736 publications from this institution
A lunar sampler plays the critical role and is of great importance in lunar explorations. In this paper, an error model was established for a novel flexible lunar sampler which has low weight, small volume, large workspace, and low power consumption. Based on its specific configuration, the forward and inverse kinematic models and the kinetostatic model are developed to formulate the volumetric error model of the mechanism. The error model is built by considering three classes of error sources: flexibility-induced errors, structural parameter-induced errors, and joint clearance errors. The flexible errors are compensated according to the experimental data; the second class of errors are modeled based on complete differential-coefficient theory, while the third class of errors are modeled based on the deterministic method. For the third class in error modeling, contact modes are introduced to build a joint clearance-induced error model. The relationship between different error sources and the output pose error of the sampling head is obtained. Finally, the error distribution in the workspace is evaluated.
In the field of virtual reality and teleoperation, haptic interaction between human operator and a computer or telerobot plays an increasingly important role in performing delicate tasks, such as robotic telesurgery, virtual reality based training systems for surgery, virtual reality based rehabilitation systems (Dario et al, 2003) (Taylor, Stoianovici, 2003) (Popescu, et al, 2000), etc.These applications call for the implementation of effective means of haptic display to the human operator.Haptic display can be classified into the following types: texture display, friction display, shape display, softness display, temperature display, etc.Previous researches on haptic display mainly focused on texture display (Lkei et al, 2001), friction display (Richard, Cutkosky, 2002) and shape display (Kammermeier et al, 2000).Only a few researches dealt with softness display, which consists of stiffness display and compliance display.The stiffness information is important to the human operator for distinguishing among different objects when haptically telemanipulating or exploring the soft environment.Some effective softness haptic rendering methods for virtual reality have already been proposed, such as a finite-element based method (Payandeh, Azouz, 2001), a pre-computation based method (Doug et al, 2001), etc.An experimental system for measuring soft tissue deformation during needle insertions has been developed and a method to quantify needle forces and soft tissue deformation is proposed (Simon, Salcudean, 2003).However, there are no effective softness haptic display devices with a wide stiffness range from very soft to very hard for virtual reality yet.The existing PHANToM arm as well as some force feedback data-gloves are inherently force display interface devices, which are unable to produce large stiffness display of hard object owing to the limitation of output force of the motors.This chapter focuses on the softness haptic display device design for human-computer interaction (HCI).We firstly review the development of haptic display devices especially softness haptic display devices.Then, we give the general principles of the softness haptic display device design for HCI.According to the proposed design principles, a novel method to realize softness haptic display device for HCI is presented, which is based on control of deformable length of an elastic element.The proposed softness haptic display device is composed of a thin elastic beam, an actuator for adjusting the deformable length of the beam, fingertip force sensor, position sensor for measuring the movement of human www.intechopen.com
In order to meet the actual requirements of nuclear radiation and chemical leak detection, and emergency response, a new small teleoperated robot for nuclear radiation and chemical detection is proposed. A small-size robot is manufactured according to technical requirements and the overall structure and control system is described. Meanwhile, based on the principles of human-robot interaction, a user-friendly human-robot interaction interface is designed to provide a good telepresence for the operator, helping the operator to perceive and judge the robot's situation to better assist in making the right decisions and in giving timely operation instructions. The experiment results show the robot system operates reliably and meets the technical requirements.
This paper presents a novel two-step autonomous navigation method for search and rescue robot. The algorithm based on the vision is proposed for terrain identification to give a prediction of the safest path with the support vector regression machine (SVRM) trained off-line with the texture feature and color features. And correction algorithm of the prediction based the vibration information is developed during the robot traveling, using the judgment function given in the paper. The region with fault prediction will be corrected with the real traversability value and be used to update the SVRM. The experiment demonstrates that this method could help the robot to find the optimal path and be protected from the trap brought from the error between prediction and the real environment.
Abstract Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fibers. However, decoding dexterous body movements from sEMG signals is still quite challenging. In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles. Specifically, twenty able-bodied participants performed 12 finger movements under two paces and three arm postures. HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm. Also, a data-glove was used to record the finger joint angles. Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured. The collected data samples were further employed for automated recognition of dexterous finger movements. The introduced dataset offers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic finger movements. As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for finger movement decoding.
Key words: sensor
Support vector machines (SVMs) have gained wide acceptance because of the high generalization ability for a wide range of pattern recognition problems. We address problems associated with complex pattern recognition in this paper and present a tree-structured support vector machine (TSSVM) with confusion cross. A TSSVM is overall a binary tree, whose internal nodes are modular SVMs. Those two non-terminal nodes generated from the same parent node perform discounted confusion crossover. The presented approach is evaluated against other classifiers investigated lately. The performance of the proposed approach is demonstrated with some typical complex classification problems.
