Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations. Traditional models often lack the sophistication needed to handle the intricate dynamics inherent to autonomous vehicles and the interaction relationships among agents in the scene. As a result, these models have a lower model capacity, which then leads to higher prediction errors and lower training efficiency. In our research, we employ EqMotion, a leading equivariant particle, and human prediction model that also accounts for invariant agent interactions, for the task of multi-agent vehicle motion forecasting. In addition, we use a multi-modal prediction mechanism to account for multiple possible future paths in a probabilistic manner. By leveraging EqMotion, our model achieves state-of-the-art (SOTA) performance with fewer parameters (1.2 million) and a significantly reduced training time (less than 2 hours).
Although the use of smart phones in working environment is ubiquitous, the research on how the use of smart phones affects the working results is still in its infancy. Based on social learning theory, expectancy violation theory and resource conservation theory, this study explores how the boss phubbing (Bphubbing) affects employees' performance. Through the survey and data analysis of 215 employees, the results show that: (1) There is a negative correlation between Bphubbing and employee job performance. (2) Employees ‘ psychological capital plays a full mediation role in the relationship between Bphubbing and employee job performance. (3) work-family enrichment has a negative moderating effect on the relationship between Bphubbing and employees ’ psychological capital, which confirms the reverse stress-buffering model. This study expands the research on the mechanism of the negative impact of improper use of smart phones on the organization, which is conducive to the organization and managers to scientifically view the positive and negative sides of the use of smart phones for information management in the workplace, so as to put forward scientific suggestions for the correct use of smart phones.
Humanoid robots show the ability to replace humans for some dangerous tasks in complex environment. There are various motion types for robots moving into the dangerous environment like walking, rolling and crawling. However, the higher centroid brings instability when humanoid robots execute walking motion. Rolling motion may cause damage crash to the robot's mechanical structure. Therefore, a crawling action with low-centroid and less collision force is designed for robots to execute dangerous tasks. Firstly, a mechanical structure is designed for our robot called BHR-FCR. BHR-FCR own 23-Degree of Freedom (DoF) which provides possibility to achieve crawling motion. Then, Newton-Euler iterative recursion and Lagrange method are adopted for dynamics analysis which provides the basis for trajectory planning. Moreover, in the view of energy loss, the whole-body motion trajectory is generated by dynamic model and trajectory optimization with collocation method. The simulation and real-world experiments depict that BHR-FCR robot is able to crawl across lower wall and crawl on the slope which illustrates the stability of BHR-FCR mechanical structure and the effectiveness proposed algorithm.
Due to the robustness of ConvNet landmarks to appearance changes and viewpoint changes, there are some excellent works in ConvNet landmarks based visual place recognition (VPR). However, when dealing with extremely complex environmental changes, the performance of ConvNet landmarks based VPR is also difficult to meet the needs of practical applications because of the lack of information. This paper proposes a robust visual place recognition in challenging environments using landmarks associated with spatiotemporal and semantic Information (LSTS-VPR). LSTS-VPR combines ConvNet landmarks and image sequence to take advantage of the rich information of the objects contained in the sequence at different views and different time periods to filter and obtain more discriminative landmarks such as buildings and trees. LSTS-VPR consists of two modules, namely landmarks associated with spatiotemporal and semantic information-based feature aggregation module (LSTS-FAM) and multi-stage filtering-based feature matching module (MF-FMM). In LSTS-FAM, we use tube linking algorithm to connect the similar landmarks in different frames of one sequence and then calculate the landmark weights to screen the landmarks with high repeatability, which are proportional to the length of the tube. In MF-FMM, we use the semantic similarity, center distance and geometric proportion of landmarks as constraints to obtain the best matching sequence more accurately. Finally, the results of experiments on eight datasets demonstrate that the method proposed in this paper can better cope with a variety of changes including appearance changes, viewpoint changes, and occlusion of dynamic targets than state-of -art VPR approaches.
In this paper, we report on our use of cloud-robotics solutions to teach a Robotics Applications Programming course at Zurich University of Applied Sciences (ZHAW). The usage of Kubernetes based cloud computing environment combined with real robots - turtlebots and Niryo arms - allowed us to: 1) minimize the set up times required to provide a Robotic Operating System (ROS) simulation and development environment to all students independently of their laptop architecture and OS; 2) provide a seamless "simulation to real" experience preserving the exciting experience of writing software interacting with the physical world; and 3) sharing GPUs across multiple student groups, thus using resources efficiently. We describe our requirements, solution design, experience working with the solution in the educational context and areas where it can be further improved. This may be of interest to other educators who may want to replicate our experience.
