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The dragline silk of spiders is of particular interest to science due to its unique properties that make it an exceptional biomaterial that has both high tensile strength and elasticity. To improve these natural fibers, researchers have begun to try infusing metals and carbon nanomaterials to improve mechanical properties of spider silk. The objective of this study was to incorporate carbon nanomaterials into the silk of an orb-weaving spider, Nephila pilipes, by feeding them solutions containing graphene and carbon nanotubes. Spiders were collected from the field and in the lab were fed solutions by pipette containing either graphene sheets or nanotubes. Major ampullate silk was collected and a tensile tester was used to determine mechanical properties for pre- and post-treatment samples. Raman spectroscopy was then used to test for the presence of nanomaterials in silk samples. There was no apparent incorporation of carbon nanomaterials in the silk fibers that could be detected with Raman spectroscopy and there were no significant improvements in mechanical properties. This study represents an example for the importance of attempting to replicate previously published research. Researchers should be encouraged to continue to do these types of investigations in order to build a strong consensus and solid foundation for how to go forward with these new methods for creating novel biomaterials.

Abstract Background Powered exoskeleton can improve the mobility for people with movement deficits by providing mechanical support and facilitate the gait training. This pilot study evaluated the effect of gait training using a newly developed powered lower limb exoskeleton robot for individuals with complete spinal cord injury (SCI). Methods Two participants with a complete SCI were recruited for this clinical study. The powered exoskeleton gait training was 8 weeks, 1 h per session, and 2 sessions per week. The evaluation was performed before and after the training for (1) the time taken by the user to don and doff the powered exoskeleton independently, (2) the level of exertion perceived by participants while using the powered exoskeleton, and (3) the mobility performance included the timed up-and-go test, 10-m walk test, and 6-min walk test with the powered exoskeleton. The safety of the powered exoskeleton was evaluated on the basis of injury reports and the incidence of falls or imbalance while using the device. Results The results indicated that the participants were donning and doffing the powered lower limb exoskeleton robot independently with a lower level of exertion and walked faster and farther without any injury or fall incidence when using the powered exoskeleton than when using a knee–ankle–foot orthosis. Bone mineral densities was also increased after the gait training. No adverse effects, such as skin abrasions, or discomfort were reported while using the powered exoskeleton. Conclusions The findings demonstrated that individuals with complete SCI used the powered lower limb exoskeleton robot independently without any assistance after 8 weeks of powered exoskeleton gait training. Trial registration Trial registration: National Taiwan University Hospital. Trial registration number: 201210051RIB. Name of registry: Hui-Fen Mao. URL of registry: Not available. Date of registration: December 12th, 2012. Date of enrolment of the first participant to the trial: January 3rd, 2013.

In this paper, an object localization and tracking system is implemented with an ultrasonic sensing technique and improved algorithms. The system is composed of one ultrasonic transmitter and five receivers, which uses the principle of ultrasonic ranging measurement to locate the target object. This system has several stages of locating and tracking the target object. First, a simple voice activity detection (VAD) algorithm is used to detect the ultrasonic echo signal of each receiving channel, and then a demodulation method with a low-pass filter is used to extract the signal envelope. The time-of-flight (TOF) estimation algorithm is then applied to the signal envelope for range measurement. Due to the variations of position, direction, material, size, and other factors of the detected object and the signal attenuation during the ultrasonic propagation process, the shape of the echo waveform is easily distorted, and TOF estimation is often inaccurate and unstable. In order to improve the accuracy and stability of TOF estimation, a new method of TOF estimation by fitting the general (GN) model and the double exponential (DE) model on the suitable envelope region using Newton–Raphson (NR) optimization with Levenberg–Marquardt (LM) modification (NRLM) is proposed. The final stage is the object localization and tracking. An extended Kalman filter (EKF) is designed, which inherently considers the interference and outlier problems of range measurement, and effectively reduces the interference to target localization under critical measurement conditions. The performance of the proposed system is evaluated by the experimental evaluation of conditions, such as stationary pen localization, stationary finger localization, and moving finger tracking. The experimental results verify the performance of the system and show that the system has a considerable degree of accuracy and stability for object localization and tracking.

This work presents a novel calibration technique capable of simultaneously calibrating a camera’s intrinsic parameters and hand-eye-workspace relations. In addition to relaxing the requirement of a precise calibration reference to achieve manipulator accuracy, the proposed method functions when the hand is not in the view field of the eye. The calibration method uses a laser pointer mounted on the hand to project laser beams onto a planar object, which serves as the working plane. Collected laser spot images must adhere to certain nonlinear constraints established by each hand pose and the corresponding plane-laser intersection. This work also introduces calibration methods for two cases using single and multiple planes. A multistage closed-form solution is derived and serves as the initial guess to the nonlinear optimization procedure that minimizes errors globally, allowing the proposed calibration method to function without manual intervention. The effectiveness of the proposed method is verified by comparison with existing hand-eye calibration methods via simulation and experiments using an industrial manipulator.

This paper introduces an Embedded Software Architecture for Intelligent Robot systems (ESAIR) that addresses the issues of parallel thread executions on multi-core processor platforms. ESAIR provides a thread scheduling interface to improve the execution performance of a robot system by assigning a dedicated core to a running thread on the fly and dynamically rescheduling the priority of the thread. In the paper, we describe the object-oriented design and the control functions of ESAIR. The modular design of ESAIR helps improve the software quality, reliability and scalability in research and real practice. We prove the improvement by realizing ESAIR on an autonomous robot, named AVATAR. AVATAR implements various human-robot interactions, including speech recognition, human following, face recognition, speaker identification, etc. With the support of ESAIR, AVATAR can integrate a comprehensive set of behaviors and peripherals with better resource utilization.

Design of a new differential-velocity–type compliant joint with two motors is proposed for robotic manipulators. The speed/output torque of the differential-velocity–type joint is synthesized by controlling the differential speeds of a pair of actuators. Descriptions of the design concept, theoretical analysis, and the experiments are presented in this article. The experimental results showed that (1) the stiffness of the differential-velocity–type joint can be regulated by changing the speeds of both motors, (2) the differential-velocity–type joint actually reduced 7.9%−15.7% time compared with the single-drive joint when performing five cycles of reciprocation, and (3) a new design of delta-type robot with differential-velocity–type driving modules was developed for verification. The experimental results showed that the differential-velocity–type delta robot can perform flexible assembly operations within a 0.028-mm tolerance.

Localization in a wireless sensor network (WSN) becomes important for many modern applications, like landslide detection, precision agriculture, health care, and so forth. The more precise the position of an anchor node is, the more accurate the localization of a sensor node can be measured. Since the Global Positioning System (GPS) device cannot work properly indoor, some existing localization methods configure anchor nodes in a manual fashion. However, neither applying GPS modules nor manually configuring anchor nodes is suitable for a WSN and especially artificial errors of manual configuration may be propagated and affect the results of localization. In this paper, we propose an alternative method to estimate anchor node locations in an indoor environment. We collect the Received Signal Strength Indicator (RSSI) data from the anchor node when human is walking around them. Meanwhile, we use a wearable IMU-camera device to assist the moving trajectory estimation. We implement a monocular Visual Odometry with a human walking model to estimate moving trajectories. An Unscented Kalman Filter (UKF) is used to estimate the anchor node location by fusing the RSSI data and moving trajectory. The experiment results show that the proposed method has lower estimation error when locating anchors.

Sound source localization is an important feature in robot audition. This work proposes a sound source number and directions estimation method under a multisource reverberant environment. An eigenstructure-based generalized cross-correlation method is proposed to estimate time delay among microphones. A source is considered as a candidate if the corresponding time delay combination among microphones gives reasonable sound speed estimation. Under reverberation, some candidates might be spurious but their direction estimations are not consistent for consecutive data frames. Therefore, an adaptive K-means++ algorithm is proposed to cluster the accumulated results from the sound speed selection mechanism. Experimental results demonstrate the performance of the proposed algorithm in a real room.

This work presents a robust speaker's location detection algorithm using a single linear microphone array that is capable of detecting multiple speech sources under the assumption that there exist nonoverlapped speech segments among sources. Namely, the overlapped speech segments are treated as uncertainty and are not used for detection. The location detection algorithm is derived from a previous work (2006), where Gaussian mixture models (GMMs) are used to model location-dependent and content and speaker-independent phase difference distributions. The proposed algorithm is proven to be robust against the complex vehicular acoustics including noise, reverberation, near-filed, far-field, line-of-sight, and non-line-of-sight conditions, and microphones' mismatch. An adaptive system architecture is developed to adjust the Gaussian mixture (GM) location model to environmental noises. To deal with unmodeled speech sources as well as overlapped speech signals, a threshold adaptation scheme is proposed in this work. Experimental results demonstrate high detection accuracy in a noisy vehicular environment.

This work describes a robust background subtraction scheme involving shadow and highlight removal for indoor environmental surveillance. Foreground regions can be precisely extracted by the proposed scheme despite illumination variations and dynamic background. The Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM). Based on CBM, the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM) can be extracted and applied to build the gradient-based version of the probabilistic background model (GBM). Furthermore, a new dynamic cone-shape boundary in the RGB color space, called a cone-shape illumination model (CSIM), is proposed to distinguish pixels among shadow, highlight, and foreground. A novel scheme combining the CBM, GBM, and CSIM is proposed to determine the background which can be used to detect abnormal conditions. The effectiveness of the proposed method is demonstrated via experiments with several video clips collected in a complex indoor environment.