In recent years, we have seen the applications of distributed autonomous mobile robots (DAMRs) in a broad spectrum of areas like search and rescue, disaster management, warehouse, and delivery systems. Although each type of systems employing DAMRs has its specific challenges, they are all limited by energy since the robots are powered by batteries which have not advanced in decades. This motivates the development of energy efficiency for such systems. Although there has been research on optimizing energy for robotic systems, their approaches are from low-level (e.g., mechanic, system control, or avionic) perspectives. They, therefore, are limited to a specific type of robots and not easily adjusted to apply for different types of robots. Moreover, there is a lack of work studying the problem from a software perspective and abstraction. In this thesis, we tackle the problem from a software perspective and are particularly interested in DAMR systems in which a team of networked robots navigating in a physical environment and acting in concert to accomplish a common goal. Also, the primary focus of our work is to design schedules (or plans) for the robots so that they can achieve their goal while spending as little energy as possible. To this end, we study the problem in three different contexts: - Managing reliability and energy consumption tradeoff. That is, we propose that robots verify computational results of one another to increase the corroboration of outputs of our DAMR systems. However, this new feature requires robots to do additional tasks and consume more energy. Thus, we propose approaches to reach a balance between energy consumption and the reliability of results obtained by our DAMR systems. - Extending the operational time of robots. We first propose that our DAMR systems should employ charging stations where robots can come to recharge their batteries. Then, we aim to design schedules for the robots so that they can finish all their tasks while consuming as little energy and time (including the time spent for recharging) as possible. Moreover, we model the working space by a connected (possibly incomplete) graph to make the problem more practical. - Coping with environmental changes. This path planning problem takes into account not only energy limits but also changes in the physical environment, which may result in overheads (i.e., additional time and energy) that robots incur while doing their tasks. To tackle the problem from a software perspective, we first utilize Gaussian Process and Polynomial Regression to model disturbances and energy consumption, respectively, then proposed techniques to generate plans and adjust them when robots detect environmental changes. For each problem, we give a formal description, a transformation to integer (linear) programming, online algorithms, and an online algorithm. Moreover, we also rigorously analyze the proposed techniques by conducting simulations and experiments in a real network of unmanned aerial vehicles (UAVs). / Thesis / Candidate in Philosophy

Bats have a unique flying style that allows them to be highly dexterous in capturing prey and have great freedom of movement in flight. Bats' wings have a wing membrane that is tensioned by their fingers and arms, allowing them to retract their wings laterally in flight. This distinct motion has allowed bats to be the only mammals capable of sustained flight, adding to their evolutionary uniqueness. This thesis presents the creation of the VALKRIE (Versatile Aerial Lifesize Kinetic Robot Inspired by bat Evolution) project: a to-scale simplified bat-inspired ornithopter that can be remotely controlled, sustain flight, and passively retract and extend its wings laterally. VALKRIE mimics the dimensions and size of its biological counterpart, Hipposideros diadema, a medium-sized bat; setting its aerodynamical constraints to the dimensions of Hipposideros diadema. Bats' maneuverability is derived from their unique wing motion while in flight, retracting and extending their wings. VALKRIE mimics this motion by simplifying the joint structure of a bat's wing and passively retracting and extending the wings. By simplifying the complex anatomy of bat wing motion, VALKRIE can maintain flight and generate sufficient lift for increasing altitude. With a simplified design, VALKRIE only has two motors that actuate wing flapping, wing retraction, and rotation of the hind legs. With this simplified design, the operator can remotely control VALKRIE by increasing and decreasing the wingbeat frequency and steering to the right and left with the hind legs. / Master of Science / Bats have a unique flying style that allows them to be highly dexterous in capturing prey and have great freedom of movement in flight. Bats' wings have a wing membrane that is tensioned by their fingers and arms, allowing them to retract their wings laterally in flight. This distinct motion has allowed bats to be the only mammals capable of sustained flight, adding to their evolutionary uniqueness. This thesis presents the creation of the VALKRIE (Versatile Aerial Lifesize Kinetic Robot Inspired by bat Evolution) project: a to-scale simplified bat-inspired ornithopter that can be remotely controlled, sustain flight, and passively retract and extend its wings laterally. VALKRIE mimics the dimensions and size of its biological counterpart, Hipposideros diadema, a medium-sized bat; setting its aerodynamical constraints to the dimensions of Hipposideros diadema. Bats' maneuverability is derived from their unique wing motion while in flight, retracting and extending their wings. VALKRIE mimics this motion by simplifying the joint structure of a bat's wing and passively retracting and extending the wings. By simplifying the complex anatomy of bat wing motion, VALKRIE can maintain flight and generate sufficient lift for increasing altitude. With a simplified design, VALKRIE only has two motors that actuate wing flapping, wing retraction, and rotation of the hind legs. With this simplified design, the operator can remotely control VALKRIE by increasing and decreasing the wingbeat frequency and steering to the right and left with the hind legs.

Multi-robot systems (MRS) offer many performance benefits over single robots for tasks that can be completed by one robot. They offer potential redundancies to the system to improve robustness and allow tasks to be completed in parallel. These benefits, however, can be quickly offset by losses in productivity from diminishing returns caused by interference between robots and communication problems. This dissertation developed and evaluated MRS control architectures to solve the dynamic multi-robot autonomous routing problem. Dynamic multi-robot autonomous routing requires robots to complete a trip from their initial location at the time of task allocation to an assigned destination. The primary concern for the control architectures was how well the communication requirements and overall system performance scaled as the number of robots in the MRS got larger. The primary metrics for evaluation of the controller were the effective robot usage rate and the bandwidth usage. This dissertation evaluated several different approaches to solving dynamic multi-robot autonomous routing. The first three methods were based off of common MRS coordination approaches from previous research. These three control architectures with distributed control without communication (a swarm-like method), distributed control with communication, and centralized control. An additional architecture was developed to solve the problem in a way that scales better as the number of robots increase. This architecture, mixed mode autonomy, combines the strengths of distributed control with communication and centralized control. Like distributed control with communication, mixed mode autonomy's performance degrades gracefully with communication failures and is not dependent on a single controller. Like centralized control, there is oversight from a central controller to ensure repeatable high performance of the system. Each of the controllers other than distributed control without communication is based on building world models to facilitate coordination of the routes. A second variant of mixed mode autonomy was developed to allow robots to share parts of their world models with their peers when their models were incomplete or outdated. The system performance was evaluated for three example applications that represent different cases of dynamic multi-robot autonomous routing. These example applications were the automation of open pit mines, container terminals, and warehouses. The effective robot usage rates for mixed mode autonomy were generally significantly higher than the other controllers with a higher numbers of robots. The bandwidth usage was also much lower. These performance trends were also observed across a wide range of operating conditions for dynamic multi-robot autonomous routing. The original contributions from this work were the development of a new MRS control architecture, development of system model for the dynamic multi-robot autonomous routing problem, and identification of the tradeoffs for MRS design for the dynamic multi-robot autonomous routing problem. / Ph. D.

Rugged terrains, including much of the earth's surface, other planets, and many man-made structures, are inaccessible to wheeled and tracked vehicles. This has inspired research into legged vehicles. Prior to the research described here, virtually all legged vehicle designs relied on the concept of mounting actuated leg-type mechanisms onto a single rigid frame or chassis. This dissertation research explores and advances a novel vehicle concept that uses passive legs attached to an actuated multibody structure. This new vehicle moves only its actuated body trunk to achieve locomotion; thus moving in a manner similar to that used by insect larvae known as caterpillars. The passive-legged design is termed a "crawling" vehicle, to differentiate it from "walking" vehicles, which have powered legs. A conceptual design for the proposed vehicle was developed using insights from observations of caterpillar specimen geometry, gaits, leg trajectories, and ranges of motion. The flexible, segmented body of the robot is realized using a series of actuated truss-like mechanisms, resulting in a configuration similar to the body structure of caterpillars. A computer simulation was developed to verify the concept and to assist in creating future designs. This simulation includes a parametric model of the robot structure, an efficient kinematics model, a motion programming method based on six-dimensional parametric cubic trajectories, static stability analysis, actuator velocity and acceleration analysis, wire-frame animations, and rendering, thus providing synthesis and analysis tools for this new class of vehicle. Results of this work show that by using properly designed Stewart-Gough platform mechanisms for the vehicle multibody structure, a range of motion very similar to that of caterpillars is achievable. Simulation tests showed that imitating the caterpillars" primary gait (or stepping sequence) yields superior speed and efficiency, with little reduction of stability, when compared to a simpler, more obvious gait. With proper controls, this crawling vehicle will, like its biological counterpart, be intrinsically stable and have excellent maneuverability over rough terrain. The crawling vehicle is shown to be a viable legged locomotion system that may prove to have superior rough terrain mobility to all previous types of man-made land vehicles. / Ph. D.

Bats are famous for their capability of navigating in dense forests for hundreds of kilometers within one night by using their sonar system. Airborne sonar hasn't been heavily used in the industrial world compared to other sensors such as lidar, radar, and cameras. In this study, we applied a biosonar robot to navigate in a dense forest with bat-like FM-CF ultrasonic signals with deep learning. The results presented show that airborne biosonar can classify different areas' plants, in addition to achieving a similar level of navigation granularity compared to GPS, which is about 6 meters of radius resolution. The time- frequency representations of echoes from the forest are used as input data to explore the biosonar navigation ability, and the state-of-the-art CNN deep network (Resnet 152) is used as the brain to do the echolocation in the dense forest. The navigation ability can be improved significantly by combining multiple 10 ms long echoes, however, the data size of the reflected waves is much smaller than the other popularly used sensors, as echo can be collected at a rate of 40 echoes per second. The results can prove that airborne sonar can be used to navigate in GPS-denied environments, and can be an important sensor used in a scenario when other sensors meet constraints, like in the sensor fusion applications. / Doctor of Philosophy / The ability to identify natural landmarks could contribute to the navigation skills of echolo- cating bats and also advance the quest for autonomy in natural environments with man- made systems. The critical sensors used in autonomous robot navigation are camera array, radar, and lidar, airborne sonar hasn't been verified for its navigation efficiency. However, recognizing natural landmarks based on biosonar echoes has to deal with the unpredictable nature of echoes that are typically superpositions of contributions from many different reflec- tors with unknown properties. This dissertation intends to explore the bioinspired airborne sonar navigation ability in dense natural forests. The first part of this project is to use reflected echoes to navigate on a large scale, data was collected from different mountains which are dozens of kilometers away from each other, and we achieved the use of one single navigator in those locations. The second part is to explore the navigation granularity of airborne sonar sensors, data were collected from a small dense forest area, we try to classify which part of the foliage was based on the echo, and in the end, we achieved GPS accuracy for navigation. The finding in this work proves that the sonar sensor can play an important role in the sensing system, with the help of a deep neural network, with a 10 ms long echo, it can have a similar navigation ability to GPS.

Traditionally, underwater vehicles have been using propellers for locomotion but they are not only inefficient but generate large acoustic signature. Researchers have taken inspiration from efficient swimmers like fish to address the issue with alternate propulsion mechanism. Mostly, research on fish locomotion involved studying a foil tethered to a fixed point inside uniform flow. A major drawback of such study is that neither it resembles a freely swimming fish nor it takes into consideration the dynamics of moving fish on propulsive forces. Hence, in our current study, we focus on comparing the performance of a free swimming fin over tethered fin both experimentally and numerically. Experimentally, we focus on the oscillatory form of locomotion where the caudal fin pitches to generate necessary thrust as seen in boxfish. We intend to investigate the Caudal fin kinematics and its physical properties on locomotion performance. To better understand, we build an automated robo-physical model that swims in a circular path so as to carry extensive experiments. We focus on understanding the effect of flexibility, shape and thickness of caudal fin on performance. Currently, we have studied three different flexibility and for each flexibility, we studied three different shape. We found there must be an optimal flexibility for minimising the Cost of Transport (COT). We also propose that the steady forward speed linearly varies with tail tip velocity. Furthermore, we investigated the effect of thickness of fin and considered uniform and tapered fin with equal area moment of inertia. Numerically, we investigated the effect of phase offset between heave and pitch motion on the performance of a freely swimming fin and compared that to a tethered fin. A freely-swimming fin self propels and moves with steady speed while a tethered fin remains stationary and actuates under uniform flow. We model the fin as a rigid body undergoing prescribed motion in an inviscid fluid and solved for coupled interaction using panel method. We show the effect of phase offset for optimum performance and found a significant difference between tethered and freely swimming fin. / M.S. / Underwater vehicles use propeller based mechanism but they are inefficient and generate noise. Researchers have taken inspiration from nature to replace propellers with efficient propulsion mechanism. In the current study, we design a robotic model to understand the effect of various kinematic and physical properties of tail fin on performance. Our research is unique from past study in the aspect that most research involved studying performance using a robotic model fixed at its position which does not resemble a freely-swimming fish. Hence, in our current study, we focus on comparing the performance of our freely swimming model with tethered fin. The robot has one degree of freedom and can pitch its tail to generate thrust. We intend to investigate the tail fin kinematics and its physical properties on locomotion performance. We focus on understanding the effect of flexibility, shape and thickness of fin on performance. Currently, we have studied three different flexibility and for each flexibility, we studied three different shape. We showed there exists an optimal flexibility for maximising efficiency. For any fin undergoing combined pitch and heave motion, there exists a phase offset between them which will maximise the performance. Researchers have tried to understand its impact using both experiment and numerical simulation. In the current study, we study the impact of phase offset between pitch and heave for a freely-swimming fin and compare that to a fixed fin. A freely-swimming fin self propels and moves with steady speed while a tethered fin remains stationary and actuates under uniform flow. We show the effect of phase offset for optimum performance and found a significant difference between tethered and freely swimming fin.

Instability of humanoid robots is a common problem, especially given external disturbances or difficult terrain. Even with the robustness of most whole body controllers, instability is inevitable given the right conditions. When these unstable events occur they can result in costly damage to the robot potentially causing a cease of normal functionality. Therefore, it is important to study and develop methods to control a humanoid robot during a fall to reduce the chance of critical damage. This thesis proposes joint angular velocity strategies to reduce the impact velocity resulting from a lateral, backward, or forward fall. These strategies were used on two and three link reduced order models to simulate a fall from standing height of a humanoid robot. The results of these simulations were then used on a full degree of freedom robot, Viginia Tech's humanoid robot ESCHER, to validate the efficacy of these strategies. By using angular velocity strategies for the knee and waist joint, the reduced order models resulted in a decrease in impact velocity of the center of mass by 58%, 87%, and 74% for a lateral, backward, and forward fall respectively in comparison to a rigid fall using the same initial conditions. Best case angular velocity strategies were then developed for various initial conditions for each falling direction. Finally, these parameters were implemented on the full degree of freedom robot which showed results similar to those of the reduced order models. / Master of Science / Instability of humanoid robots is a common problem, especially given external disturbances or difficult terrain. Even with the robustness of most whole body controllers, instability is inevitable given the right conditions. When these unstable events occur they can result in costly damage to the robot potentially causing a cease of normal functionality. Therefore, it is important to study and develop methods to control a humanoid robot during a fall to reduce the chance of critical damage. This thesis proposes strategies that rotate the joints at a constant rate to reduce damage resulting from a lateral, backward, or forward fall. These strategies were used on two and three link simplistic models to simulate a fall from standing height of a humanoid robot. The results of these simulations were then used on a full robot, Viginia Tech’s humanoid robot ESCHER, to validate the efficacy of these strategies. By constant joint rotation strategies for the knee and waist joint, the simplistic models resulted in a decrease in impact velocity of the center of mass by 58%, 87%, and 74% for a lateral, backward, and forward fall respectively in comparison to a rigid fall using the same initial conditions. Best case joint rotation strategies were then developed for various initial conditions for each falling direction. Finally, these parameters were implemented on the full robot which showed results similar to those of the reduced order models.

In this thesis, we explore methods to enable assistive robotic arms mounted on wheelchairs to assist disabled users with their daily activities. To effectively aid users, these robots must recognize a variety of tasks and provide intuitive control mechanisms. We focus on developing techniques that allow these assistive robots to learn diverse tasks, manipulate different types of objects, and simplify user control of these complex, high-dimensional systems. This thesis is structured around three key contributions. First, we introduce a method for assistive robots to autonomously learn complex, high-dimensional behaviors in a given environment and map them to a low-dimensional joystick interface without human demonstrations. Through controlled experiments and a user study, we show that this approach outperforms systems based on human-demonstrated actions, leading to faster task completion compared to industry-standard baselines. Second, we improve the efficiency of reinforcement learning for robotic manipulation tasks by introducing a waypoint-based algorithm. This approach frames task learning as a sequence of multi-armed bandit problems, where each bandit problem corresponds to a waypoint in the robot's trajectory. We introduce an approximate posterior sampling solution that builds the robot's motion one waypoint at a time. Our simulations and real-world experiments show that this approach achieves faster learning than state-of-the-art baselines. Finally, to address the challenge of manipulating a variety of objects, we introduce RIgid-SOft (RISO) grippers that combine soft-switchable adhesives with standard rigid grippers and propose a shared control framework that automates part of the grasping process. The RISO grippers allow users to manipulate objects using either rigid or soft grasps, depending on the task. Our user study reveals that, with the shared control framework and RISO grippers, users were able to grasp and manipulate a wide range of household objects effectively. The findings from this research emphasize the importance of integrating advanced learning algorithms and control strategies to improve the capabilities of assistive robots in helping users with their daily activities. By exploring different directions within the domain of assistive robotics, this thesis contributes to the development of methods that enhance the overall functionality of assistive robotic arms. / Master of Science / In this thesis, we explore ways to make robotic arms attached to wheelchairs more helpful for people with disabilities in their everyday lives. To be truly useful, these robots need to understand a variety of tasks and be easy for users to control. Our focus is on developing techniques that help these robots learn different tasks, handle different types of objects, and make controlling them simpler. The thesis is built around three main contributions. First, we introduce a way for robots to learn how to perform complex tasks on their own and then simplify controlling robots for those tasks so users can control the robot to perform different tasks using just a joystick. We show through experiments that this approach helps people complete tasks faster than systems that rely on human-taught actions. Second, we improve how robots learn to perform tasks using a more efficient learning method. This method breaks down tasks into smaller steps, and the robot learns how to move toward each step more quickly. Our tests show that this approach speeds up the learning process compared to other methods. Finally, we address the challenge of handling different types of objects by developing a new type of robotic gripper that combines soft and rigid gripping options. This gripper allows users to pick up and manipulate a wide variety of household objects more easily, thanks to a control system that helps automate part of the process. In our user study, people found it easier to use the new gripper to handle different items. Overall, this research highlights the importance of combining learning algorithms and userfriendly controls to make assistive robots better at helping people with their daily tasks. These contributions advance the development of robotic arms that can more effectively assist users.

Painting still plays a fundamental role in communication nowadays. For example, the paint on the road, called road surface marking, guides the traffic in order and maintains the high efficiency of the entire modern traffic system. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, the mechanical and electronic design of a Painting Robot was discussed. The overall design was to fulfill the requirements of the line painting. Computer vision techniques were applied to this thesis since the camera was selected as the major sensor of the robot. Advanced control theory was introduced to this thesis as well. Three different controllers were developed. The Proportional-Integral (PI) controller with an anti-windup feature was designed to overcome the drawbacks of the traditional PI controller. Model Reference Adaptive Control (MRAC) was introduced into this thesis to deal with the uncertainties of the system. At last, the hybrid PI-MRAC controller was implemented to maintain the advantages of both PI and MRAC approaches. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot. / Master of Science / Painting still plays a fundamental role in communication nowadays. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, a Painting Robot with a two-camera system was designed. Computer vision techniques and advanced control theory were introduced into this thesis. Three different controllers were developed, including Proportional-Integral (PI) with an anti-windup feature, Model Reference Adaptive Control (MRAC) and the hybrid PI-MRAC. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot.

An industrial vision system is a useful and often integral part of a computer integrated manufacturing system. Successful integration of vision capabilities into a manufacturing system involves extracting from image data the information which has meaning to the task at hand, and communicating that information to the larger system. The goal of this research was to integrate the activities of a stand-alone vision system into the operation of a manufacturing system; more specifically, the host controller and vision system were expected to work together to determine the status of pallets moving through the system. Pallet status was based on whether the objects on the pallet were correct in shape, location, and orientation, as compared to a pallet model generated using the microcomputer-based CADKEY CAD program. Cadd.c, a C language program developed for this research, extracts object area, perimeter, centroid, and principal angle from the CAD KE Y model for comparison to counterparts generated by the vision system. This off-line approach to supplying known parameters to the vision system was chosen over the traditional "teach by showing" method to take advantage of existing CAD data and to avoid disruption of the production system. The actual comparison of model and image data was performed by a program written in VPL, the resident language of the GE Optomation II Vision System. The comparison program relies on another short VPL program to obtain a pixel/inch ratio which equates the disparate units of the two systems. Model parameters are passed to the vision system via hardware and software links developed as part of this research. Three C language programs enable the host computer to communicate commands and parameters, and receive program results from the vision system. Preliminary testing of the system revealed that the object location and surface texture, lighting conditions, and pallet background all affected the image parameter calculations and hence the comparison process. / Master of Science