ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers

Compared to classic robotics, biological nervous systems respond to stimuli in a fast and efficient way regarding the body motor actions. Decision making, once the sensory information arrives to the brain, is in the order of ms, while the whole process from sensing to movement requires tens of ms. Classic robotic systems usually require complex computational abilities. Key differences between biological systems and robotic machines lie in the way information is coded and transmitted. A neuron is the "basic" element that constitutes biological nervous systems. Neurons communicate in an event-driven way through small currents or ionic pulses (spikes). When neurons are arranged in networks, they allow not only for the processing of sensory information, but also for the actuation over the muscles in the same spiking manner. This paper presents the application of a classic motor control model (proportional-integral-derivative) developed with the biological spike processing principle, including the motor actuation with time enlarged spikes instead of the classic pulse-width-modulation. This closed-loop control model, called spike-based PID controller (sPID), was improved and adapted for a dual FPGA-based system to control the four joints of a bioinspired light robot (BioRob X5), called event-driven BioRob (ED-BioRob). The use of spiking signals allowed the system to achieve a current consumption bellow 1A for the entire 4 DoF working at the same time. Furthermore, the robot joints commands can be received from a population of silicon-neurons running on the Dynap-SE platform. Thus, our proposal aims to bridge the gap between a general purpose processing analog neuromorphic hardware and the spiking actuation of a robotic platform

Generating pointing motions for a humanoid robot by combining motor primitives

The human motor system is robust, adaptive and very flexible. The underlying principles of human motion provide inspiration for robotics. Pointing at different targets is a common robotics task, where insights about human motion can be applied. Traditionally in robotics, when a motion is generated it has to be validated so that the robot configurations involved are appropriate. The human brain, in contrast, uses the motor cortex to generate new motions reusing and combining existing knowledge before executing the motion. We propose a method to generate and control pointing motions for a robot using a biological inspired architecture implemented with spiking neural networks. We outline a simplified model of the human motor cortex that generates motions using motor primitives. The network learns a base motor primitive for pointing at a target in the center, and four correction primitives to point at targets up, down, left and right from the base primitive, respectively. The primitives are combined to reach different targets. We evaluate the performance of the network with a humanoid robot pointing at different targets marked on a plane. The network was able to combine one, two or three motor primitives at the same time to control the robot in real-time to reach a specific target. We work on extending this work from pointing to a given target to performing a grasping or tool manipulation task. This has many applications for engineering and industry involving real robots

Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks:a Validation-Based Approach

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses

Real-time myoelectric control of wrist/hand motion in Duchenne muscular dystrophy:A case study

Introduction: Duchenne muscular dystrophy (DMD) is a genetic disorder that induces progressive muscular degeneration. Currently, the increase in DMD individuals' life expectancy is not being matched by an increase in quality of life. The functioning of the hand and wrist is central for performing daily activities and for providing a higher degree of independence. Active exoskeletons can assist this functioning but require the accurate decoding of the users' motor intention. These methods have, however, never been systematically analyzed in the context of DMD.Methods: This case study evaluated direct control (DC) and pattern recognition (PR), combined with an admittance model. This enabled customization of myoelectric controllers to one DMD individual and to a control population of ten healthy participants during a target-reaching task in 1- and 2- degrees of freedom (DOF). We quantified real-time myocontrol performance using target reaching times and compared the differences between the healthy individuals and the DMD individual.Results and Discussion: Our findings suggest that despite the muscle tissue degeneration, the myocontrol performance of the DMD individual was comparable to that of the healthy individuals in both DOFs and with both control approaches. It was also evident that PR control performed better for the 2-DOF tasks for both DMD and healthy participants, while DC performed better for the 1-DOF tasks. The insights gained from this study can lead to further developments for the intuitive multi-DOF myoelectric control of active hand exoskeletons for individuals with DMD

A Monolithic Compliant Continuum Manipulator: a Proof-of-Concept Study

Continuum robots have the potential to form an effective interface between the patient and surgeon in minimally invasive procedures. Magnetic actuation has the potential for accurate catheter steering, reducing tissue trauma and decreasing radiation exposure. In this paper, a new design of a monolithic metallic compliant continuum manipulator is presented, with flexures for precise motion. Contactless actuation is achieved using time-varying magnetic fields generated by an array of electromagnetic coils. The motion of the manipulator under magnetic actuation for planar deflection is studied. The mean errors of the theoretical model compared to experiments over three designs are found to be 1.9 mm and 5.1degrees in estimating the in-plane position and orientation of the tip of the manipulator, respectively and 1.2 mm for the whole shape of the manipulator. Maneuverability of the manipulator is demonstrated by steering it along a path of known curvature and also through a gelatin phantom which is visualized in real time using ultrasound imaging, substantiating its application as a steerable surgical manipulator

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

BACKGROUND: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. CONTRIBUTION: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging

Highly Manoeuvrable Eversion Robot Based on Fusion of Function with Structure

Despite their soft and compliant bodies, most of today’s soft robots have limitations when it comes to elongation or extension of their main structure. In contrast to this, a new type of soft robot called the eversion robot can grow longitudinally, exploiting the principle of eversion. Eversion robots can squeeze through narrow openings, giving the possibility to access places that are inaccessible by conventional robots. The main drawback of these types of robots is their limited bending capability due to the tendency to move along a straight line. In this paper, we propose a novel way to fuse bending actuation with the robot’s structure. We devise an eversion robot whose body forms both the central chamber that acts as the backbone as well as the actuators that cause bending and manoeuvre the manipulator. The proposed technique shows a significantly improved bending capability compared to externally attaching actuators to an eversion robot showing a 133% improvement in bending angle. Due to the increased manoeuvrability, the proposed solution is a step towards the employment of eversion robots in remote and difficult-to-access environments

The efficacy of different torque profiles for weight compensation of the hand

Orthotic wrist supports will be beneficial for people with muscular weakness to keep their hand in a neutral rest position and prevent potential wrist contractures. Compensating the weight of the hands is complex since the level of support depends on both wrist and forearm orientations. To explore simplified approaches, two different weight compensation strategies (constant and linear) were compared to the theoretical ideal sinusoidal profile and no compensation in eight healthy subjects using a mechanical wrist support system. All three compensation strategies showed a significant reduction of 47-53% surface electromyography activity in the anti-gravity m. extensor carpi radialis. However, for the higher palmar flexion region, a significant increase of 44-61% in the m. flexor carpi radialis was found for all compensation strategies. No significant differences were observed between the various compensation strategies. Two conclusions can be drawn: (1) a simplified torque profile (e.g., constant or linear) for weight compensation can be considered as equally effective as the theoretically ideal sinusoidal profile and (2) even the theoretically ideal profile provides no perfect support as other factors than weight, such as passive joint impedance, most likely influence the required compensation torque for the wrist joint

A hybrid haptic stimulation prosthetic wearable device to recover the missing sensation of the upper limb amputees

A hybrid haptic feedback stimulation system that is capable in sensing the contact pressure, the surface texture, and the temperature, simultaneously, was designed for a prosthetic hand to provide a tactile sensation to amputation patients. In addition, the haptic system was developed to enable the prosthetic’s users to implement withdrawal reflexes due to the thermal noxious stimulus in a quick manner. The re-sensation is achieved by non-invasively stimulating the skin of the patients’ residual limbs, based on the type and the level of tactile signals provided by the sensory system of the prostheses. Accordingly, three stages of design and development were performed to satisfy the research methodology. A vibrotactile prosthetic device, which is designed for the detection of contact pressure and surface texture in upper extremity, represents. While, the design of a novel wearable hybrid pressure-vibration haptic feedback stimulation device for conveying the tactile information regarding the contact pressure between the prosthetic hand and the grasped objects represents the second methodology stage. Lastly, the third stage was achieved by designing a novel hybrid pressure-vibration-temperature feedback stimulation system to provide a huge information regarding the prostheses environment to the users without brain confusing or requiring long pre-training. The main contribution of this work is the development and evaluation of the first step of a novel approach for a lightweight, 7 Degrees-Of-Freedom (DOF) tactile prosthetic arm to perform an effective as well as fast object manipulation and grasping. Furthermore, this study investigates the ability to convey the tactile information about the contact pressure, surface texture, and object temperature to the amputees with high identification accuracy by mean of using the designed hybrid pressure-vibration-temperature feedback wearable device. An evaluation of sensation and response has been conducted on forty healthy volunteers to evaluate the ability of the haptic system to stimulate the human nervous system. The results in term of Stimulus Identification Rate (SIR) show that all the volunteers were correctly able to discriminate the sensation of touch, start of touch, end of touch, and grasping objects. While 94%, 96%, 97%, and 95.24% of the entire stimuli were successfully identified by the volunteers during the experiments of slippage, pressure level, surface texture, and temperature, respectively. The position tracking controller system was designed to synchronize the movements of the volunteers’ elbow joints and the prosthetic’s elbow joint to record the withdrawal reflexes. The results verified the ability of the haptic system to excite the human brain at the abnormal noxious stimulus and enable the volunteers to perform a quick withdrawal reflex within 0.32 sec. The test results and the volunteers' response established evidence that amputees are able to recover their sense of the contact pressure, the surface texture, and the object temperature as well as to perform thermal withdrawal reflexes using the solution developed in this work

Stress Relaxation Behavior of Additively Manufactured Polylactic Acid (PLA)

In this work, the stress relaxation behavior of 3D printed PLA was experimentally investigated and analytically modeled. First, a quasi-static tensile characterization of additively manufactured samples was conducted by considering the effect of printing parameters like the material infill orientation and the outer wall presence. The effect of two thermal conditioning treatments on the material tensile properties was also investigated. Successively, stress relaxation tests were conducted, on both treated and unconditioned specimens, undergoing three different strains levels. Analytical predictive models of the viscous behavior of additive manufactured material were compared, highlighting and discussing the effects of considered printing parameters