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- All Subjects: Advection Diffusion
- All Subjects: Vehicles, Remotely piloted--Design and construction.
- Creators: Berman, Spring Melody
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
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 175 but offers the capability of commercially available vehicles costing over 500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws.
All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 175 but offers the capability of commercially available vehicles costing over 500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws.
All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
ContributorsLin, Zhenyu (Author) / Rodriguez, Armando Antonio (Committee member) / Si, Jennie (Committee member) / Berman, Spring Melody (Committee member) / Arizona State University (Publisher)
Created2015