Authority-Sharing Control of Assistive Robotic Walkers

A recognized consequence of population aging is a reduced level of mobility, which undermines the life quality of several senior citizens. A promising solution is represented by assisitive robotic walkers, combining the benefits of standard walkers (improved stability and physical support) with sensing and computing ability to guarantee cognitive support. In this context, classical robot control strategies designed for fully autonomous systems (such as fully autonomous vehicles, where the user is excluded from the loop) are clearly not suitable, since the user’s residual abilities must be exploited and practiced. Conversely, to guarantee safety even in the presence of user’s cognitive deficits, the responsibility of controlling the vehicle motion cannot be entirely left to the assisted person. The authority-sharing paradigm, where the control authority, i.e., the capability of controlling the vehicle motion, is shared between the human user and the control system, is a promising solution to this problem. This research develops control strategies for assistive robotic walkers based on authority-sharing: this way, we ensure that the walker provides the user only the help he/she needs for safe navigation. For instance, if the user requires just physical support to reach the restrooms, the robot acts as a standard rollator; however, if the user’s cognitive abilities are limited (e.g., the user does not remember where the restrooms are, or he/she does not recognize obstacles on the path), the robot also drives the user towards the proper corridors, by planning and following a safe path to the restrooms. The authority is allocated on the basis of an error metric, quantifying the distance between the current vehicle heading and the desired movement direction to perform the task. If the user is safely performing the task, he/she is endowed with control authority, so that his/her residual abilities are exploited. Conversely, if the user is not capable of safely solving the task (for instance, he/is going to collide with an obstacle), the robot intervenes by partially or totally taking the control authority to help the user and ensure his/her safety (for instance, avoiding the collision). We provide detailed control design and theoretical and simulative analyses of the proposed strategies. Moreover, extensive experimental validation shows that authority-sharing is a successful approach to guide a senior citizen, providing both comfort and safety. The most promising solutions include the use of haptic systems to suggest the user a proper behavior, and the modification of the perceived physical interaction of the user with the robot to gradually share the control authority using a variable stiffness vehicle handling

Unifying nonholonomic and holonomic behaviors in human locomotion

Our motivation is to understand human locomotion to better control locomotion of virtual systems (robots and mannequins). Human locomotion has been studied so far in different disciplines. We consider locomotion as the level of a body frame (in direction and orientation) instead of the complexity of many kinematic joints systems as other approaches. Our approach concentrates on the computational foundation of human locomotion. The ultimate goal is to find a model that explains the shape of human locomotion in space. To do that, we first base on the behavior of trajectories on the ground during intentional locomotion. When human walk, they put one foot in front of the other and consequently, the direction of motion is deduced by the body orientation. That’s what we called the nonholonomic behavior hypothesis. However, in the case of a sideward step, the body orientation is not coupled to the tangential direction of the trajectory, and the hypothesis is no longer validated. The behavior of locomotion becomes holonomic. The aim of this thesis is to distinguish these two behaviors and to exploit them in neuroscience, robotics and computer animation. The first part of the thesis is to determine the configurations of the holonomic behavior by an experimental protocol and an original analytical tool segmenting the nonholonomic and holonomic behaviors of any trajectory. In the second part, we present a model unifying nonholonomic and holonomic behaviors. This model combines three velocities generating human locomotion: forward, angular and lateral. The experimental data in the first part are used in an inverse optimal control approach to find a multi-objective function which produces calculated trajectories as those of natural human locomotion. The last part is the application that uses the two behaviors to synthesize human locomotion in computer animation. Each locomotion is characterized by three velocities and is therefore considered as a point in 3D control space (of three speeds). We collected a library that contains locomotions at different velocities - points in 3D space. These points are structured in a tetrahedra cloud. When a desired speed is given, it is projected into the 3D space and we find the corresponding tetrahedron that contains it. The new animation is interpolated by four locomotions corresponding to four vertices of the selected tetrahedron. We exhibit several animation scenarios on a virtual character

Social robot navigation in urban dynamic environments

Deploying mobile robots in social environments requires novel navigation algorithms which are capable of providing valid solutions in such challenging scenarios. The main objective of the present dissertation is to develop new robot navigation approaches able to solve in an intelligent way the navigation problem in urban settings while considering at the same time the interactions with pedestrians, similar to what people easily do with little attention. Before studying in depth navigation algorithms, this thesis focuses on prediction algorithms to provide a more detailed model of the scene. Understanding human motion in outdoor and indoor scenarios is an appealing requirement to characterize correctly urban settings. Urban environments consist essentially of static obstacles and people, which are treated as dynamic and highly uncertain obstacles. Accordingly, it is mandatory to calculate people's intentions in order to successfully build a human prediction model that generates the corresponding human trajectories and considers their interactions with the environment, such as other pedestrians, static obstacles or even robots. It is of great interest that service robots can navigate successfully in typical urban environments, which are dynamic and constrained. In addition, people's behavior should not be conditioned by the presence and the maneuvering of robots. To this end, the robot navigation should seek to minimize its impact on the environment, in our case, on people. This thesis proposes new robot navigation methods that contemplate the social interactions taking place in the scene. In order to procure more intelligence to the navigation algorithm, we propose to integrate seamlessly the human motion prediction information into a new robot planning approach. Real experimentation is essential for the validation of the navigation algorithms. As there are real people involved, we must validate the results in real settings since simulation environments have limitations. In this thesis, we have implemented all the prediction and navigation algorithms in our robotic platform and we have provided plenty of evaluations and testings of our algorithms in real settings.Ubicar robots móviles en entornos sociales requiere novedosos algoritmos de navegación que sean capaces de aportar soluciones válidas en éstos exigentes escenarios. El prinicipal objetivo de la presente disertación es el de desarrollar nuevas soluciones para la navegación de robots que sean capaces de resolver, de una manera más inteligente, los problemas de navegación en emplazamientos urbanos, a la vez que se consideran las interacciones con los transeúntes de manera similar a lo que la gente hace fácilmente prestando poca atención. Antes de estudiar en profundidad los algoritmos de navegación, esta tesis se centra en los algoritmos de predicción para proporcionar un modelo más detallado de la escena. Entender el movimiento humando en entornos exteriores e interiores es un requerimiento deseable para caracterizar correctamente emplazamientos urbanos. Los entornos urbanos están consistituídos por muchos objetos dinámicos y altamente impredecibles, la gente. Por lo tanto, es obligatorio calcular las intenciones de la gente para constriur de manera exitosa un modelo de predicción humano que genere las correspondientes trayectorias humanas y considere sus interacciones con el entorno, como otros peatones, obstáculos estáticos o incluso robots. Es de gran interés que los robots de servicios puedan navegar correctamente en entornos típicamente urbanos, que son dinámicos y acotados, además de que el comportamiento de las personas no debería estar condicionado por la presencia y las maniobras de los robots. Con este fin, la navegación de robots debe buscar minimizar su impacto al entorno, en nuestro caso, a la gente. Esta tesis propone nuevos métodos para la navegación de robots que contemplen las interacciones sociales que suceden en la escena. Para proporcionar una navegación más inteligente, proponemos integrar de manera suave el algoritmo de predicción del movimiento humano con un nuevo enfoque de planificación de trayectorias. La experimentación real es esencial para la validación de los algoritmos de navegación. Ya que hay personas reales implicadas, debemos validar los resultados en emplazamientos reales porque el entorno de simulación tiene limitaciones. En esta tesis hemos implementado todos los algoritmos de predicción y de navegación en la plataforma robótica y hemos proporcionado multitud de evaluaciones y pruebas de nuestros algoritmos en entornos reales

Performance measures and control laws for active and semi-active suspensions

This thesis concentrates on two competing performance requirements of general suspension systems: "smoothness" and tracking. The focus of the thesis is on real-time feedback controls which can be applied in microprocessors with relatively limited capacity. Evolutionary algorithms (EAs) are used as a tool in the investigation of a wide range of control algorithms. Jerk (the rate of change of acceleration) is used as the basis of the suspension comfort performance measure, and a nonlinear cost function is applied to tracking, which targets the travel limits of the suspension (termed the "rattlespace"). Tracking measures currently in use generally fail to explicitly refer to the working space width. This matter is analysed, showing that driver slowdown is a complicating factor. The test rig of the physical experiment is of the semi-active type. High performing semi-active controls are generally based on active controls. Thus active controls are also investigated in this thesis. By stiffening the suspension as it moves away from equilibrium it can be made to combine softness over smooth roads with the capacity to react to large bumps when needed. Electronic control produces a much greater range of possible responses than is possible with just rubber or neoprene bump stops. Electronic, real-time control can attempt to target a smooth chassis trajectory within the possible future limits of rattlespace. Two general methods are proposed and analysed: one that adjusts the suspension stiffening according to the current road state, and another that targets edge trajectories within the possible future movements of the rattlespace. Some of these controls performed very well. With further investigation, they may be developed into extremely high performance controls, especially because of their high adaptability to varying conditions. The problem of avoiding collisions with rattlespace limits is related to the problem of avoiding overshoot of a limit distance. It becomes apparent that the residual acceleration at the point of closest approach needs to be limited, otherwise instability results. This led to the search for controls that attain rest without overshooting the final rest position. It was found that the minimum jerk needed for a general minimum-time control that does not overshoot zero displacement is always the control with just one intermediate switch of control, instead of two switches that are generally needed. This was proven to be optimal, and because of its optimality it works consistently when applied as a closed-loop, real-time optimal control. This control deals with the most difficult part of the trajectory: the final, "docking" manoeuvre. The control proved to be robust in physical experiments and it may itself have a number of applications. Some heuristics have been developed here to account for stochastic movement of the rattlespace edges in suspension controls, and these have proven quite successful in numerical experiments. Semi-active suspensions have a limit on the forces they can apply (the passivity constraint), but clipped versions are known to produce uncomfortable jerk. One method developed in this thesis produces a vast improvement in semi-active controls in the numerical experiments