###### Abstract
The increase in the use of educational robots in classrooms [14] and labs has been made possible thanks to the appearance of low or middle-cost educational robotics kits, which are mechanically easy to construct. So there exists a wide variety of robotics kits (see [15] and the references there in for a review of educational robotics kits, including cost an applications), several of them integrated with ROS [16; 17] and/or using smartphones [18; 19]. In a previous work [19], we presented a low-cost educational mobile robot, called Andruino-A1 based on Android and Arduino as an open system (hardware and software). It was created to be used as a robotic educational tool to enable professional education and training for adults (VET) and undergraduate students who perform learning tasks with real robots, in the laboratory, in the classroom or even as homework, according to educational policy. \"BYOR: Bring [Build] Your Own Robot.\" Recently, a new Andruino robot, Andruino-R2, has been briefly introduced in [20]. In this article we present a detailed description of the Andruino-R2 robot (see Figure 1) and a comparison with Andruino-A1, highlighting the advantages of the new system. It is integrated with ROS, and allows the utilization of multiple sensors and communication resources: light sensors, ultrasonic sensors, GPS, camera, accelerometer, compass, Bluetooth, WiFi... The construction of the robot itself, together with its programming, provide learning outcomes in various areas. These sensors and resources can be applied in a simple way to carry out advanced tasks using real systems, but also maintaining the possibility of their integration with environments and simulation tools thanks to the 3D models developed. In addition, this article focuses on examples of how Andruino-R2 can be applied in the areas of intelligent control, machine vision and machine learning, maintaining a didactic approach, to illustrate its versatility and simplicity. These techniques are increasingly relevant and some authors demand that they be incorporated into the ordinary curriculum of VET and engineering students [21]. However, in many cases, only simulation environments are considered or commercial educational robots are used with a higher cost [22; 23; 24]. Low-cost robots have also been used, but generally with a very specific approach as in [25] where the implementation of fuzzy controllers in embedded systems is shown. On the contrary Andruino-R2, allows to acquire the holistic vision that the construction of a complete system offers the student, in addition to being flexible enough to implement different sets of techniques in a simple way. Specifically, a practical application has been made to visual tracking of trajectories integrated with a Fuzzy Collision Risk system, which avoids collision with obstacles ahead at a close distance, in a qualitative way. Likewise, Machine Learning has also been applied to solve the problem of robot localization based on Wi-Fi signals in a three-room scenario. Three supervised multiclass classification algorithms have been applied and compared, Multiclass Logistic Regression (MLR), Multiclass Neural Network (MNN) and Multiclass Decision Forest (MDF), in addition to proposing improvements based on expanding the dataset using new context features. Therefore, Andruino-R2 is a versatile and valuable tool for education, research and innovation in several domains, but it maintains its simplicity, its low cost and its open source. The paper is organized as follows: Section 2 presents the design criteria on which Andruino robots are based. Sections 3 and 4 provide details of both the hardware and the software respectively of the new Andruino-R2 robot, the differences with Andruino-A1, as well as the relevant aspects that they contribute from an educational point of view. In Section 5 computer vision and fuzzy logic are applied to visually track a trajectory integrated with a fuzzy collision risk system.