Julio Fajardo

This study presents a multimaterial robotic end-effector that combines compliant gripping structures with deformation sensing using thermoplastic polyurethane (TPU) and conductive polylactic acid (PLA). Four lattice geometries—diamond, hexagonal, triangular, and square—were evaluated through robotic pick-and-place experiments, electrical-response measurements, and cyclic durability tests. Each geometry was tested until visible structural damage, permanent deformation, or loss of functionality was observed; consequently, the accumulated number of cycles depended on the durability of each lattice. The triangular lattice exhibited localized fractures and limited cyclic durability, completing 1200 pick-and-place repetitions and approximately 900 sensor cycles. The hexagonal lattice provided an intermediate balance between functional durability and electrical response, completing 4350 pick-and-place repetitions and approximately 4984 sensor cycles. The square lattice completed 2850 pick-and-place repetitions but exhibited permanent deformation and did not complete the conductive-sensor durability test. The diamond lattice achieved the most favorable overall performance, completing 5700 accumulated pick-and-place repetitions without visible defects and approximately 9969 sensor cycles over 72 h while maintaining a periodic ADC response. These findings demonstrate that lattice geometry considerably influences grasping performance, structural integrity, electrical responsiveness, and cyclic durability. Therefore, an integrated assessment of these characteristics is required when selecting a lattice geometry for sensorized compliant gripping. Under the evaluated experimental conditions, the diamond lattice is recommended for the repetitive manipulation of delicate or irregular objects, whereas the hexagonal lattice represents an alternative when a balance between functional durability and electrical response is required.

Upper limb prosthesis have been evolving since 1967, progressing from mechanical prosthesis to robotic prosthesis, integrating electronics with emerging materials. This work aims to develop a low-cost integrated active upper limb prosthesis using additive manufacturing, controlled by the user with a myoelectric sensor to perform three hand gestures: power grip, two-fingered cup movement, and gripper actions. The prosthesis is based on the reduction of degrees of freedom, the analysis of five additive manufacturing materials, and the implementation of a neural network to control the gestures performed by the prosthesis. The precision obtained in the best model saved by the neural network was 91.25% and a recall of 90.75%. A key contribution of this work is the integration of soft 3D printing materials, which enhance object grasping by conforming to various shapes. In this case, grasping was achieved using a driven cable and a flex sensor in three gestures with different objects.

Force myography (FMG) detects hand gestures based on muscular contractions, featuring as an alternative to surface electromyography. However, typical FMG systems rely on spatially-distributed arrays of force-sensing resistors to resolve ambiguities. The aim of this proof-of-concept study is to develop a method for identifying hand poses from the static and dynamic components of FMG waveforms based on a compact, single-channel optical fiber sensor. As the user performs a gesture, a micro-bending transducer positioned on the belly of the forearm muscles registers the dynamic optical signals resulting from the exerted forces. A Raspberry Pi 3 minicomputer performs data acquisition and processing. Then, convolutional neural networks correlate the FMG waveforms with the target postures, yielding a classification accuracy of (93.98 ± 1.54)% for eight postures, based on the interrogation of a single fiber transducer.

The complexity of the user interfaces and the operating modes present in numerous assistive devices, such as intelligent prostheses, influence patients to shed them from their daily living activities. A methodology to evaluate how diverse aspects impact the workload evoked when using an upper-limb bionic prosthesis for unilateral transradial amputees is proposed and thus able to determine how user-friendly an interface is. The evaluation process consists of adapting the same 3D-printed terminal device to the different user-prosthesis-interface schemes to facilitate running the tests and avoid any possible bias. Moreover, a study comparing the results gathered by both limb-impaired and healthy subjects was carried out to contrast the subjective opinions of both types of volunteers and determines if their reactions have a significant discrepancy, as done in several other studies.

People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.

Localization is still one of the most fundamental tasks for autonomous navigation of mobile robots. However, the existing methods lack robustness when dealing with uncertainties without assuming some characteristics about noise inputs and the nonlinearity of the measurement models. In this work, a theoretical basis for designing two separate extended robust filters based on linear matrix inequalities is proposed to solve the localization problem. The first approach is based on the design of an ℋ ∞ observer-based filter through a two-step prediction correction structure. In this way, a convex optimization problem needs to be solved at each time step to determine the observer-gain that corrects the predicted pose of a differential wheeled robot. The second approach considers the advantages of a full-order filter which guarantees a better performance under the ℋ ∞ robust requirements. Besides, satisfactory results that validate theoretical remarks were performed in real and virtual scenarios through simulation frameworks.

Mobile robots are playing an important role as monitoring and immediate response services supporting risk management systems for natural or artificial disasters. In this work, an integration of systems based on a Unmanned Aerial Vehicle collects relevant information and show the status of a wildfire through an interactive dashboard, which can be accessed by the risk and disaster management systems through any computer. In addition, the system can identify people and communities at risk by presenting their estimated geolocation on a map, as well as the spread of the fire. In this way, it will be possible to understand the magnitude of the disaster, and thus develop better rescue and monitoring strategies.

Numerous assistive devices possess complex ways to operate and interact with the subjects, influencing patients to shed them from their activities of daily living. With the purpose of presenting a better solution to mitigate issues generated by complex or expensive alternatives, a test comparing different user-prosthesis interfaces was elaborated to determine the effects of diverse aspects in their user-friendliness, including that of a version created for this work. A simplistic, anthropomorphic and 3D-printed upper-limb prosthesis was adapted to evaluate all the renditions considered. The chosen design facilitates the modification of its operational mode, facilitating running the tests. Additionally, the selected prosthetic device can easily be adapted to the amputees' lifestyle in a successful way, as shown by experimental results, providing validity to the study. For the interaction process, a wireless third party device was elected to gather the user intent and, in some renditions, to work in tandem with some sort of visual feedback or with a multimodal alternative to verify their impact on the user.