In this paper the workspace and payload capacity of a new design of reconfigurable Delta-type parallel robot is analysed. The reconfiguration is achieved by adjusting the length of the kinematic chains of a given robot link simultaneously and symmetrically during the operation of the robot. This would produce a dynamic workspace in shape and volume. A numerical analysis of the variation of shape and volume of the workspace and payload capacity of the robot is presented. Based both on the results of this analysis and on practical requirements, a proposal for the design of a reconfiguring mechanism is presented.

The kinematic design, development, and navigation control of a new autonomous mobile manipulator for unstructured terrain is presented in this work. An innovative suspension system is designed based on the kinematic synthesis of an adaptable, passive mechanism. This novel suspension can compensate for irregularities in the terrain by using two pairs of bogies joined by a crank-slider mechanism and facilitates the control of the robotic platform using video cameras. The mobile robot is also equipped with a robotic manipulator, of which a synthesis, simulation, and experimental validation are presented. Additionally, manipulation is accomplished during motion on rough terrain. The proposed mobile robot has been fabricated using additive manufacturing (AM) techniques. A vision-based control approach, from here on named mobile linear-camera space manipulation (MLCSM), for mobile manipulators has been synthesized and implemented to conduct experimental tests. This mobile manipulator has been designed to traverse uneven terrain so that the loading platform is kept close to horizontal while crossing obstacles up to one-third of the size of its wheels. This feature allows for the onboard cameras to stay oriented toward the target; it also allows for any device mounted on the payload platform to remain nearly horizontal during the task. The developed control approach allows us to estimate the position and orientation of the manipulator’s end effector and update its trajectory along the path toward the target. The experiments show a final precision for engagement of a pallet within +/−2.5 mm in position and +/−2 deg in orientation

This paper presents the theoretical development and experimental implementation of a sensing technique for the robust and precise localization of a robotic wheelchair. Estimates of the vehicle’s position and orientation are obtained, based on camera observations of visual markers located at discrete positions within the environment. A novel implementation of a particle filter on camera sensor space (Camera-Space Particle Filter) is used to combine visual observations with sensed wheel rotations mapped onto a camera space through an observation function. The camera space particle filter fuses the odometry and vision sensors information within camera space, resulting in a precise update of the wheelchair’s pose. Using this approach, an inexpensive implementation on an electric wheelchair is presented. Experimental results within three structured scenarios and comparative performance using an Extended Kalman Filter (EKF) and Camera-Space Particle Filter (CSPF) implementations are discussed. The CSPF was found to be more precise in the pose of the wheelchair than the EKF since the former does not require the assumption of a linear system affected by zero-mean Gaussian noise. Furthermore, the time for computational processing for both implementations is of the same order of magnitude.

We created a sensor-fusion suite for the acquisition of biometric information that can be used for the estimation of human control strategy in a variety of everyday tasks. This work focuses on the experimental validation of the integrated motion capture subsystem based on raster images. Understanding human control strategies utilized in everyday activity requires measurement of several variables that can be grouped as kinematic, dynamic, and biological-feedback variables. Hence, there is a strong need for the acquisition, analysis, and synchronization of the information measured by a variety of transducers. Our system was able to capture the complex dynamics of a flexible robot by means of two inexpensive web cameras without compromising accuracy. After validating the vision system by means of the robotic device, a direct measure of the center of gravity (COG) position during the recovery from a fall was performed on two groups of human subjects separated by age. The instrumental setup was used to estimate how ankle operational stiffness changes as function of age. The results indicate a statistical increase of stiffness for the older group.

This work combines the kinematics estimate of human standing with a hybrid identification algorithm to identify a set of ankle dynamics mechanical parameters. We used the hold and release (H&R) experimental paradigm to model a set of recoverable falls on a population of unimpaired adults. Body kinematics was acquired with a microsoft kinect (mk) version 2 after benchmarking its position accuracy to a camera-based vision system (CVS). The system identification algorithm, combining an extended Kalman filter (EKF) and a genetic algorithm (GA), allowed to identify the effect of tendon and muscle stiffness at the ankle joint, separately. This work highlights that, when associated to soft-computing techniques, affordable tracking devices developed for the gaming industry can be used for the reliable assessment of neuromechanical parameters in clinical settings.

The estimation of human ankle's mechanical impedance is an important tool for modeling human balance. This work presents the implementation of a parameter-estimation approach based on a state-augmented extended Kalman filter (AEKF) to infer the ankle's mechanical impedance during quiet standing. However, the AEKF filter is sensitive to the initialization of the noise covariance matrices. In order to avoid a time-consuming trial-and-error method and to obtain a better estimation performance, a genetic algorithm (GA) is proposed to best tune the measurement noise (Rk) and process noise covariances (Q) of the extended Kalman filter (EKF). Results using simulated data show the efficacy of the proposed algorithm for parameter-estimation of a third-order biomechanical model. Experimental validation of these results is also presented. They suggest that age is an influencing factor in the human balance.