Traditional robots, while capable of being efficient and effective for the task they were designed, are dangerous when operating in unmodeled environments or around humans. The field of soft robotics attempts to increase the safety of robots thus enabling them to operate in environments where traditional robots should not operate. Because of this, soft robots were developed with different goals in mind than traditional robots and as such the traditional metrics used to evaluate standard robots are not effective for evaluating soft robots. New metrics need to be developed for soft robots so that effective comparison and evaluations can be made. This dissertation attempts to lay the groundwork for that process through a survey on soft robot metrics. Additionally we propose six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. New models, control methods and estimation methods also need to be developed for soft robots. Many of the traditional methods and assumptions for modeling and controlling robotic systems are not able to provide the fidelity that is needed for soft robots to effectively complete useful tasks. This dissertation presents specific developments in each of these areas of soft robot metrics, modeling, control and estimation. We show several incremental improvements to soft robot dynamic models as well as how they were used in control methods for more precise control. We also demonstrate a method for linearizing high degree of freedom models so it can be simplified for use in faster control methods for better performance. Lastly, we present an improved continuum joint configuration estimation method that uses a linear combination of length measurements. All these developments combine to help build the "fundamental engineering framework" that is needed for soft robotics as well as helping to move robots out of their confined spaces and bring them into new unmodeled/unstructured environments.

Despite the existence of robots that are capable of lifting heavy loads, robotic assistants that can help people move objects as part of a team are not available. This is because of a lack of critical intelligence that results in inefficient and ineffective performance of these robots. This work makes progress towards improved intelligence of robotic lifting assistants by studying human-human teams in order to understand basic principles of co-manipulation teamwork. The effect of modus, or the manner in which a team moves an object together, is the primary study of this work. Data was collected from over 30 human-human trials in which participants in teams of two co-manipulated an object that weighed 60 pounds. These participants maneuvered through a series of five obstacles while carrying the object, exhibiting one of four modi at any given time. The raw data from these experiments was cleaned and distilled into a pose trajectory, velocity trajectory, acceleration trajectory, and interaction wrench trajectory. Classifying on the original base set of four modi with a neural net showed that two of the three modi were very similar, such that classification between three modi was more appropriate. The three modi used in classification were \emph{quickly}, \emph{smoothly} and \emph{avoiding obstacles}. Using a convolutional neural net, three modi were able to be classified from a validation set with up to 85\% accuracy. Detecting modus has the potential to greatly improve human-robot co-manipulation by providing a means to determine an appropriate robot behavior objective function. Survey data showed that participants trust each other more after working together and that they feel that their partners are more qualified after they worked together. A number of modified scales were also shown to be reliable which will allow future researchers in human-robot co-manipulation to properly evaluate how humans feel about working with each other. These same scales will also provide a useful comparison to human-robot teams in order to determine how much humans trust robots as co-manipulation team members.

The frequent occurrences of natural and technological disasters have incurred grave loss of life and damage to property. For mitigating the miserable aftermaths, multi-robot teleoperation systems have been developed and deployed to cooperate with human rescuers in post-earthquake scenarios, and to sample, monitor and clean pollutants in marine environments. With a bidirectional communication channel, human users can deliver commands/requests to guide the motions of the remote robots, and can receive visual/audio feedback to supervise the status of the remote environment, throughout multi-robot teleoperation. Furthermore, the remote robots can send force feedback to human operators to improve their situational awareness and task performance. This way, a closed-loop multi-robot teleoperation system becomes bilateral in which coordinated robots physically interact and exchange energy with human users, and hence needs to be rendered passive for safe human-robot interaction. Beyond guaranteeing closed-loop passivity, the control of a bilateral multi-robot teleoperation system faces two challenging problems: preserving the communication connectivity of the remote robots; and dispatching the teleoperation authority to multiple human users. Because wireless transmission of radio/acoustic signals between the remote robots is constrained by their distances, bilateral multi-robot teleoperation control must coordinate the motions of the remote robots appropriately so as to maintain their communication network connected. Further, multiple human users can send possibly conflicting teleoperation commands to the remote robots, a distributed authority dispatch algorithm is thus needed for the remote robot network to recognize and follow the most urgent user commands at runtime. This thesis develops an energy shaping strategy to preserve the connectivity of the remote robots, and to dispatch control authority over the remote robots to human users, during bilateral multi-robot teleoperation. Chapter 1 introduces the application background of multi-robot teleoperation as well as the state-of-the-art development in related research areas. In Chapter 2, a dynamic interconnection and damping strategy is proposed to reduce and constrain the position error between the local and remote robots to any prescribed bound during bilateral teleoperation. Chapter 3 derives a gradient plus damping control from a bounded potential function and then unifies it into an indirect coupling framework to preserve all communication links of an autonomous multi-robot system with time-varying delays and bounded actuation. On these bases, Chapter 4 develops a dynamic feedforward-feedback passivation strategy to preserve all communication links and thus the connectivity of the tree network of the remote robots while rendering the bilateral multi-robot teleoperation close loop passive. Specifically, by blending the sliding variable in Chapter 2 with the bounded potential function in Chapter 3, the dynamic passivation strategy decomposes the dynamics of the remote robots into a power-preserving interconnection of two subsystems, and regulates the energy behaviour of each subsystem to preserve the tree communication connectivity of the remote robots. To handle time-varying communication delays, the strategy further transforms the communication channels between the local and remote robots into a dynamic controller for passivating bilateral teleoperation. Superior to existing controls, the strategy using a bounded potential function can circumvent numerical instability, reduce noise sensitivity and facilitate future extensions to accommodate robot actuator saturation. On the other side, Chapter 5 designs a distributed and exponentially convergent winners-take-all authority dispatch algorithm that activates the teleoperation of only human users with the most urgent requests in real time. After formulating the problem as a constrained quadratic program, we employ an exact penalty function method to construct a distributed primal-dual dynamical system that can solve the problem at an exponential rate. Because the equilibrium of the system changes with user requests, we then interconnect the dynamical system with physical robot dynamics in a power-preserving way, and passivate closed-loop multi-robot teleoperation using multiple storage functions from a switched system perspective. Finally, Chapter 6 provides some conclusive remarks and two problems regarding connectivity preservation and authority dispatch for future study. / Graduate

In this thesis, a kinematic model for controlling an Omni-directional 3 DOF DDR with an external navigation point is presented. Two different dynamic models for investigating the resulting torque on the three active motors on the robot are also developed and validated. The focus of the thesis is on the design of kinematic and dynamic models in an ideal environment and the kinematic model in a high fidelity environment. The kinematic model uses inverse kinematics to translate the controlling motion reference from the external navigation point to the three active motors on the DDR. The thesis also includes a comparison of the two different dynamic models based on Kane's method and Newton's second law of motion, respectively. The models presented in this thesis could aid autonomous robots with attached payloads such as hospital beds move with the centre of geometry as a focal point and thereby perform movements. The results show that such a kinematic model for controlling the specific robot is shown to be feasible in an ideal environment. However, due to PID controllers for the active wheel motors not being exact enough, the model in the high fidelity environment does not perform correctly in all cases. Furthermore, the dynamic model results provide an understanding of the difference of torque dependant on the distance to the navigation point.

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In strategically rich settings in which machines and people do not fully share the same preferences, machines must learn to cooperate and compromise with people to establish mutually successful relationships. However, designing machines that effectively cooperate with people in these settings is difficult due to a variety of technical and psychological challenges. To better understand these challenges, we conducted a series of user studies in which we investigated human-human, robot-robot, and human-robot cooperation in a simple, yet strategically rich, resource-sharing scenario called the Block Dilemma, a game in which players must balance fairness, efficiency, and risk. While both human-human and robot-robot pairs typically learn fair and cooperative solutions over time, our results show that these solutions tend to be different when communication is permitted versus when it is not. While people followed a less risky and less efficient solution, pairs of robots followed a more risky but more efficient solution. This difference in humans’ and machines’ behavior appears to influence human-robot cooperation negatively, as our studies show that human-robot pairs did not frequently produce either form of cooperation without communication. These results speak to the need for machine behavior to be better aligned with human behavior. While machines may behave more efficiently and produce better results than people when following their own calculations, machines may often better facilitate human-machine cooperation by aligning their behavior with human behavior rather than expecting human behavior to become more efficient.

This thesis describes the design of a robotic inspection tool which is based on a differential track-drive platform. The robotic inspection tool is a one man-portable UGV that has been developed for the purpose of non-destructive evaluation (NDE) and internal inspection of environments where human penetration may be difficult or hazardous. Various NDE and sensing techniques are described in this paper but the focus is on the mechanical and electrical design of the platform itself. The platform is a versatile device for mobile robotics research and development that supports a wide variety of instrumentation and payloads. Variable height control of the payload is achieved with a scissor lift assembly that provides accurate positioning of equipped sensors and instrumentation. The architecture of the platform was designed to support future autonomous implementations. / Master of Science

In recent years, the field of Soft Robotics has grown exponentially resulting in a variety of different soft robot designs. A majority of the current soft robots can easily be split into two distinct categories: Navigation and Grasping. Navigation robots alter their body orientation to navigate around an environment. Grasping robots are designed to grasp a variety of unknown objects without damaging said object. However, only a few robots are able to demonstrate both aspects and even fewer robots are able to do both simultaneously. As thus, the goal of this thesis is to create a soft robot that is able to pick up and support an additional payload. This thesis will explore the challenges and difficulties that come with designing such a robot. For this thesis, we chose to simplify the manufacturing process making it easy to create and test different designs. We primarily used Pneumatic Network actuators for the majority of the soft robot. This allowed us to use a layered manufacturing approach to create the full robot. Finally, we split the robot into two main components which have their own purpose, which made it easy to test and design each component. Attached to this thesis are three different supplementary videos. The first one labeled "Walking Gaits" demonstrate how the robot is capable of moving forward. This video is comprised of several sections showing the full robot moving, just the base moving, and the full robot briefly moving as it supports a payload. The second video is labeled "Additional Walking". This video shows how the base can effectively move around a given environment. The final video if called "Grasping Method" which demonstrates the different grasping methods that the full robot uses to pick up objects. / 2021-06-03T00:00:00Z

L'interaction homme-robot arrive dans une phase intéressante ou la relation entre un homme et un robot est envisage comme 'un partenariat plutôt que comme une simple relation maitre-esclave. Pour que cela devienne une réalité, le robot a besoin de comprendre le comportement humain. Il ne lui suffit pas de réagir de manière appropriée, il lui faut également être socialement proactif. Pour que ce comportement puis être mise en pratique le roboticien doit s'inspirer de la littérature déjà riche en sciences sociocognitives chez l'homme. Dans ce travail, nous allons identifier les éléments clés d'une telle interaction dans le contexte d'une tâche commune, avec un accent particulier sur la façon dont l'homme doit collaborer pour réaliser avec succès une action commune. Nous allons montrer l'application de ces éléments au cas un système robotique afin d'enrichir les interactions sociales homme-robot pour la prise de décision. A cet égard, une contribution a la gestion du but de haut niveau de robot et le comportement proactif est montre. La description d'un modèle décisionnel d'collaboration pour une tâche collaboratif avec l'humain est donnée. Ainsi, l'étude de l'interaction homme robot montre l'intéret de bien choisir le moment d'une action de communication lors des activités conjointes avec l'humain / Human Robot Interaction is entering into the interesting phase where the relationship with a robot is envisioned more as one of companionship with the human partner than a mere master-slave relationship. For this to become a reality, the robot needs to understand human behavior and not only react appropriately but also be socially proactive. A Companion Robot will also need to collaborate with the human in his daily life and will require a reasoning mechanism to manage thecollaboration and also handle the uncertainty in the human intention to engage and collaborate. In this work, we will identify key elements of such interaction in the context of a collaborative activity, with special focus on how humans successfully collaborate to achieve a joint action. We will show application of these elements in a robotic system to enrich its social human robot interaction aspect of decision making. In this respect, we provide a contribution to managing robot high-level goals and proactive behavior and a description of a coactivity decision model for collaborative human robot task. Also, a HRI user study demonstrates the importance of timing a verbal communication in a proactive human robot joint action

Worldwide, about 35.7 million people were estimated to be affected by dementia in 2010. One way to treat dementia is by exercising, but human trainers are few and expensive. Robots can be mass produced and work at anytime of the day. This report describes research done for developing a robot exercise coach intended for treating dementia. Three main problems for people with dementia were identified: memory, attention and motivation. By using computer vision the robot can help count repetitions, grade exercise correctness and make sure that the user is still paying attention. The Kinect was used for skeleton tracking to count repetitions and provide video. For motivation, motivational models and flow theory were used to design the users interaction with the robot and make it more enjoyable and engaging. Feedback was believed to be an important part of this interaction. To provide extra feedback skeleton tracking was turned into the robot mimicking the user. To test which combination of feedback and interaction was most enjoyable, a user study was done. The user study consisted of 11 subjects, each interacting with three different systems, each system with varying levels of feedback. After interacting, the subject filled out a survey and had an interview. The results from the user study showed evidence that repetition counting and exercise correctness feedback but no mimicking is the most enjoyable. With a statistically significant difference in regards to repetition counting at the 0.05 level. Younger people found the mimicking enjoyable but still preferred the system without it, and older people found it confusing. In future systems like this, repetition counting and exercise correctness feedback should be seen as important parts of the interaction.