Controlling a soft robot poses a challenge due to its mechanical characteristics.Although the manufacturing process is well-established, there are still shortcomings in their control, which often limits them to static tasks.In this study, we aim to address some of these limitations by introducing a neural network-based controller specifically designed for the throwing task using a soft robotic arm.Drawing inspiration from previous research, we have devised a method for controlling the movement of the soft robotic arm during the ballistic task.By employing a feed-forward neural network, we approximate the relationship between the actuation pattern and the resulting landing position.This enables us to predict the input sequence that needs to be transmitted to the robot's actuators based on the desired landing coordinates.To validate our approach, we conducted experiments using a 2-module soft robotic arm, which was utilized to throw four different objects towards ten target boxes positioned beneath the robot.We considered two actuation modalities, depending on whether the distal module was activated.The results indicate a success rate, defined as the proportion of successful trials out of the total number of throws, of up to 68% when a single module was actuated.These findings demonstrate the potential of our proposed controller in achieving successful performance of the throwing task using a soft robotic arm.

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.

The availability of inexpensive, miniaturized sensors and the development of efficient and accurate filtering algorithms are important elements in order that pedestrian navigation system (PNS) will become an enabling technology for monitoring unconstrained daily living activities of human subjects. In this paper, we develop and test a computational method that estimates the path traveled by a walker from in-shoe mounted tri-axis inertial and magnetic sensors. The interest for inertial and magnetic sensors stems from their low cost, suitable form factors, small power consumption, which may give rise to self-contained, portable PNS whose hindrance to walking is limited. An important element in our approach is represented by an extended Kalman filter (EKF), whose aim is to estimate the body part orientation and to perform the in-line calibration of the tri-axis magnetometer. Two validation tests are applied to either acceleration or magnetic vector measurements, in order to adapt the measurement noise covariance matrix against the effects of body motion and external magnetic fields on the sensed gravity and earth's magnetic fields involved in orientation determination. Additionally, some biomechanical facts about how humans normally move are exploited to enhance the positioning performance of the developed method.

Hand movement data acquisition is used in many engineering applications ranging from the analysis of gestures to the biomedical sciences. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. While they have been around for over three decades, they keep attracting the interest of researchers from increasingly diverse fields. This paper surveys such glove systems and their applications. It also analyzes the characteristics of the devices, provides a road map of the evolution of the technology, and discusses limitations of current technology and trends at the frontiers of research. A foremost goal of this paper is to provide readers who are new to the area with a basis for understanding glove systems technology and how it can be applied, while offering specialists an updated picture of the breadth of applications in several engineering and biomedical sciences areas.

In this paper, we develop a system for which applications in the field of personal navigation are planned. In the current version, the system embodies a Global Positioning System (GPS) receiver and an inertial measurement unit (IMU), composed of two dual-axis accelerometers and one single-axis gyro. The IMU is positioned at a subject's foot instep, and it is intended to produce estimates of some gait parameters, including stride length, stride time, and walking speed. Data from GPS and IMU are managed by a DSP-based control box. The computations performed by the DSP processor allow to detect subsequent foot contacts by a threshold-based method applied to gyro signal, and to reconstruct the trajectory of the foot instep by numerical strapdown integration. Features of human walking dynamics are incorporated in the algorithm to enhance the estimation accuracy against errors due to sensor noise and integration drift. All computations are performed by the DSP processor in real-time conditions. The foot sensor performance is assessed during outdoor level walking trials. The traveled distance estimated by inertial dead-reckoning is compared with the estimate produced by GPS in experimental conditions where GPS can be used as a reference source for accurate absolute positioning. Results show the remarkable accuracy achieved by foot inertial sensing.

In this paper, we present bird's-eye (BE) viewer - a sensor-based navigation system intended to assist motor-impaired people driving their mobility aids to a specified goal. The BE-viewer is built around a vision-based 2D localization system based on a monocular image system composed of one web-camera, and endowed with real-time capability of estimating the location of active light-emitting markers. The 2D localization system is integrated in a human-machine-interface, which provides the user with a "top-view" sketch of the current and desired configurations. We introduce a protocol for the functional assessment of the docking procedure, here interpreted as a pursuit tracking task. The protocol allows to characterize how subjects use their resources to achieve a balance between speed, accuracy and control effort, depending on whether the perceptual enhancement by the BE-viewer is available or not. The benefits of the BE-view approach are presented and discussed on preliminary experiments where normal subjects perform docking procedures using a motorized anterior walker.

This paper reviews a new approach in the area of rehabilitation robotics. The service robot MATS helps disable and elderly people in their day life activities in common living environment like kitchen, bathroom, bedroom, etc. In this way the quality of life of the important part of population improves toward their social integration. This new prototype has new abilities like climb from one wall to another or from the table to the wheelchair, and at the same time to be attached and move with the wheelchair. The robot is totally autonomous and needs only power supply to be operated. This paper presents the distributed software architecture and the concept design of the HMI which handles the robotic system.

In this paper we illustrate the main features of a vision-based 2D localisation system, endowed with the real-time capability of estimating the location of active light-emitting markers disposed on a horizontal plane. The vision-based system revolves around using an inexpensive, off-the-shelf web-camera under computer control: the system is simple to install and operate, yet accurate to a level which is acceptable in a range of applications. Specifically, we describe how we intend to exploit the localisation system in an application whose ultimate goal is to support motor-impaired users in the approach to a specific configuration with their mobility aid, e.g., an electric wheelchair. To serve this application, the vision-based localisation system is integrated in a human-machine-interface, built around an handheld PC, which aims at providing the driver with a "top-view" representation of the scene. In our preliminary experiments, the perceptual enhancement is shown beneficial in increasing the docking accuracy up to few millimeters and tenths of degrees from the "target" configuration.

In this paper we describe a motorised rollator which aims at supporting people with motor impairments while walking and transporting objects. The prototype presented here incorporates a few additional functions, including the force enhancement to the user in the direct control mode, and the ability to follow the user at a distance, with or without collision avoidance, in the follow-me mode. The user input device for the direct control mode is composed of bilateral grips, equipped with strain gage force sensors, whose output signals are used for motor speed control. The follow-me mode is built around a trilateration system based on ultrasonic and infrared sensing technologies; the system activates a user-worn transponder for person tracking. We discuss both the motivations behind the control modes of interest and the technical solutions we have devised for their implementation. The results of some preliminary experiments are also presented and discussed, to show the feasibility of the approach.

The subject of the authors' investigation in this paper concerns how an in-air sonar sensor using correlation techniques can distinguish its own emissions from the emissions of other sonar sensors. Because of the limitations in the time-bandwidth products of transmit signals that can be used with narrowband sonar sensors, coded excitation ranging systems based on the classical theory of pseudorandom or random signals cannot achieve in practice high degrees of rejection between the different emissions. The method the authors present in this paper allows for designing pairs of coded transmit signals with high degrees of rejection, while the high accuracy and good spatial resolution of correlator-based range measurements are retained. The experimental verification of the method is carried out using high-frequency piezoelectric sensors.

In this paper we describe our approach to the design of proximity sensor arrays. Each sensing element of the proximity sensor array is a composite sensor, i.e. a sensor which is composed of an in-air ultrasonic rangefinder and an infrared detector. This sensor arrangement is capable, in principle, to achieve a perception of the explored objects that is not necessarily limited to their geometrical properties (size, shape and location relative to the sensor); possibly, the perception can be extended to other relevant features. In this paper, we show that, in principle, a sort of perception of the surface reflectance (the color) is achievable. The concept of the proposed sensor array spans a wide range of potential applications in flexible industrial automation, service robotics and autonomous mobility. The system described, in particular, is intended for providing an advanced wheelchair with the navigational capabilities required for improving the driving skills of disabled users.

A robust methodology for mobile robot localisation, or relocation, is described, making use of airborne sonar. A linear array of three sequentially fired ultrasonic transducers is proposed for performing a full scan of the environment, whose map is assumed to be a priori given. A geometric approach to relocation is adopted: the segmentation algorithm is based on an extended Kalman filter for estimating the geometrical parameters of different targets and related uncertainty. The features characterised by minimal geometrical uncertainty are used by the matching algorithm to perform a least squares search for the displacement and rotation that best take the sensor-based map into coincidence with the a priori reference map. A few heuristics are provided to enhance data robustness. An effective pruning strategy finally leads to solutions of the relocation problem with sub-centimeter positional accuracies and orientational accuracies within one degree.>

For several applications of interest in the robotic field, range sensing is often accomplished by means of single ultrasonic sensors. Additional information about the objects' surface, such as the orientation relative to the sensing device, has to be extracted by means of ultrasonic sensor arrays. So far, a few researchers have dealt with the problem of determining also the local curvature of curved reflectors. The aim of this paper is to investigate the minimum amount of range information needed to estimate distance, orientation and radius of cylindrical targets by means of a linear array of ultrasonic transducers. The proposed imaging problem is stated within the field of statistical estimation theory: an iterative linearised least-squares estimator, i.e. an extended Kalman filter, is designed and used for optimal processing of the sensed data. The theoretical analysis of the filtering algorithm performance allows one to elucidate the critical factors ultimately affecting the achievable accuracies of the proposed technique. Experimental results are presented, in close agreement with the predictions of the theoretical analysis.>

Ultrasonic pulse-echo ranging systems based on threshold detection methods are popular devices in the robotic field, as a means for determining the proximity of objects in a cost-effective manner. Despite their widespread use, serious concerns are often raised regarding the accuracy of the sensed data, particularly when the return signals are received at low signal-to-noise ratios. In principle, correlation-based detection methods provide better performance for their outstanding capability of detecting and recovering weak signals buried in noise, so as to permit ranging at longer distances, or, the distance being the same, at higher frequencies, with resulting improvements in spatial resolution in spite of the increased attenuation. In this paper the authors describe a simple pulse-echo ranging system whose receiver signal processing is completely digital: the use of appropriate sampling techniques and signal processing algorithms allows one to benefit from the advantages of correlation-based detection methods for accurate ranging of multiple objects, without compromising the most prominent features of pulse-echo sonar systems, namely their relatively low cost and simplicity of operation. The experimental results presented concern the use of relatively high-frequency ultrasonic transducers.>

A bstract-Ultrasonic ranging systems are widely used for mobile robot navigation; despite their simplicity and low cost, such systems suffer from a few shortcomings, mainly due to some unfavourable characteristics of sound propagation in air. Methodologies for emending the typical errors arising in the processing of sparse wide-angle sonar data are needed for obtaining meaningful maps of the environment: we are investigating a transducer array configuration that extracts features such as walls or edges in a specular 2D robot world by processing time-of-flights. Accurate target localization data, both in range and azimuth, are provided as well. Purposeful active sensing techniques are introduced, aiming at gathering sensorial data in less d ifficult operating conditions: given the azimuth estimates, a tracking procedure is issued that may significantly improve the Signal-to-Noise ratio at the receiver site by pointing the transducers directly toward the target. The proposed array configuration can efficiently map objects in the environment up to several meters: experimental results closely agree with the predictions provided by theoretical models of sound propagation and noise phenomena.

Describes the architecture and the components of a robot workstation designed to investigate active perception, with particular emphasis on tactile sensing procedures. The workstation is centered around a multifunctional finger-like probe equipped with different types of sensors, and a PUMA 562 manipulator to carry out exploration. The system operates under the control of a distributed architecture in which different sensory information is processed in parallel, whereas exploratory parameters (contact force, direction, velocity) are set in real time. Dedicated exploratory procedures allow to extract specific object features and to sort different objects out of a given set.>

The design of a robot system capable of carrying out complex sensory-motor sequences intended for collecting information on some body functions through palpation is proposed. The main features of the robot system, which consists of an articulated finger incorporating joint force and position sensors as well as piezoelectric polymer skin-like sensors, are discussed. The subroutine PALPATION, aimed at detecting hardened regions embedded in soft biological tissues through an exploratory procedure which replicates the operation of the physician's finger, is presented. A simple model for the behavior of the piezoelectric polymer fingertip sensor is proposed, and preliminary experimental results indicating both the validity of the model and the feasibility of the proposed automated palpatory procedure are reported.>