Advanced Robotics

Yang, Jingtao; Chen, Mingyou; Chen, Weilin; Lu, Qinghua; Wei, Huiling; Zhang, Yunzhi

doi: 10.1080/01691864.2024.2395922pmid: N/A

Slip detection plays a crucial role in robotic operations and has received increasing attention in the field of robotics. We introduced a slip detection model called VT-VT, based on the fusion of vision and touch using a transformer architecture. The model leverages the advantages of a 'divided spatial-temporal attention' mechanism, capturing global contextual information more effectively and exhibiting high sensitivity to potential temporal features during the slipping process. To validate the effectiveness of VT-VT, we conducted experiments on a public slip detection dataset, achieving a test accuracy of up to 90.52%, significantly outperforming a convolutional neural network–long short-term memory model combined with different feature extraction networks. Furthermore, this study revealed the impact of different patch size values, different modal perception modes, and varying lighting conditions on the performance of the VT-VT model. Moreover, we analyzed the real-time capabilities of the VT-VT model.

Suwa, Sotaro; Takeshita, Keisuke; Yamazaki, Kimitoshi

doi: 10.1080/01691864.2024.2391831pmid: N/A

This manuscript describes a TAsk and Motion Planning (TAMP) method for mobile manipulators. We focus on fetch-and-carry tasks, and we aim to simultaneously generate both a sequence of actions and a motion sequence for each action. The number of factors to be considered in solving such a problem, and the interactions among them are complex due to the multifaceted characteristics of mobile manipulation. As a result, straightforwardly performing simultaneous TAMP may require a significant amount of processing time. Therefore, we reduce the complexity of the problem by formulating fetch-and-carry planning for a Mixed Integer Linear Programming (MILP) problem. The proposed method can obtain the sequence of actions and the movement of the mobile manipulator in less than one second in many cases. The effectiveness of the proposed method is verified in environments in which delivery objects and obstacles are placed in various patterns, using a robot with an omnidirectional mobile platform and a serial link manipulator.

Yamasaki, Kakeru; Shibata, Tomohiro; Hénaff, Patrick

doi: 10.1080/01691864.2024.2384422pmid: N/A

In a diverse society, a crucial aspect of developing social robots is their ability to adapt to each individual's characteristics. There are two types of adaptation: physical and social. In pHRI, attention is paid to physical adaptation, but by investigating the social meaning of physical adaptation, we can extend it to social adaptation. Although it is a physical interaction, a handshake is a social action with complex meanings. We realized a handshaking robot with a Rowat-Selverston CPG controller that can synchronize with human movements. This study aims to clarify the relationship between physical and social adaptation by examining individual characteristics inferred from the internal state of the robot and human-robotic social attributes. Our finding is that there is a correlation between the internal state of the robot and some robotic social attributes. It was also found that the length of the handshake had little effect on these social perceptions. This study highlights the complex interplay between robot physical adaptability and social attribution, providing a foundation for developing robots capable of personalized and socially meaningful interactions.

Shoda, Koki; Louhi Kasahara, Jun Younes; Asama, Hajime; An, Qi; Yamashita, Atsushi

doi: 10.1080/01691864.2024.2388119pmid: N/A

In this paper, we introduce a novel approach for defect detection in hammering inspections using Unmanned Aerial Vehicles (UAVs). Despite the promising application of UAVs for inspecting hard-to-reach structures, like bridges, their efficiency is often compromised by the significant ego noise produced by their motor-propeller systems. This noise complicates the discrimination between healthy and defective hammering sounds. In previous research, methods to improve robustness through supervised learning have been proposed; however, these methods require the labeling of hammering sounds by skilled inspectors to train the discrimination model. To overcome this problem, we propose an ego-noise reduction method based on propeller vibrations. By reducing ego noise and thereby making the characteristics of hammering sound more dramatically clear, we enable unsupervised defect detection amidst ego noise. Our experiments with concrete specimens demonstrate that our technique achieves defect detection with an accuracy on par with the supervised method. The proposed method proves especially beneficial for hammering inspections, in which the domain gap–the variability in acoustic signatures of hammering sounds caused by differences in concrete mix ratios and curing conditions from one site to another–presents a significant challenge. Our approach effectively adapts to these variations, ensuring reliable defect detection across diverse construction environments.