vertical flight. Finally, the linear controllers are implemented in the fabricated spherical tail-sitter UAV for experimental validation. The performance trade-offs and the response of the UAV with the linear and nonlinear controllers are discussed in detail.
ContributorsRamasubramaniyan, Sri Ram Prasath (Author) / Berman, Spring M (Thesis advisor) / Mignolet, Marc P (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2018
Description
Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern.
First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation.
To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.
First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation.
To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.
ContributorsHaggerty, Zz Mae (Author) / Berman, Spring M (Thesis advisor) / Aukes, Daniel (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2018
Description
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses various perception and control problems in autonomous aerial robotics. The objective of this thesis is to motivate the use of perspective cues in single images for the planning and control of quadrotors in indoor environments. In addition to providing empirical evidence for the abundance of such cues in indoor environments, the usefulness of these perspective cues is demonstrated by designing a control algorithm for navigating a quadrotor in indoor corridors. An Extended Kalman Filter (EKF), implemented on top of the vision algorithm, serves to improve the robustness of the algorithm to changing illumination.
In this thesis, vanishing points are the perspective cues used to control and navigate a quadrotor in an indoor corridor. Indoor corridors are an abundant source of parallel lines. As a consequence of perspective projection, parallel lines in the real world, that are not parallel to the plane of the camera, intersect at a point in the image. This point is called the vanishing point of the image. The vanishing point is sensitive to the lateral motion of the camera and hence the quadrotor. By tracking the position of the vanishing point in every image frame, the quadrotor can navigate along the center of the corridor.
Experiments are conducted using the Augmented Reality (AR) Drone 2.0. The drone is equipped with the following componenets: (1) 720p forward facing camera for vanishing point detection, (2) 240p downward facing camera, (3) Inertial Measurement Unit (IMU) for attitude control , (4) Ultrasonic sensor for estimating altitude, (5) On-board 1 GHz Processor for processing low level commands. The reliability of the vision algorithm is presented by flying the drone in indoor corridors.
In this thesis, vanishing points are the perspective cues used to control and navigate a quadrotor in an indoor corridor. Indoor corridors are an abundant source of parallel lines. As a consequence of perspective projection, parallel lines in the real world, that are not parallel to the plane of the camera, intersect at a point in the image. This point is called the vanishing point of the image. The vanishing point is sensitive to the lateral motion of the camera and hence the quadrotor. By tracking the position of the vanishing point in every image frame, the quadrotor can navigate along the center of the corridor.
Experiments are conducted using the Augmented Reality (AR) Drone 2.0. The drone is equipped with the following componenets: (1) 720p forward facing camera for vanishing point detection, (2) 240p downward facing camera, (3) Inertial Measurement Unit (IMU) for attitude control , (4) Ultrasonic sensor for estimating altitude, (5) On-board 1 GHz Processor for processing low level commands. The reliability of the vision algorithm is presented by flying the drone in indoor corridors.
ContributorsRavishankar, Nikhilesh (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2018
Description
Robotic swarms can potentially perform complicated tasks such as exploration and mapping at large space and time scales in a parallel and robust fashion. This thesis presents strategies for mapping environmental features of interest – specifically obstacles, collision-free paths, generating a metric map and estimating scalar density fields– in an unknown domain using data obtained by a swarm of resource-constrained robots. First, an approach was developed for mapping a single obstacle using a swarm of point-mass robots with both directed and random motion. The swarm population dynamics are modeled by a set of advection-diffusion-reaction partial differential equations (PDEs) in which a spatially-dependent indicator function marks the presence or absence of the obstacle in the domain. The indicator function is estimated by solving an optimization problem with PDEs as constraints. Second, a methodology for constructing a topological map of an unknown environment was proposed, which indicates collision-free paths for navigation, from data collected by a swarm of finite-sized robots. As an initial step, the number of topological features in the domain was quantified by applying tools from algebraic topology, to a probability function over the explored region that indicates the presence of obstacles. A topological map of the domain is then generated using a graph-based wave propagation algorithm. This approach is further extended, enabling the technique to construct a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots, filtered using intensity measurements of an external signal. Next, a distributed method was developed to construct the occupancy grid map of an unknown environment using a swarm of inexpensive robots or mobile sensors with limited communication. In addition to this, an exploration strategy which combines information theoretic ideas with Levy walks was also proposed. Finally, the problem of reconstructing a two-dimensional scalar field using observations from a subset of a sensor network in which each node communicates its local measurements to its neighboring nodes was addressed. This problem reduces to estimating the initial condition of a large interconnected system with first-order linear dynamics, which can be solved as an optimization problem.
ContributorsRamachandran, Ragesh Kumar (Author) / Berman, Spring M (Thesis advisor) / Mignolet, Marc (Committee member) / Artemiadis, Panagiotis (Committee member) / Marvi, Hamid (Committee member) / Robinson, Michael (Committee member) / Arizona State University (Publisher)
Created2018
Description
Toward the ambitious long-term goal of developing a robotic circus, this thesis addresses key steps toward the development of a ground robot that can catch a ball. Toward this end, we examine nonlinear quadratic drag trajectories for a tossed ball. Relevant least square error fits are provided. It is also shown how a Kalman filter and Extended Kalman filter can be used to generate estimates for the ball trajectory.
Several simple ball intercept policies are examined. This includes open loop and closed loop policies. It is also shown how a low-cost differential-drive research grade robot can be built, modeled and controlled. Directions for developing more complex xy planar intercept policies are also briefly discussed. In short, the thesis establishes a foundation for future work on developing a practical ball catching robot.
Several simple ball intercept policies are examined. This includes open loop and closed loop policies. It is also shown how a low-cost differential-drive research grade robot can be built, modeled and controlled. Directions for developing more complex xy planar intercept policies are also briefly discussed. In short, the thesis establishes a foundation for future work on developing a practical ball catching robot.
ContributorsDAS, NIRANGKUSH (Author) / Rodriguez, Armando A (Thesis advisor) / Berman, Spring M (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
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