# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 6 Automation and Robotic Production in HMLV Production
### Robotics in HMLV Manufacturing Systems
#### 6.1.2 Programming and Algorithm Training  
A high variation of products meant that robots had to be programmed frequently. New methods and approaches are used to minimise the effort of setting up the robotic  
Figure 11: Types of industrial validation according to research area and industrial sectors.  
system. One of the most prominent ways of achieving this goal is to minimise the need for reprogramming to reduce the costs of employing robots, especially for SMEs [101].  
Traditionally, robot teaching involves recording the waypoints of the robot's end-effector, and then the path is interpolated to generate trajectories. Recording the waypoints is time-consuming and does not have the adaptability for complex trajectories or geometries, since it is difficult to collect multiple waypoints via pure manual teaching [102].  
One of the lead-through ways is where the user manipulates the robot along the desired path to the desired target points, where the robot records the entire process. This form of learning by demonstration (LfD) is an easy way to program the robots intuitively. [PERSON], et al. [103] expanded on this method by developing an algorithm to analyse segments of the operations. Then, human operators can perform corrections and evaluations to optimise the operations. [PERSON], et al. [104] also optimised the training method by including the robot's visual feedback and force sensors.  
On the contrary, programming can be carried out without the actual robot, this is called offline programming. This method is performed virtually with the robot and its workplace in a virtual environment. The teaching of the robotic tasks is achieved on the virtual robot. Therefore, programmers can teach the robots without interrupting production processes, thus reducing the unnecessary downtime of the robots [105]. Both teams, [PERSON] et al. (2020) and [PERSON] et al. (2020), developed augmented reality systems where users can move around the work cell to define the target point and path the robot should follow.  
Telemanipulation is an offline programming system where the operators guide robotic systems online, with interactive devices, due to the large flexibility from the high cognitive and perceptual capabilities of the human-in-the-loop robot teaching process. Telemanipulation's limitations include portability, low accuracy, and cumbersome motion mapping methods. These limit the effective robot teaching in multi-degree-of-freedom (DoF) manipulation under force and path constraints.  
Therefore, more straightforward and efficient solutions have been developed for telemanipulation. One such use case in the HMLV industry is in the aero-manufacturing industry. Tape masking is used to cover objects' surfaces during industrial processes, such as plasma spraying, spray painting, and shot spending. Work is being carried out to improve the learning capability of the path that is taught by human telemanipulation to provide automation solutions, to free operators from these tedious and unhealthy workloads [102; 105].  
To improve learning in actions that cannot be optimised due to unknown parameters, [PERSON], et al. [106] developed a method with Bayesian optimisation for a generic iterative approach in robotic learning. The users set the correct outcome of the action (e.g., dropping a ring into a fixture), and a camera captures the desired outcome. Then, the robot iterates different parameters (distance, height, and angle) of the operation until the desired result (from the camera) is achieved. Therefore, the robot can automatically find the most reliable solution for the parameters.