abstract: As robotic systems are used in increasingly diverse applications, the interaction of humans and robots has become an important area of research. In many of the applications of physical human robot interaction (pHRI), the robot and the human can be seen as cooperating to complete a task with some object of interest. Often these applications are in unstructured environments where many paths can accomplish the goal. This creates a need for the ability to communicate a preferred direction of motion between both participants in order to move in coordinated way. This communication method should be bidirectional to be able to fully utilize both the robot and human capabilities. Moreover, often in cooperative tasks between two humans, one human will operate as the leader of the task and the other as the follower. These roles may switch during the task as needed. The need for communication extends into this area of leader-follower switching. Furthermore, not only is there a need to communicate the desire to switch roles but also to control this switching process. Impedance control has been used as a way of dealing with some of the complexities of pHRI. For this investigation, it was examined if impedance control can be utilized as a way of communicating a preferred direction between humans and robots. The first set of experiments tested to see if a human could detect a preferred direction of a robot by grasping and moving an object coupled to the robot. The second set tested the reverse case if the robot could detect the preferred direction of the human. The ability to detect the preferred direction was shown to be up to 99% effective. Using these results, a control method to allow a human and robot to switch leader and follower roles during a cooperative task was implemented and tested. This method proved successful 84% of the time. This control method was refined using adaptive control resulting in lower interaction forces and a success rate of 95%. / Dissertation/Thesis / M.S. Mechanical Engineering 2014

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.

Ageing is linked to a decrease in mobility, which affects the quality of life of many elderly individuals. This is a growing challenge in industrialised societies since the proportion of elderly individuals is becoming larger. One potential solution that would keep these individuals active and independent is the use of mobility assist devices. These devices are designed to reduce the energy demand of the user with the use of electric motors providing torques at joints of the lower limb. Although promising, these devices have a problem: they become uncomfortable after prolonged usage. This is especially true for devices designed to produce substantial assistance. The research goal consisted of quantifying the performance of the physical interfaces, or points of attachments, of an experimental device with multiple interface adjustments. The device was fabricated with design criteria similar to active assist devices to simulate the mechanical behaviour of these particular devices. This analysis provided design recommendations that could ultimately enhance the performance of assist devices available on the market and thus the quality of life of many individuals. This research used force mapping and motion capture to quantify the kinetic and the kinematic compatibility of the device. Experimental results have shown that the position, shape and other parameters of the interfaces had an effect on the relative movement of the brace, or the brace performance. The device interface migration was greater when the interfaces were positioned furthest away form the joint. An increasing level of assistance showed more relative movement between the brace and the user. Interface geometry had a noticeable effect on force distribution over the interface. The results and methodology of this research offers an in depth understanding of the mechanical behavior of the physical interfaces of the developed assist device. Nevertheless, further research and development in the field of human machine interactions are needed in order to develop a physical human-machine interface that will ensure the success of powered assist devices in the future.

Les robots humanoïdes sont les plus appropriés pour travailler en coopération avec l'homme. En effet, puisque les humains sont naturellement habitués à collaborer entre eux, un robot avec des capacités sensorielles et de locomotion semblables aux leurs, sera le plus adapté. Cette thèse vise à rendre les robot humanoïdes capables d'aider l'homme, afin de concevoir des 'humanoïdes collaboratifs'. On considère ici la tâche de transport collaboratif d'objets. D'abord, on montre comment l'utilisation simultanée de vision et de données haptiques peut améliorer la collaboration. Une stratégie combinant asservissement visuel et commande en admittance est proposée, puis validée dans un scénario de transport collaboratif homme/humanoïde.Ensuite, on présente un algorithme de génération de marche, prenant intrinsèquement en compte la collaboration physique. Cet algorithme peut être spécifié suivant que le robot guide (leader) ou soit guidé (follower) lors de la tâche. Enfin, on montre comment le transport collaboratif d'objets peut être réalisé dans le cadre d'un schéma de commande optimale pour le corps complet. / Humanoid robots provide many advantages when working together with humans to perform various tasks. Since humans in general have alot of experience in physically collaborating with each other, a humanoid with a similar range of motion and sensing has the potential to do the same.This thesis is focused on enabling humanoids that can do such tasks together withhumans: collaborative humanoids. In particular, we use the example where a humanoid and a human collaboratively carry and transport objectstogether. However, there is much to be done in order to achieve this. Here, we first focus on utilizing vision and haptic information together forenabling better collaboration. More specifically the use of vision-based control together with admittance control is tested as a framework forenabling the humanoid to better collaborate by having its own notion of the task. Next, we detail how walking pattern generators can be designedtaking into account physical collaboration. For this, we create leader and follower type walking pattern generators. Finally,the task of collaboratively carrying an object together with a human is broken down and implemented within an optimization-based whole-bodycontrol framework.