Matching Items (15)
Description
This thesis considers two problems in the control of robotic swarms. Firstly, it addresses a trajectory planning and task allocation problem for a swarm of resource-constrained robots that cannot localize or communicate with each other and that exhibit stochasticity in their motion and task switching policies. We model the population dynamics of the robotic swarm as a set of advection-diffusion- reaction (ADR) partial differential equations (PDEs).
Specifically, we consider a linear parabolic PDE model that is bilinear in the robots' velocity and task-switching rates. These parameters constitute a set of time-dependent control variables that can be optimized and transmitted to the robots prior to their deployment or broadcasted in real time. The planning and allocation problem can then be formulated as a PDE-constrained optimization problem, which we solve using techniques from optimal control. Simulations of a commercial pollination scenario validate the ability of our control approach to drive a robotic swarm to achieve predefined spatial distributions of activity over a closed domain, which may contain obstacles. Secondly, we consider a mapping problem wherein a robotic swarm is deployed over a closed domain and it is necessary to reconstruct the unknown spatial distribution of a feature of interest. The ADR-based primitives result in a coefficient identification problem for the corresponding system of PDEs. To deal with the inherent ill-posedness of the problem, we frame it as an optimization problem. We validate our approach through simulations and show that reconstruction of the spatially-dependent coefficient can be achieved with considerable accuracy using temporal information alone.
Specifically, we consider a linear parabolic PDE model that is bilinear in the robots' velocity and task-switching rates. These parameters constitute a set of time-dependent control variables that can be optimized and transmitted to the robots prior to their deployment or broadcasted in real time. The planning and allocation problem can then be formulated as a PDE-constrained optimization problem, which we solve using techniques from optimal control. Simulations of a commercial pollination scenario validate the ability of our control approach to drive a robotic swarm to achieve predefined spatial distributions of activity over a closed domain, which may contain obstacles. Secondly, we consider a mapping problem wherein a robotic swarm is deployed over a closed domain and it is necessary to reconstruct the unknown spatial distribution of a feature of interest. The ADR-based primitives result in a coefficient identification problem for the corresponding system of PDEs. To deal with the inherent ill-posedness of the problem, we frame it as an optimization problem. We validate our approach through simulations and show that reconstruction of the spatially-dependent coefficient can be achieved with considerable accuracy using temporal information alone.
ContributorsElamvazhuthi, Karthik (Author) / Berman, Spring Melody (Thesis advisor) / Peet, Matthew Monnig (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2014
Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
Description
Coordination and control of Intelligent Agents as a team is considered in this thesis.
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
ContributorsKAMBAM, KARTHIK (Author) / Zhang, Wenlong (Thesis advisor) / Nedich, Angelia (Thesis advisor) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Image stabilization is a highly desired feature for many systems involving cameras. A camera stabilizer effectively prevents or compensates for unwanted camera movement to provide this stabilization. The use of stabilized camera technology on board aerial vehicles is one such application where the stabilization can greatly improve the overall capability of the system. The requirements for such a system include a continuous control algorithm and hardware to determine and adjust the camera orientation. The topic of developing an aerial camera control and electronic stabilization system is thus explored in the contents of this paper.
ContributorsJauregui, Joseph (Co-author) / Brown, Steven (Co-author) / Burger, Kevin (Thesis director) / Hansen, Mark (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
This study aims to showcase the results of a quadrotor model and the mathematical techniques used to arrive at the proposed design. Multicopters have made an explosive appearance in recent years by the controls engineering community because of their unique flight performance capabilities and potential for autonomy. The ultimate goal of this research is to design a robust control system that guides and tracks the quadrotor's trajectory, while responding to outside disturbances and obstacles that will realistically be encountered during flight. The first step is to accurately identify the physical system and attempt to replicate its behavior with a simulation that mimics the system's dynamics. This becomes quite a complex problem in itself because many realistic systems do not abide by simple, linear mathematical models, but rather nonlinear equations that are difficult to predict and are often numerically unstable. This paper explores the equations and assumptions used to create a model that attempts to match roll and pitch data collected from multiple test flights. This is done primarily in the frequency domain to match natural frequency locations, which can then be manipulated judiciously by altering certain parameters.
ContributorsDuensing, Jared Christopher (Author) / Takahashi, Timothy (Thesis director) / Garrett, Frederick (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
Description
In this project, an existing waveform generator designed by the vagus nerve stimulation (VNS) technology firm Hoolest Performance Technologies was modified and characterized. Voltage feedback and current feedback systems were designed in order to improve output voltage and current regulation. A wireless communication system was implemented onboard the newly designed waveform generator in order to improve user experience and allow the system to be controlled remotely. Finally, a custom printed circuit board was designed according to the established circuit schematics for the above components, and the layout was miniaturized to a total board footprint area of 1.5 square inches. The completed device was characterized according to several figures of merit including current consumption, voltage and current regulation, and short-circuit behavior.
ContributorsPatterson, John Michael (Author) / Kozicki, Michael (Thesis director) / Mian, Sami (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous
Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design, control objectives for rear-wheel drive ground vehicles.
Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform
that can be used for conducting FAME research.
A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.
The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability
of commercially available vehicles costing over $2000.
All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following
summarizes the key hardware demonstrations presented and analyzed:
(1)Cruise (v, ) control along a line,
(2) Cruise (v, ) control along a curve,
(3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle,
(4) Finish the track with camera pan tilt structure in minimum time,
(5) Finish the track without camera pan tilt structure in minimum time,
(6) Vision based tracking performance with different cruise speed vx,
(7) Vision based tracking performance with different camera fixed look-ahead distance L,
(8) Vision based tracking performance with different delay Td from vision subsystem,
(9) Manually remote controlled robot to perform indoor SLAM,
(10) Autonomously line guided robot to perform indoor SLAM.
For most cases, hardware data is compared with, and corroborated by, model based
simulation data. In short, the thesis uses low-cost self-designed rear-wheel
drive robot to demonstrate many capabilities that are critical in order to reach the
longer-term FAME goal.
Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design, control objectives for rear-wheel drive ground vehicles.
Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform
that can be used for conducting FAME research.
A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.
The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability
of commercially available vehicles costing over $2000.
All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following
summarizes the key hardware demonstrations presented and analyzed:
(1)Cruise (v, ) control along a line,
(2) Cruise (v, ) control along a curve,
(3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle,
(4) Finish the track with camera pan tilt structure in minimum time,
(5) Finish the track without camera pan tilt structure in minimum time,
(6) Vision based tracking performance with different cruise speed vx,
(7) Vision based tracking performance with different camera fixed look-ahead distance L,
(8) Vision based tracking performance with different delay Td from vision subsystem,
(9) Manually remote controlled robot to perform indoor SLAM,
(10) Autonomously line guided robot to perform indoor SLAM.
For most cases, hardware data is compared with, and corroborated by, model based
simulation data. In short, the thesis uses low-cost self-designed rear-wheel
drive robot to demonstrate many capabilities that are critical in order to reach the
longer-term FAME goal.
ContributorsLu, Xianglong (Author) / Rodriguez, Armando Antonio (Thesis advisor) / Berman, Spring (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2016
Description
Modern life is full of challenging optimization problems that we unknowingly attempt to solve. For instance, a common dilemma often encountered is the decision of picking a parking spot while trying to minimize both the distance to the goal destination and time spent searching for parking; one strategy is to drive as close as possible to the goal destination but risk a penalty cost if no parking spaces can be found. Optimization problems of this class all have underlying time-varying processes that can be altered by a decision/input to minimize some cost. Such optimization problems are commonly solved by a class of methods called Dynamic Programming (DP) that breaks down a complex optimization problem into a simpler family of sub-problems. In the 1950s Richard Bellman introduced a class of DP methods that broke down Multi-Stage Optimization Problems (MSOP) into a nested sequence of ``tail problems”. Bellman showed that for any MSOP with a cost function that satisfies a condition called additive separability, the solution to the tail problem of the MSOP initialized at time-stage k>0 can be used to solve the tail problem initialized at time-stage k-1. Therefore, by recursively solving each tail problem of the MSOP, a solution to the original MSOP can be found. This dissertation extends Bellman`s theory to a broader class of MSOPs involving non-additively separable costs by introducing a new state augmentation solution method and generalizing the Bellman Equation. This dissertation also considers the analogous continuous-time counterpart to discrete-time MSOPs, called Optimal Control Problems (OCPs). OCPs can be solved by solving a nonlinear Partial Differential Equation (PDE) called the Hamilton-Jacobi-Bellman (HJB) PDE. Unfortunately, it is rarely possible to obtain an analytical solution to the HJB PDE. This dissertation proposes a method for approximately solving the HJB PDE based on Sum-Of-Squares (SOS) programming. This SOS algorithm can be used to synthesize controllers, hence solving the OCP, and also compute outer bounds of reachable sets of dynamical systems. This methodology is then extended to infinite time horizons, by proposing SOS algorithms that yield Lyapunov functions that can approximate regions of attraction and attractor sets of nonlinear dynamical systems arbitrarily well.
ContributorsJones, Morgan (Author) / Peet, Matthew M (Thesis advisor) / Nedich, Angelia (Committee member) / Kawski, Matthias (Committee member) / Mignolet, Marc (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2021
Description
This project compared two optimization-based formulations for solving multi-robot task allocation problems with tether constraints. The first approach, or the ”Iterative Method,” used the common multiple traveling salesman (mTSP) formulation and implemented an algorithm over the formulation to filter out solutions that failed to satisfy the tether constraint. The second approach, named the ”Timing Formulation,” involved constructing a new formulation specifically designed account for robot timings, including the tether constraint in the formulation itself. The approaches were tested against each other in 10-city simulations and the results were compared. The Iterative Method could provide answers in 1- and 2-norm variations quickly, but its mTSP model formulation broke down and became infeasible at low city numbers. The 1-norm Timing Formulation quickly and reliably produced solutions but faced high computation times in its 2-norm manifestation. Ultimately, while the Timing Formulation is a more optimal method for solving tether-constrained task allocation problems, its reliance on the 1-norm for low computation times causes it to sacrifice some realism.
ContributorsGoodwin, Walter (Author) / Yong, Sze Zheng (Thesis director) / Grewal, Anoop (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05
ContributorsGoodwin, Walter (Author) / Yong, Sze Zheng (Thesis director) / Grewal, Anoop (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05