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Insect Intelligent Building (I2B) is a novel platform of intelligent buildings. The outstanding feature of I2B is the decentralized network structure connected by smart nodes. I2B can employ APPs (applications) developed by various practitioners or programming fans to manage and control buildings. However, due to the unique parallel operation of I2B platform and the popularization of APP developers, there still exists no effective approach to supporting I2B APP development. To deal with the challenges and provide meaningful guidance for describing and developing I2B APP and motivating the prospective programming language design, we propose INR, a programming model for I2B APP development. Three submodels in INR, namely, Individual, Neighborhood, and Region, are defined and implemented, respectively, for describing different task requirements. Moreover, new mechanisms of Tag-based programming and Clustering operation are established to support the plug-and-play and parallel abilities of APPs in I2B. Finally, we apply the programming model into an application case to illustrate the developing pattern of the I2B APP and verify the effectiveness of our approach.

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Integrating an exoskeleton as the external apparatus for a brain–machine interface (BMI) has the advantage of providing multiple contact points to determine body segment postures and allowing control to and feedback from each joint. When using macaques as subjects to study the neural control of movement, an upper limb exoskeleton design with unlikely singularity is required to guarantee safe and accurate tracking of joint angles over all possible range of motion (ROM). Additionally, the compactness of the design is of more importance considering macaques have significantly smaller body dimensions than humans. This paper proposes a six degree-of-freedom (DOF) passive upper limb exoskeleton with 4DOFs at the shoulder complex. System kinematic analysis is investigated in terms of its singularity and manipulability. A real-time data acquisition system is set up, and system kinematic calibration is conducted. The effectiveness of the proposed exoskeleton system is finally demonstrated by a pilot animal test in the scenario of a reach and grasp task.

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This paper describes a novel optical fiber assembly system featuring a multi-axis alignment function based on micro-vision feedback control. It consists of an active parallel alignment mechanism, a passive compensation mechanism, a micro-gripper and a micro-vision servo control system. The active parallel alignment part is a parallelogram-based design with remote-center-of-motion (RCM) function to achieve precise rotation without fatal lateral motion. The passive mechanism, with five degrees of freedom (5-DOF), is used to implement passive compensation for multi-axis errors. A specially designed 1-DOF micro-gripper mounted onto the active parallel alignment platform is adopted to grasp and rotate the optical fiber. A micro-vision system equipped with two charge-coupled device (CCD) cameras is introduced to observe the small field of view and obtain multi-axis errors for servo feedback control. The two CCD cameras are installed in an orthogonal arrangement—thus the errors can be easily measured via the captured images. Meanwhile, a series of tracking and measurement algorithms based on specific features of the target objects are developed. Details of the force and displacement sensor information acquisition in the assembly experiment are also provided. An experiment demonstrates the validity of the proposed visual algorithm by achieving the task of eliminating errors and inserting an optical fiber to the U-groove accurately.

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