Although the topic of HRI (human-robot interaction) has prevailed for a long time, we still require a great endeavor to achieve the vision which robot collaborates with human friendly. One of the reasons for this is the lacking of security. Since robot cannot have the same inteligence and physical experience as human, at least for now, they won't communicate with us fluently, and may do harm to human physically. In order to stay clear from these accidents, we need to experiment with a safer environment and make robot more smart which can be done with predicting human trajectory. In this paper a virtual environment which built up for HRI is introduced firstly. In this model, interactive object models and human body model which can be controlled by skeleton tracking algorithm. The code of human body model is published on the Github, hoping to help researchers who do HRI debugging in virtual environment Then we propose human trajectory prediction model using neural network and implement it in real world. The results show our prediction model outperforms other typical models and the video of our experiment provides more intuitive perspective.
An intelligent walking-aid cane robot is developed for assisting the elderly and the physically challenged with walking. A motion control method is proposed for the cane robot based on human walking intention estimation. Moreover, the safety is investigated for both the cane robot and the elderly. The fall detection and prevention concepts are proposed to guarantee the safety of the elderly while walking with the cane robot. However, the deficiency of the cane robot is that it can be overturned easily because of its small size and light weight. Therefore, a controllable universal joint is designed for adjusting the tilted angle of its stick. The stability of the cane robot during the fall prevention procedure can then be enhanced by controlling the tilted angle of stick to an optimal position. A center of pressure (COP)-based fall detection (COP-FD) method is used to detect the risk of falling. In this method, the user's COP is calculated in real time using an integrated force sensory system, which comprises a six-axis force/torque sensor and an inshoe load sensor. When the COP reaches the boundary of the specified safety area, i.e., the support polygon, it is assessed that the user is going to fall down. The COP-FD method can be used in various cases of falling. However, for cases of stumbling, a rapid fall detection method is proposed based on leg motion detection, and Dubois' fuzzy possibility theory is applied to adapt to different users. When the risk of falling has been detected, a fall prevention impedance control is executed considering both the interaction compliance and system stability. In the study, a control simulation platform was established to obtain the optimal controller parameters, and all the proposed methods were finally verified through simulations and experiments.
Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal's envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree.
An intelligent cane robot is designed for aiding the elderly and handicapped people's walking. The robot consists of a stick, a group of sensors, and an omnidirectional basis driven by three Swedish wheels. Recognizing the user's walking intention plays an important role in the motion control of our cane robot. To quantitatively describe the user's walking intention, a concept called "intentional direction (ITD)" is proposed. Both the state model and the observation model of ITD are obtained by enumerating the possible walking modes and analyzing the relationship between the human–robot interaction force and the walking intention. From these two models, the user's walking intention can be online inferred using the Kalman filtering technique. Based on the estimated intention, a new admittance motion control scheme is proposed for the cane robot. Walking experiments aided by the cane robot on a flat ground and slope are carried out to validate the proposed control approach. The experimental results show that the user feels more natural and comfortable when our intention-based admittance control is applied.
In electronic manufacturing system, the design of the robotic gripper is important for the successful accomplishment of the assembly task. Due to the restriction of the architecture of traditional robotic hands, the status of assembly parts during the assembly process cannot be effectively detected. In this research, an intelligent robotic gripper - i-Hand equipped with multiple small sensors is designed and built for this purpose, getting the essential parameters for some specific mathematical model. Mating connectors by robot, as an experimental case in this paper, is studied to evaluate the performance of i-Hand. A simple new model is proposed to describe the process of mating connectors, within which the distance between the connector and deformable Printed Circuit Board (PCB) is detected by i-Hand. An online Fault Detection and Diagnosis (FDD) algorithm is proposed. Various possible situations during assembly are considered and handled according to an event driven work flow. The effectiveness of proposed model and algorithm is proved by the experiments.
The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the system is poor. In this paper, a novel fault diagnosis method based on coaxial vibration signal feature fusion (CVSFF) is proposed to fully analyze the multisensor information of the system and build a more reliable diagnostic system. An ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into a number of intrinsic mode functions (IMFs). Then the autocorrelation analysis is introduced to reduce the random noise remaining in IMFs. After that, the Rényi entropy is calculated as the feature of bearings. Finally, the features of coaxial vibration signal are fused by a multiple-kernel learning support vector machine (MKL-SVM) to classify bearing conditions. In order to verify the effectiveness of the CVSFF method in REB diagnosis, eight data sets from the Case Western Reserve University Bearing Data Center are selected. The fault classification results demonstrate that the proposed approach is a valuable tool for bearing faults detection, and the fused feature from coaxial sensors improves fault classification accuracy for REBs.
Resiliometer is used to monitoring the bardness-compressive strength and also the surficial sharacteristics(dilatation,water content,etc.)of concrete. The results are affected by the arrangements of measuring points, measuring time and the degree of carbonization of concrete.
In electronic manufacturing systems, the design of the robotic hand is important for successful accomplishment of the assembly task, and also for human and robot coworker coordinated assembly. Due to the restrictions on the architecture of traditional robotic hands, the status of assembly parts, such as position and rotation during the assembly process cannot be detected effectively. In this research, an intelligent robotic hand – i-Hand, equipped with multiple small sensors – is designed and built for this purpose. Mating connectors by robot, as an experimental case in this paper, is studied to evaluate i-Hand performance. A new model that converts the traditional time-zone-driven model to an event-driven model is proposed to describe the process ofmating connectors, within which, most importantly, the distance between the connector and deformable Printed Circuit Board (PCB) is detected by i-Hand. The generated curve has provided more robust parameters than our previously studied Fault Detection and Diagnosis (FDD) classifier. Various possible situations during assembly are considered and handled based on this event-driven work flow. The effectiveness of our proposed model and algorithm is proven in experiments.
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