# Suitability of UR5 Robot for Robotic 3D Printing
## 3 Measuring Crucial Robot Performance Characteristics
### Robot Drift  
Robot drift can occur both in the described motion of the robot along a straight line, which can be demonstrated by a longer process run of the robot and is manifested by a shift in an undesirable direction from the intended programmed trajectory. In the case of motion along a circular trajectory, drift can be manifested by increasing or decreasing the radius of the path described by the end tool of the robot.  
While several methods are available for measuring robot drift, the choice of methods depends on the specific robot, the working environment, and the desired level of accuracy. The most expensive measurement methods are as follows:  
* Visual tracking--For simple robot designs, drift can be tracked visually using marker tape or reference points in the work environment.
* Measurement using position sensors--More accurate measurement is made possible by using external position sensors, such as laser scanners or image recognition cameras, to track the robot's position relative to reference points in the working environment.
* Odometry--This measurement method uses data from the robot's encoders to determine the position of individual mechanical parts of the robot. This method is prone to measurement inaccuracies, but it is a method for easily estimating the drift of a robot.
* Simultaneous localization and mapping (SLAM)--This advanced measurement technique allows a robot to simultaneously create a map of the work environment and localize its position within it. The data thus obtained can be used to accurately determine drift with high measurement accuracy.  
Measuring and minimizing drift is essential for robot performance and accuracy. The measurement method chosen depends on the specific robot and application. Robot drift cannot be completely eliminated, although several methods for minimizing it are available. Better motion accuracy can be achieved through the calibration, maintenance, and implementation of sophisticated control algorithms, and robot drift can be minimized. Periodic calibration of the robot's sensors (encoders) ensures the accuracy of the acquired data and thus reduces the error rate when checking the robot for proper functionality. Periodic maintenance of the robot's motors, gears, and joints is another important task to ensure that backlash is minimized, and the overall accuracy of the robot's executed motions is increased. Minimizing unwanted robot drift can also be achieved by implementing feedback correction algorithms in the robot control system to detect and correct drift based on the sensor data. It is also necessary to optimize robot motion path planning algorithms that minimize abrupt changes in robot direction and velocity, thereby reducing the risk of undesired robot drift in real time.  
Within robotic 3D printing, measuring the robot's drift is critical to achieving accurate and consistent printing. Drift, defined as the undesired deviation of the robot from its intended trajectory, can cause various problems manifested as 3D-printing process errors (Figure 1), which include  
* Incorrect material placement (warping, stringing, and poor bridging)--If the robot shifts during printing, the printer nozzle can place the filament in the wrong place, causing printing errors.
* Vibration and ringing--Drift can produce an uneven filament distribution on the print pad, resulting in uneven print surfaces.
* Layer shifting--If the robot shifts between printing layers, this may result in material layer shifting and the print structure being distorted.  
Precise measurement and minimizing robot drift are crucial for achieving high-quality prints in robotic 3D printing. By using various drift measurement and correction methods, along with calibration, maintenance, and advanced control algorithms, it is possible to achieve accurate and consistent 3D printing with robotic systems.