Knock-Knock: Acoustic Object Recognition using Stacked Denoising Autoencoders

This paper presents a successful application of deep learning for object recognition based on acoustic data. The shortcomings of previously employed approaches where handcrafted features describing the acoustic data are being used, include limiting the capability of the found representation to be widely applicable and facing the risk of capturing only insignificant characteristics for a task. In contrast, there is no need to define the feature representation format when using multilayer/deep learning architecture methods: features can be learned from raw sensor data without defining discriminative characteristics a-priori. In this paper, stacked denoising autoencoders are applied to train a deep learning model. Knocking each object in our test set 120 times with a marker pen to obtain the auditory data, thirty different objects were successfully classified in our experiment and each object was knocked 120 times by a marker pen to obtain the auditory data. By employing the proposed deep learning framework, a high accuracy of 91.50% was achieved. A traditional method using handcrafted features with a shallow classifier was taken as a benchmark and the attained recognition rate was only 58.22%. Interestingly, a recognition rate of 82.00% was achieved when using a shallow classifier with raw acoustic data as input. In addition, we could show that the time taken to classify one object using deep learning was far less (by a factor of more than 6) than utilizing the traditional method. It was also explored how different model parameters in our deep architecture affect the recognition performance.Comment: 6 pages, 10 figures, Neurocomputin

Soft fluidic rotary actuator with improved actuation properties

The constantly increasing amount of machines operating in the vicinity of humans makes it necessary to rethink the design approach for such machines to ensure that they are safe when interacting with humans. Traditional mechanisms are rigid and heavy and as such considered unsuitable, even dangerous when a controlled physical contact with humans is desired. A huge improvement in terms of safe human-robot interaction has been achieved by a radically new approach to robotics - soft material robotics. These new robots are made of compliant materials that render them safe when compared to the conventional rigid-link robots. This undeniable advantage of compliance and softness is paired with a number of drawbacks. One of them is that a complex and sophisticated controller is required to move a soft robot into the desired positions or along a desired trajectory, especially with external forces being present. In this paper we propose an improved soft fluidic rotary actuator composed of silicone rubber and fiber-based reinforcement. The actuator is cheap and easily manufactured providing near linear actuation properties when compared to pneumatic actuators presented elsewhere. The paper presents the actuator design, manufacturing process and a mathematical model of the actuator behavior as well as an experimental validation of the model. Four different actuator types are compared including a square-shaped and three differently reinforced cylindrical actuators

Observer-based Control of Inflatable Robot with Variable Stiffness

In the last decade, soft robots have been at the forefront of a robotic revolution. Due to the flexibility of the soft materials employed, soft robots are equipped with a capability to execute new tasks in new application areas -beyond what can be achieved using classical rigid-link robots. Despite these promising properties, many soft robots nowadays lack the capability to exert sufficient force to perform various real-life tasks. This has led to the development of stiffness-controllable inflatable robots instilled with the ability to modify their stiffness during motion. This new capability, however, poses an even greater challenge for robot control. In this paper, we propose a model-based kinematic control strategy to guide the tip of an inflatable robot arm in its environment. The bending of the robot is modelled using an Euler-Bernoulli beam theory which takes into account the variation of the robot's structural stiffness. The parameters of the model are estimated online using an observer based on the Extended Kalman Filter (EKF). The parameters' estimates are used to approximate the Jacobian matrix online and used to control the robot's tip considering also variations in the robot's stiffness. Simulation results and experiments using a fabric-based planar 3-degree-of-freedom (DOF) inflatable manipulators demonstrate the promising performance of the proposed control algorithm

Control Space Reduction and Real-Time Accurate Modeling of Continuum Manipulators Using Ritz and Ritz-Galerkin Methods

To address the challenges with real-time accurate modeling of multi-segment continuum manipulators in the presence of significant external and body loads, we introduce a novel series solution for variable-curvature Cosserat rod static and Lagrangian dynamic method. By combining a modified Lagrange polynomial series solution, based on experimental observations, with Ritz and Ritz-Galerkin methods, the infinite modeling state space of a continuum manipulator is minimized to geometrical position of a handful of physical points (in our case two). As a result, a unified easy to implement vector formalism is proposed for the nonlinear impedance and configuration control. We showed that by considering the mechanical effects of highly elastic axial deformation, the model accuracy is increased up to 6%. The proposed model predicts experimental results with 6-8% (4-6 [mm]) mean error for the Ritz-Galerkin method in static cases and 16-20% (12-14 [mm]) mean error for the Ritz method in dynamic cases, in planar and general 3D motions. Comparing to five different models in the literature, our approximate solution is shown to be more accurate with the smallest possible number of modeling states and suitable for real-time modeling, observation and control applications

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

Three-Axis Fiber-Optic Body Force Sensor for Flexible Manipulators

This paper proposes a force/torque sensor structure that can be easily integrated into a flexible manipulator structure. The sensor's ring-like structure with its hollow inner section provides ample space for auxiliary components, such as cables and tubes, to be passed through and, hence, is very suitable for integration with tendon-driven and fluid-actuated manipulators. The sensor structure can also accommodate the wiring for a distributed sensor system as well as for diagnostic instruments that may be incorporated in the manipulator. Employing a sensing approach based on optical fibers as done here allows for the creation of sensors that are free of electrical currents at the point of sensing and immune to magnetic fields. These sensors are inherently safe when used in the close vicinity of humans and their measuring performance is not impaired when they are operated in or nearby machines, such as magnetic resonance imaging scanners. This type of sensor concept is particularly suitable for inclusion in instruments and robotic tools for minimally invasive surgery. This paper summarizes the design, integration challenges, and calibration of the proposed optical three-axis force sensor. The experimental results confirm the effectiveness of our optical sensing approach and show that after calibrating its stiffness matrix, force and momentum components can be determined accurately

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Abstract—This paper focuses on designing interval type-2 (IT2) control for nonlinear systems subject to parameter uncertainties. To facilitate the stability analysis and control synthesis, an IT2 TS fuzzy model is employed to represent the dynamics of nonlinear systems of which the parameter uncertainties are captured by IT2 membership functions characterized by the lower and upper membership functions. A novel IT2 fuzzy controller is proposed to perform the control process, where the membership functions and number of rules can be freely chosen and different from those of the IT2 T-S fuzzy model. Consequently, the IT2 fuzzymodel- based (FMB) control system is with imperfectly matched membership functions, which hinders the stability analysis. To relax the stability analysis for this class of IT2 FMB control systems, the information of footprint of uncertainties, and the lower and upper membership functions are taken into account for the stability analysis. Based on the Lyapunov stability theory, some stability conditions in terms of linear matrix inequalities are obtained to determine the system stability and achieve the control design. Finally, simulation and experimental examples are provided to demonstrate the effectiveness and the merit of the proposed approach