Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

One of the most demanding skills for a mobile robot is to be intelligent enough to know its own location. The global localization problem consists of obtaining the robot’s pose (position and orientation) in a known map if the initial location is unknown. This task is addressed applying evolutionary computation concepts (Differential Evolution). In the current approach, the distances obtained from the laser sensors are combined with the predicted scan (in the known map) from possible locations to implement a cost function that is optimized by an evolutionary filter. The laser beams (sensor information) are modeled using a combination of probability distributions to implement a non-symmetric fitness function. The main contribution of this paper is to apply the probabilistic approach to design three different cost functions based on known divergences (Jensen–Shannon, Itakura–Saito, and density power). The three metrics have been tested in different experiments and the localization module performance is exceptional in regions with occlusions caused by different obstacles. This fact validates that the non-symmetric probabilistic approach is a suitable technique to be applied to multiple metrics.

Scan matching is one of the most important tasks that must be solved to achieve simultaneous localization and mapping with autonomous mobile robots. This paper shows the development and results of a new 3D scan matching algorithm based on the Normal Distribution Transform and the bio-inspired optimization algorithm Differential Evolution. Different tests have been carried out to show the good performance of the new solution to the scan matching problem.

Caminar es una tarea extraordinariamente compleja que requiere la intervención de todo el sistema nervioso, viéndose afectada por diversas patologías neurológicas. Los exoesqueletos de rehabilitación de la marcha actuales, aunque potencialmente evidencian una mejora tras la rehabilitación, aún no han mostrado su superioridad terapéutica respecto a la terapia convencional de manera concluyente. Además, los dispositivos robóticos y exoesqueletos de rehabilitación de la marcha actuales no permiten entrenar específicamente la transferencia de peso o requieren de un buen estado funcional para ser utilizados. En este artículo se presenta el modelo conceptual de un sistema robótico ambulatorio acoplado al exoesqueleto Exo-H3 para la rehabilitación de la marcha implementando estrategias de aprendizaje por refuerzo. Se espera que este sistema favorezca la plasticidad neuronal al aportar mayor libertad al usuario para explorar nuevos patrones de movimiento y aumentar su control motor voluntario y su participación activa, mientras el exoesqueleto se adapta y garantiza su equilibrio y estabilidad. Los pasos futuros contemplan el diseño y la fabricación de cada uno de los subsistemas robóticos, validando su funcionamiento individual y en conjunto, con la participación de sujetos sanos y patológicos.

Robotic exoskeletons are usually controlled in joint-space, being spatio-temporal step features an uncontrolled consequence. However, clinical practice is focused on the cartesian movement of the foot rather than analytical hip/knee/ankle flexion/extension movements. In order to enable robotic exoskeletons to control the cartesian trajectory of the foot, a kinematic model to translate from joint-space to cartesian-space is needed. Besides, such a model requires to consider the forward progression of the direct (joints-to-foot) kinematic model to calculate the spatio-temporal stepping features. This paper presents a direct gait kinematic model including pelvis, hips, knees and ankles degrees of freedom based on Denavit-Hartenberg method and also proposes a method to iteratively compute the progression of the kinematic chain considering a straight walking over a flat floor. This model has been experimentally validated, comparing its results with the gait of 5 healthy subjects recorded by a Vicon Motion Capture System. Results show RMS errors lower than 1 cm and 5 cm in vertical and horizontal directions respectively.