# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 1 Introduction  
Worker safety is one of the most important issues in the world of manufacturing today. In a decade that is projected beyond Industry 4.0, humans can work alongside robots and ensure flexible production without machine downtime. The results of this improvement are already visible in companies that have introduced new technologies and methods to reduce production costs and optimize processes. Looking to the future, we hear more and more about Industry 5.0, which envisages greater human involvement in industrial activities [1, 2]. The coexistence of humans and robots is a promising goal in the industry, as it allows them to work without protective barriers, in cooperation, and with different tasks. The introduction of new technologies such as collaborative robots (cobots) makes it possible to ensure safety in this context inherently. As described in [3], cooperation between humans and robots is very common in the industrial environment, especially in assembly lines. The main problem is that the presence of the operator near the robot opposes the speeds required for sufficient production volume. Typically in collaborative robotic cells, When an operator enters the robot's workspace, the manipulator must reduce its speed and stop immediately in case of direct contact with any part of it. This aspect may cause a slowdown in production, stopping the machine repeatedly [4, 5]. In addition, robotic applications must be certified according to strict safety standards, which require the analysis of possible collisions of the robot with a human through the use of special measuring devices [6]. One way to mitigate these problems is to implement obstacle avoidance algorithms, i.e., strategies that modify the robot's motion in real-time to avoid a collision with an obstacle/human once the latter's position is sensed by a dedicated sensor system. In general, obstacle avoidance strategies are based on the combination of:a method for obstacles identification and localization;
* a control law able to modify in real time the motion of robot based on obstacles coordinates. Numerous methods can be used to identify an object or the position of the human body in space. Studies about the prediction of human motion and the evaluation of the risk of collision can be found in [7], whereas strategies that are based on the position of the manipulator, using, for example, [PERSON]'s method for systems with high degrees of freedom, can be found in [8]. Several methods for identifying obstacles are based on contacts, in which capacitive touch sensors [9] or torque sensors [10] are used to measure the force exerted on the part of the manipulator. Other methods evaluate the position of obstacles using vision systems [11]. For example, cameras can detect the movement of the human arm (or body) by extrapolating the coordinates of skeletal models [12] and predicting their position in space thanks to machine learning algorithms [13]. In the case of depth sensors, such as RGBD cameras, it is possible to perceive the human pose in space [14; 15], also by using multiple cameras around objects [16], or to detect objects in the workspace, for example through filters that isolate a specific color [17; 18]. If greater precision is required, it is possible to use devices that are positioned in specific areas of the body, allowing, with an initial calibration, to identify the position of limbs in space. An example of such devices are wearable inertial measurement units (IMU) [19]. In all cases, whatever the sensor, a high acquisition frequency, at least 100 Hz, is required to ensure real-time control and capture the movement of even very dynamic obstacles. Regarding the real-time control law, this paper resumes the obstacle avoidance algorithm proposed by the authors in [20; 21]. Such a strategy allows the collaborative robot to move from one point to another with a trajectory that changes when an obstacle is perceived.