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- All Subjects: robotics
- Genre: Masters Thesis
Description
Unmanned aerial vehicles (UAVs) are widely used in many applications because of their small size, great mobility and hover performance. This has been a consequence of the fast development of electronics, cheap lightweight flight controllers for accurate positioning and cameras. This thesis describes modeling, control and design of an oblique-cross-quadcopter platform for indoor-environments.
One contribution of the work was the design of a new printed-circuit-board (PCB) flight controller (called MARK3). Key features/capabilities are as follows:
(1) a Teensy 3.2 microcontroller with 168MHz overclock –used for communications, full-state estimation and inner-outer loop hierarchical rate-angle-speed-position control,
(2) an on-board MEMS inertial-measurement-unit (IMU) which includes an LSM303D (3DOF-accelerometer and magnetometer), an L3GD20 (3DOF-gyroscope) and a BMP180 (barometer) for attitude estimation (barometer/magnetometer not used),
(3) 6 pulse-width-modulator (PWM) output pins supports up to 6 rotors
(4) 8 PWM input pins support up to 8-channel 2.4 GHz transmitter/receiver for manual control,
(5) 2 5V servo extension outputs for other requirements (e.g. gimbals),
(6) 2 universal-asynchronous-receiver-transmitter (UART) serial ports - used by flight controller to process data from Xbee; can be used for accepting outer-loop position commands from NVIDIA TX2 (future work),
(7) 1 I2C-serial-protocol two-wire port for additional modules (used to read data from IMU at 400 Hz),
(8) a 20-pin port for Xbee telemetry module connection; permits Xbee transceiver on desktop PC to send position/attitude commands to Xbee transceiver on quadcopter.
The quadcopter platform consists of the new MARK3 PCB Flight Controller, an ATG-250 carbon-fiber frame (250 mm), a DJI Snail propulsion-system (brushless-three-phase-motor, electronic-speed-controller (ESC) and propeller), an HTC VIVE Tracker and RadioLink R9DS 9-Channel 2.4GHz Receiver. This platform is completely compatible with the HTC VIVE Tracking System (HVTS) which has 7ms latency, submillimeter accuracy and a much lower price compared to other millimeter-level tracking systems.
The thesis describes nonlinear and linear modeling of the quadcopter’s 6DOF rigid-body dynamics and brushless-motor-actuator dynamics. These are used for hierarchical-classical-control-law development near hover. The HVTS was used to demonstrate precision hover-control and path-following. Simulation and measured flight-data are shown to be similar. This work provides a foundation for future precision multi-quadcopter formation-flight-control.
One contribution of the work was the design of a new printed-circuit-board (PCB) flight controller (called MARK3). Key features/capabilities are as follows:
(1) a Teensy 3.2 microcontroller with 168MHz overclock –used for communications, full-state estimation and inner-outer loop hierarchical rate-angle-speed-position control,
(2) an on-board MEMS inertial-measurement-unit (IMU) which includes an LSM303D (3DOF-accelerometer and magnetometer), an L3GD20 (3DOF-gyroscope) and a BMP180 (barometer) for attitude estimation (barometer/magnetometer not used),
(3) 6 pulse-width-modulator (PWM) output pins supports up to 6 rotors
(4) 8 PWM input pins support up to 8-channel 2.4 GHz transmitter/receiver for manual control,
(5) 2 5V servo extension outputs for other requirements (e.g. gimbals),
(6) 2 universal-asynchronous-receiver-transmitter (UART) serial ports - used by flight controller to process data from Xbee; can be used for accepting outer-loop position commands from NVIDIA TX2 (future work),
(7) 1 I2C-serial-protocol two-wire port for additional modules (used to read data from IMU at 400 Hz),
(8) a 20-pin port for Xbee telemetry module connection; permits Xbee transceiver on desktop PC to send position/attitude commands to Xbee transceiver on quadcopter.
The quadcopter platform consists of the new MARK3 PCB Flight Controller, an ATG-250 carbon-fiber frame (250 mm), a DJI Snail propulsion-system (brushless-three-phase-motor, electronic-speed-controller (ESC) and propeller), an HTC VIVE Tracker and RadioLink R9DS 9-Channel 2.4GHz Receiver. This platform is completely compatible with the HTC VIVE Tracking System (HVTS) which has 7ms latency, submillimeter accuracy and a much lower price compared to other millimeter-level tracking systems.
The thesis describes nonlinear and linear modeling of the quadcopter’s 6DOF rigid-body dynamics and brushless-motor-actuator dynamics. These are used for hierarchical-classical-control-law development near hover. The HVTS was used to demonstrate precision hover-control and path-following. Simulation and measured flight-data are shown to be similar. This work provides a foundation for future precision multi-quadcopter formation-flight-control.
ContributorsLu, Shi (Author) / Rodriguez, Armando A. (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Si, Jennie (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 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
Description
Vertical take-off and landing (VTOL) systems have become a crucial component of aeronautical and commercial applications alike. Quadcopter systems are rather convenient to analyze and design controllers for, owing to symmetry in body dynamics. In this work, a quadcopter model at hover equilibrium is derived, using both high and low level control. The low level control system is designed to track reference Euler angles (roll, pitch and yaw) as shown in previous work [1],[2]. The high level control is designed to track reference X, Y, and Z axis states [3].
The objective of this paper is to model, design and simulate platooning (separation) control for a fleet of 6 quadcopter units, each comprising of high and low level control systems, using a leader-follower approach. The primary motivation of this research is to examine the ”accordion effect”, a phenomenon observed in leader-follower systems due to which positioning or spacing errors arise in follower vehicles due to sudden changes in lead vehicle velocity. It is proposed that the accordion effect occurs when lead vehicle information is not directly communicated with the rest of the
system [4][5]
.
In this paper, the effect of leader acceleration feedback is observed for the quadcopter platoon. This is performed by first designing a classical platoon controller for a nominal case, where communication within the system is purely ad-hoc (i.e from one quadcopter to it’s immediate successor in the fleet). Steady state separation/positioning errors for each member of the fleet are observed and documented during simulation. Following this analysis, lead vehicle acceleration is provided to the controller (as a feed forward term), to observe the extent of it’s effect on steady state separation, specifically along tight maneuvers. Thus the key contribution of this work is a controller that stabilizes a platoon of quadcopters in the presence of the accordion effect, when employing a leader-follower approach. The modeling shown in this paper builds on previous research to design a low costquadcopter platform, the Mark 3 copter [1]. Prior to each simulation, model nonlinearities and hardware constants are measured or derived from the Mark 3 model, in an effort to observe the working of the system in the presence of realistic hardware constraints. The system is designed in compliance with Robot Operating System (ROS) and the Micro Air Vehicle Link (MAVLINK) communication protocol.
ContributorsSrinivasan, Anshuman (Author) / Rodriguez, Armando A. (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2021
Description
Over the past few decades, there is an increase in demand for various ground robot applications such as warehouse management, surveillance, mapping, infrastructure inspection, etc. This steady increase in demand has led to a significant rise in the nonholonomic differential drive vehicles (DDV) research. Albeit extensive work has been done in developing various control laws for trajectory tracking, point stabilization, formation control, etc., there are still problems and critical questions in regards to design, modeling, and control of DDV’s - that need to be adequately addressed. In this thesis, three different dynamical models are considered that are formed by varying the input/output parameters of the DDV model. These models are analyzed to understand their stability, bandwidth, input-output coupling, and control design properties. Furthermore, a systematic approach has been presented to show the impact of design parameters such as mass, inertia, radius of the wheels, and center of gravity location on the dynamic and inner-loop (speed) control design properties. Subsequently, extensive simulation and hardware trade studies have been conductedto quantify the impact of design parameters and modeling variations on the performance of outer-loop cruise and position control (along a curve). In addition to this, detailed guidelines are provided for when a multi-input multi-output (MIMO) control strategy is advisable over a single-input single-output (SISO) control strategy; when a less stable plant is preferable over a more stable one in order to accommodate performance specifications. Additionally, a multi-robot trajectory tracking implementation based on receding horizon optimization approach is also presented. In most of the optimization-based trajectory tracking approaches found in the literature, only the constraints imposed by the kinematic model are incorporated into the problem. This thesis elaborates the fundamental problem associated with these methods and presents a systematic approach to understand and quantify when kinematic model based constraints are sufficient and when dynamic model-based constraints are necessary to obtain good tracking properties. Detailed instructions are given for designing and building the DDV based on performance specifications, and also, an open-source platform capable of handling high-speed multi-robot research is developed in C++.
ContributorsManne, Sai Sravan (Author) / Rodriguez, Armando A (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2021