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During an earthquake, the seismic response of adjacent buildings will be affected by each other, which is known as structure-soil-structure interaction (SSSI). In order to study the SSSI between neighboring buildings, the slab foundation frame structure on a typical type II site in Nanjing is used as the object. ABAQUS software is used to establish a nonlinear static-dynamic coupling two-dimensional finite element analysis model of a single building and multiple buildings with different spacing. The numerical analysis of the model shows that: Under the action of earthquakes, the increase of surrounding structures will increase its own acceleration response; when the building spacing ratio is 1/2 and 1/4, the existence of adjacent buildings will reduce its own acceleration response. When the spacing ratio is greater than or equal to 1, the impact tends to be unfavorable, and it is the most unfavorable when the spacing ratio is 2. When the spacing ratio is greater than or equal to 4, the impact of adjacent buildings on each other’s peak acceleration response can be ignored.

Collaboration between heterogeneous robots promises increased robustness and efficiency of tasks with great potential in applications, such as manufacturing, search and rescue. In this paper, we present a collaboration system based on an edge computing framework, a mobile robot named “StellaX” and a manipulator. The robot StellaX is assembled by a three-wheel omnidirectional platform, a LIDAR scanner for indoor autonomous navigation, and a stereo camera which can capture objects' images. The information of objects, which can be used for grasping, is stored and shared with the manipulator through the edge computing framework with Docker and ROS. Finally, we test the collaboration system by conducting an object recognition experiment and a navigation experiment. The experimental results demonstrate the system effectiveness and prove that the system yields better real-time performance and reduces total task execution time than a cloud computing-based scenario.

In this paper, we develop and implement a kind of mobile manipulator named Jupiter, which consists of a six-degree-of-freedom(DOF) manipulator and an omnidirectional mobile platform with four actively steerable wheels. In contrast to the omnidirectional mobile manipulator with Mecanum wheels, the proposed mobile manipulator can be adopted in rough terrains. Two laser scanners and a stereo camera are mounted in the system to perceive the environment. With the IMU and odometry data, Jupiter is capable of doing SLAM by Hector SLAM method and path planning. A manipulation frameworks with a single shot multibox detector (SSD) approach is used to detect and grasp the objects. Combining the two function, both the manipulator and the platform are integrated into a system which can execute mobile manipulation tasks autonomously. Our contribution includes a complete hardware construction and software pipeline of the mobile manipulator which is capable of autonomously doing SLAM, navigation, detecting and grasping objects. The performance of the system are tested and proved by autonomously executing an mobile manipulation mission including autonomous navigation with multi-sensors and apples grasping and placing.

In this paper, a novel Attitude and Heading Reference System(AHRS) algorithm for real time controlling of various mobile robots is presented. Such a control system is essential for real time mobile robots control applications, such as entertainment, business activities, industrial, domestic assistant and etc. New angles and vectors definition, system specification and Momentum Correction Factor(MCF) are proposed to compensate the motion time delay during an inertial action. A dedicated experimental setup is established to test the algorithm. Experimental results show that the proposed algorithm is able to maintain an accurate and delay-free motion control.

This paper presents a global 3D feature descriptor for object recognition and grasping. The proposed descriptor stems from the clustered viewpoint feature histogram (CVFH) feature descriptor. Since the CVFH feature descriptor relies on shape information only, it obtains a poor performance when recognizing the objects with similar shapes. In order to improve the robustness and accuracy of object recognition, we extend CVFH feature descriptor with color information. Then this new global 3D feature descriptor is tested for multiple classes of 3D object classification by using support vector machines (SVM), and it is evaluated with a public dataset and real scenes respectively. The experimental results show that the proposed descriptor outperforms the CVFH feature descriptor in terms of recognition rate. Finally we utilize the proposed descriptor on our grasping system to recognize and grasp the objects, showing that the grasping system can accomplish the tasks well.