In the paper, a novel moving object detection (MOD) algorithm is developed and integrated with robot visual Simultaneous Localization and Mapping (vSLAM). The moving object is assumed to be a rigid body and its coordinate system in space is represented by a position vector and a rotation matrix. The MOD algorithm is composed of detection of image features, initialization of image features, and calculation of object coordinates. Experimentation is implemented on a small-size humanoid robot and the results show that the performance of the proposed algorithm is efficient for robot visual SLAM and moving object detection.

As the Industry 4.0 landscape unfolds, collaborative robots (cobots) play an important role in intelligent manufacturing. Compared with industrial robots, cobots are more flexible and intuitive in programming, especially for industrial and home service applications; However, there are still issues to be solved, including understanding human intention in a natural way, adaptability to execute tasks, and robot mobility in a working environment. As an attempt to solve the problems aforementioned, in this paper, we propose a modularized solution for mobile cobot systems, where the cobot equipped with a multi-camera localization scheme for self-localization can understand the human intention via human voice commands to execute the tasks in an unseen scenario in a small-area working environment. As far as intention understanding is concerned, we devise a natural language processing approach to establish an action base to describe human commands. According to the action base, the robot can then execute the tasks by planning a trajectory with the help of an object localization module, which integrates the point cloud and the object detected by YOLOv4 to locate the object’s position in three-dimension space. Depending on where the cobot interacts with the object, the cobot might need to navigate around the working environment. Thus, we also establish a low-cost and high-efficiency multi-camera localization system with ArUco markers to locate the mobile cobot in a larger sensing area. Experimental results show that the proposed vision-based mobile cobot can successfully interact with a human operator to assemble a wooden chair in a small workshop.