## 2 State of Art
### Mobile Manipulators
In this way, engineers can not trust only the inherently safe design of a collaborative robot or a mobile robot but have to adopt complementary measures according to the specific scenario in which the system operates. The survey of [PERSON] et al. [25] analyzes the most useful sensors and methods to perceive and react to the presence of human operators in industrial applications involving mobile manipulators. Therefore the lack of safety regulations is compensated by the use of RGB-D, stereo vision, and Time-of-Flight cameras, laser scanner and sensors, and by the wider multi-sensor integration. Recently, the American National Standards Institute (ANSI) and Robotic Industries Association (RIA) have released a new safety standard (ANSI/RIA R15.08-1) [26]. The standard tries to fill the existing gap in the behavior of the combined system, focusing on how safety signals could be exchanged between two controllers. It also includes requirements for suppliers who want to combine a robot from one manufacturer with a mobile robot from another manufacturer. Before reaching this first result, a series of experimental tests for evaluating the functional safety of mobile manipulators were initiated by the National Institute of Standards and Technology (NIST) [27, 28] whose researchers provided a list of risk scenarios involving mobile manipulators. For each scenario, not only they considered various combinations of movement of a robot and a mobile robot but also made a distinction between single and dual control. This analysis led to identifying potential hazard situations in which neither the mobile robot standards nor industrial manipulator standards provide directions of application. The real flexibility challenge lies in handling the dual-mode, because the industrial manipulator and the mobile robot have independent controllers but with collaborative safe decisions to make. According to another study of the NIST [29], there are three ways to provide decision-making control and three possible levels of motion synchronization for a dual-controller mobile manipulator system. Figure 5 outlines these concepts and highlights that the three methods for decision-making control require different minimum synchronization levels for integration. Few other works in the literature deal with the control and safety aspects of a mobile manipulator in a general way. This is due to the coordination of the two subsystems that strictly depend on the task to be solved. This aspect led researchers to solve the specific problems with their own approaches. A pick-and-place coordination problem has been addressed by [PERSON] et al. [8] with a deep reinforcement learning approach (DRL). The controller of the robot mobile base is learned using the arm planner feedback which guides the mobile robot to the place where the arm can plan a trajectory. It seems that a very specific implicit coordination method is used to perform operation tasks but nothing about the safety aspect has been dealt with. A solution for adding a collaborative robot to an industrial AGV is suggested by [PERSON] [20] who propose a Graphical User Interface (GUI) to command and manage the whole system performance. The designed GUI communicates through a wi-fi connection with an Arduino board which dispatches specific commands towards the AGV and manipulator using an interface based on I/O digital signals. When the AGV is moving, the robot is always in a safe position, therefore the Arduino board does not send moving commands to it. In the same way, only when the AGV is stopped, the robot is allowed to move. In this scenario, an explicit coordination in the form of an Arduino board is used as a general solution that is independents of the particular type of robot or base used.