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- Creators: Berman, Spring
Description
This thesis presents a process by which a controller used for collective transport tasks is qualitatively studied and probed for presence of undesirable equilibrium states that could entrap the system and prevent it from converging to a target state. Fields of study relevant to this project include dynamic system modeling, modern control theory, script-based system simulation, and autonomous systems design. Simulation and computational software MATLAB and Simulink® were used in this thesis.
To achieve this goal, a model of a swarm performing a collective transport task in a bounded domain featuring convex obstacles was simulated in MATLAB/ Simulink®. The closed-loop dynamic equations of this model were linearized about an equilibrium state with angular acceleration and linear acceleration set to zero. The simulation was run over 30 times to confirm system ability to successfully transport the payload to a goal point without colliding with obstacles and determine ideal operating conditions by testing various orientations of objects in the bounded domain. An additional purely MATLAB simulation was run to identify local minima of the Hessian of the navigation-like potential function. By calculating this Hessian periodically throughout the system’s progress and determining the signs of its eigenvalues, a system could check whether it is trapped in a local minimum, and potentially dislodge itself through implementation of a stochastic term in the robot controllers. The eigenvalues of the Hessian calculated in this research suggested the model local minima were degenerate, indicating an error in the mathematical model for this system, which likely incurred during linearization of this highly nonlinear system.
To achieve this goal, a model of a swarm performing a collective transport task in a bounded domain featuring convex obstacles was simulated in MATLAB/ Simulink®. The closed-loop dynamic equations of this model were linearized about an equilibrium state with angular acceleration and linear acceleration set to zero. The simulation was run over 30 times to confirm system ability to successfully transport the payload to a goal point without colliding with obstacles and determine ideal operating conditions by testing various orientations of objects in the bounded domain. An additional purely MATLAB simulation was run to identify local minima of the Hessian of the navigation-like potential function. By calculating this Hessian periodically throughout the system’s progress and determining the signs of its eigenvalues, a system could check whether it is trapped in a local minimum, and potentially dislodge itself through implementation of a stochastic term in the robot controllers. The eigenvalues of the Hessian calculated in this research suggested the model local minima were degenerate, indicating an error in the mathematical model for this system, which likely incurred during linearization of this highly nonlinear system.
ContributorsBaye-Wallace, Lily Catherine (Author) / Berman, Spring (Thesis director) / Marvi, Hamidreza (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor) / School for Engineering of Matter,Transport & Enrgy (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
In the next decade or so, there will be a shift in the industry of transportation across the world. Already today we have autonomous vehicles (AVs) tested in the Greater Phoenix area showing that the technology has improved to a level available to the public eye. Although this technology is not yet released commercially (for the most part), it is being used and will continue to be used to develop a safer future. With a high incidence of human error causing accidents, many expect that autonomous vehicles will be safer than human drivers. They do still require driver attention and sometimes intervention to ensure safety, but for the most part are much safer. In just the United States alone, there were 40,000 deaths due to car accidents last year [1]. If traffic fatalities were considered a disease, this would be an epidemic. The technology behind autonomous vehicles will allow for a much safer environment and increased mobility and independence for people who cannot drive and struggle with public transport. There are many opportunities for autonomous vehicles in the transportation industry. Companies can save a lot more money on shipping by cutting the costs of human drivers and trucks on the road, even allowing for simpler drop shipments should the necessary AI be developed.Research is even being done by several labs at Arizona State University. For example, Dr. Spring Berman’s Autonomous Collective Systems Lab has been collaborating with Dr. Nancy Cooke of Human Systems Engineering to develop a traffic testbed, CHARTopolis, to study the risks of driver-AV interactions and the psychological effects of AVs on human drivers on a small scale. This testbed will be used by researchers from their labs and others to develop testing on reaction, trust, and user experience with AVs in a safe environment that simulates conditions similar to those experienced by full-size AVs. Using a new type of small robot that emulates an AV, developed in Dr. Berman’s lab, participants will be able to remotely drive around a model city environment and interact with other AV-like robots using the cameras and LiDAR sensors on the remotely driven robot to guide them.
Although these commercial and research systems are still in testing, it is important to understand how AVs are being marketed to the general public and how they are perceived, so that one day they may be effectively adopted into everyday life. People do not want to see a car they do not trust on the same roads as them, so the questions are: why don’t people trust them, and how can companies and researchers improve the trustworthiness of the vehicles?
Although these commercial and research systems are still in testing, it is important to understand how AVs are being marketed to the general public and how they are perceived, so that one day they may be effectively adopted into everyday life. People do not want to see a car they do not trust on the same roads as them, so the questions are: why don’t people trust them, and how can companies and researchers improve the trustworthiness of the vehicles?
ContributorsShuster, Daniel Nadav (Author) / Berman, Spring (Thesis director) / Cooke, Nancy (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This thesis details the design and construction of a torque-controlled robotic gripper for use with the Pheeno swarm robotics platform. This project required expertise from several fields of study including: robotic design, programming, rapid prototyping, and control theory. An electronic Inertial Measurement Unit and a DC Motor were both used along with 3D printed plastic components and an electronic motor control board to develop a functional open-loop controlled gripper for use in collective transportation experiments. Code was developed that effectively acquired and filtered rate of rotation data alongside other code that allows for straightforward control of the DC motor through experimentally derived relationships between the voltage applied to the DC motor and the torque output of the DC motor. Additionally, several versions of the physical components are described through their development.
ContributorsMohr, Brennan (Author) / Berman, Spring (Thesis director) / Ren, Yi (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School for Engineering of Matter,Transport & Enrgy (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In this paper, we propose an autonomous throwing and catching system to be developed as a preliminary step towards the refinement of a robotic arm capable of improving strength and motor function in the limb. This will be accomplished by first autonomizing simpler movements, such as throwing a ball. In this system, an autonomous thrower will detect a desired target through the use of image processing. The launch angle and direction necessary to hit the target will then be calculated, followed by the launching of the ball. The smart catcher will then detect the ball as it is in the air, calculate its expected landing location based on its initial trajectory, and adjust its position so that the ball lands in the center of the target. The thrower will then proceed to compare the actual landing position with the position where it expected the ball to land, and adjust its calculations accordingly for the next throw. By utilizing this method of feedback, the throwing arm will be able to automatically correct itself. This means that the thrower will ideally be able to hit the target exactly in the center within a few throws, regardless of any additional uncertainty in the system. This project will focus of the controller and image processing components necessary for the autonomous throwing arm to be able to detect the position of the target at which it will be aiming, and for the smart catcher to be able to detect the position of the projectile and estimate its final landing position by tracking its current trajectory.
ContributorsLundberg, Kathie Joy (Co-author) / Thart, Amanda (Co-author) / Rodriguez, Armando (Thesis director) / Berman, Spring (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree dimensions would speed up the measurement or data collection process and allow for a much larger set of trees to be used in studies. In turn, this would allow studies to make more generalizable inferences about areas with trees. To this end, this thesis focuses on developing a system that generates a semantic representation of the topology of a tree in real-time. The first part describes a simulation environment and a real-world sensor suite to develop and test the tree mapping pipeline proposed in this thesis. The second part presents details of the proposed tree mapping pipeline. Stage one of the mapping pipeline utilizes a deep learning network to detect woody and cylindrical portions of a tree like trunks and branches based on popular semantic segmentation networks. Stage two of the pipeline proposes an algorithm to separate the detected portions of a tree into individual trunk and branch segments. The third stage implements an optimization algorithm to represent each segment parametrically as a cylinder. The fourth stage formulates a multi-sensor factor graph to incrementally integrate and optimize the semantic tree map while also fusing two forms of odometry. Finally, results from all the stages of the tree mapping pipeline using simulation and real-world data are presented. With these implementations, this thesis provides an end-to-end system to estimate tree topology through semantic representations for forestry and precision agriculture applications.
ContributorsVishwanatha, Rakshith (Author) / Das, Jnaneshwar (Thesis advisor) / Martin, Roberta (Committee member) / Throop, Heather (Committee member) / Zhang, Wenlong (Committee member) / Ehsani, Reza (Committee member) / Arizona State University (Publisher)
Created2022
Description
Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge
due to the limitations of conventional LiDAR or vision systems. Therefore there
exists a need for a navigation algorithm and mapping strategy which do not use vision
systems but are still able to explore and map the environment. The map can further
be used by first responders and cave explorers to access the environments.
This thesis presents the design of a collision-resilient Unmanned Aerial Vehicle
(UAV), XPLORER that utilizes a novel navigation algorithm for exploration and
simultaneous mapping of the environment. The real-time navigation algorithm uses
the onboard Inertial Measurement Units (IMUs) and arm bending angles for contact
estimation and employs an Explore and Exploit strategy. Additionally, the quadrotor
design is discussed, highlighting its improved stability over the previous design.
The generated map of the environment can be utilized by autonomous vehicles to
navigate the environment. The navigation algorithm is validated in multiple real-time
experiments in different scenarios consisting of concave and convex corners and circular
objects. Furthermore, the developed mapping framework can serve as an auxiliary
input for map generation along with conventional LiDAR or vision-based mapping
algorithms.
Both the navigation and mapping algorithms are designed to be modular, making
them compatible with conventional UAVs also. This research contributes to the
development of navigation and mapping techniques for GPS-denied environments,
enabling safer and more efficient exploration of challenging territories.
ContributorsPandian Saravanakumaran, Aravind Adhith (Author) / Zhang, Wenlong (Thesis advisor) / Das, Jnaneshwar (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2023
Description
Multibody Dynamic (MBD) models are important tools in motion analysis and are used to represent and accurately predict the behavior of systems in the real-world. These models have a range of applications, including the stowage and deployment of flexible deployables on spacecraft, the dynamic response of vehicles in automotive design and crash testing, and mapping interactions of the human body. An accurate model can aid in the design of a system to ensure the system is effective and meets specified performance criteria when built. A model may have many design parameters, such as geometrical constraints and component mechanical properties, or controller parameters if the system uses an external controller. Varying these parameters and rerunning analyses by hand to find an ideal design can be time consuming for models that take hours or days to run. To reduce the amount of time required to find a set of parameters that produces a desired performance, optimization is necessary. Many papers have discussed methods for optimizing rigid and flexible MBD models, and separately their controllers, using both gradient-based and gradient-free algorithms. However, these optimization methods have not been used to optimize full-scale MBD models and their controllers simultaneously. This thesis presents a method for co-optimizing an MBD model and controller that allows for the flexibility to find model and controller-based solutions for systems with tightly coupled parameters. Specifically, the optimization is performed on a quadrotor drone MBD model undergoing disturbance from a slung load and its position controller to meet specified position error performance criteria. A gradient-free optimization algorithm and multiple objective approach is used due to the many local optima from the tradeoffs between the model and controller parameters. The thesis uses nine different quadrotor cases with three different position error formulations. The results are used to determine the effectiveness of the optimization and the ability to converge on a single optimal design. After reviewing the results, the optimization limitations are discussed as well as the ability to transition the optimization to work with different MBD models and their controllers.
ContributorsGambatese, Marcus (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Inoyama, Daisaku (Committee member) / Arizona State University (Publisher)
Created2022
Description
The technology and science capabilities of SmallSats continue to grow with the increase of capabilities in commercial off the shelf components. However, the maturation of SmallSat hardware has also led to an increase in component power consumption, this poses an issue with using traditional passive thermal management systems (radiators, thermal straps, etc.) to regulate high-power components. High power output becomes limited in order to maintain components within their allowable temperature ranges. The aim of this study is to explore new methods of using additive manufacturing to enable the usage of heat pipe structures on SmallSat platforms up to 3U’s in size. This analysis shows that these novel structures can increase the capabilities of SmallSat platforms by allowing for larger in-use heat loads from a nominal power density of 4.7 x 10^3 W/m3 to a higher 1.0 x 10^4 W/m3 , an order of magnitude increase. In addition, the mechanical properties of the SmallSat structure are also explored to characterize effects to the mechanical integrity of the spacecraft. The results show that the advent of heat pipe integration to the structures of SmallSats will lead to an increase in thermal management capabilities compared to the current state-of-the-art systems, while not reducing the structural integrity of the spacecraft. In turn, this will lead to larger science and technology capabilities for a field that is growing in both the education and private sectors.
ContributorsAcuna, Antonio (Author) / Das, Jnaneshwar (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autonomous Robots have a tremendous potential to assist humans in environmental monitoring tasks. In order to generate meaningful data for humans to analyze, the robots need to collect accurate data and develop reliable representation of the environment. This is achieved by employing scalable and robust navigation and mapping algorithms that facilitate acquiring and understanding data collected from the array of on-board sensors. To this end, this thesis presents navigation and mapping algorithms for autonomous robots that can enable robot navigation in complexenvironments and develop real time semantic map of the environment respectively. The first part of the thesis presents a novel navigation algorithm for an autonomous underwater vehicle that can maintain a fixed distance from the coral terrain while following a human diver. Following a human diver ensures that the robot would visit all important sites in the coral reef while maintaining a constant distance from the terrain reduces heterscedasticity in the measurements. This algorithm was tested on three different synthetic terrains including a real model of a coral reef in Hawaii. The second part of the thesis presents a dense semantic surfel mapping technique based on top of a popular surfel mapping algorithm that can generate meaningful maps in real time. A semantic mask from a depth aligned RGB-D camera was used to assign labels
to the surfels which were then probabilistically updated with multiple measurements. The mapping algorithm was tested with simulated data from an RGB-D camera and the results were analyzed.
ContributorsAntervedi, Lakshmi Gana Prasad (Author) / Das, Jnaneshwar (Thesis advisor) / Martin, Roberta E (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the last few decades, with the revolution of availability of low-cost microelectronics, which allow fast and complex computations to be performed on board, there has been increasing attention to aerial vehicles, especially rotary-wing vehicles. This is because of their ability to vertically takeoff and land (VTOL), which make them appropriate for urban environments where no runways are needed. Quadrotors took considerable attention in research and development due to their symmetric body, which makes them simpler to model and control compared to other configurations.
One contribution of this work is the design of a new open-source based Quadrotor platform for research. This platform is compatible with both HTC Vive Tracking System (HVTS) and OptiTrack Motion Capture System, Robot Operating System (ROS), and MAVLINK communication protocol.
The thesis examined both nonlinear and linear modeling of a 6-DOF rigid-body quadrotor's dynamics along with actuator dynamics. Nonlinear/linear models are used to develop control laws for both low-level and high-level hierarchical control structures. Both HVTS and OptiTrack were used to demonstrate path following for single and multiple quadrotors. Hardware and simulation data are compared. In short, this work establishes a foundation for future work on formation flight of multi-quadrotor.
One contribution of this work is the design of a new open-source based Quadrotor platform for research. This platform is compatible with both HTC Vive Tracking System (HVTS) and OptiTrack Motion Capture System, Robot Operating System (ROS), and MAVLINK communication protocol.
The thesis examined both nonlinear and linear modeling of a 6-DOF rigid-body quadrotor's dynamics along with actuator dynamics. Nonlinear/linear models are used to develop control laws for both low-level and high-level hierarchical control structures. Both HVTS and OptiTrack were used to demonstrate path following for single and multiple quadrotors. Hardware and simulation data are compared. In short, this work establishes a foundation for future work on formation flight of multi-quadrotor.
ContributorsAltawaitan, Abdullah (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2019