1. Simple programing/instruction and flexibility in usage
2. Automation of tasks lacking analytic description
3. Reliability and efficiency

As will be described below, there are several different phases in the process, where different methodologies can and should be used. To make the methodology usable in a practical case, it should be utilizable by operators without deep technical knowledge with an effort that can be accepted on a production line. Ultimately, such methods must remove the need for programing completely.

The assumption here is that the parts to be assembled are properly localized, such that they can be manipulated by a robot in the desired way. The problem concerns the following steps:

1. Identification and picking the first part (A).
2. Moving A to the vicinity of the second part (B).
3. Alignment of the two parts.
4. Exertion of force with simultaneous movement for smooth insertion.
5. Termination of the task when complete insertion is complete.

The above task, with all possible challenges, can easily be performed by a human operator. An operator in majority of cases needs very limited amount of information. Using prior knowledge and experiences and the sensory system the task can be completed and all possible exceptions can be handled. With time, a human operator becomes constantly more efficient and performs the task faster and more reliably.

The topics to be handled in this use case are how a machine can be instructed, trained, perform and improve to a high level of reliability and efficiency. The process can be divided into following steps:

1. Localization of parts: Image processing, object identification, classification and localization.
2. Alignment of parts: Control and optimization with (mainly) vision inputs.
3. Insertion through exertion of forces: Control and optimization with (at least) vision and force sensor feedback
4. Sensing the termination of the process: Pattern recognition in time series.
5. Continuous improvement: Reinforcement learning.

Vision and force sensors are most commonly used sensors in such processes. The objects and environment need to be observed at moderate as well as in very close distances. Force sensors are needed but have the weakness of not being active before a complete contact. Therefore, use of other sensors could be helpful.

The method is used for assembly tasks with the target of reducing the programming effort and increasing flexibility. For that to be achieved, the effort necessary to teach, train and use the system should be minimum and the reliability should come high at short time. This implicitly means that the system should become useful with limited amount of data and at limited amount of time. After an initial relatively stable state is reached, reinforcement can be used to improve the efficiency of the system.

The solution will become more attractive if transfer learning is utilized to further reduce the initial training time.

For benchmarking purpose a specific set of objects to be assembled together should be defined and performance of the methods can be measured by necessary training time, need for computing power and memory as well as time for completion of the task. The objects in the tests can be geometrically relatively simple. Special features such as rough surfaces, tight fitting or flexibility of the objects can be considered for different classes of problems.

• Standardization of definition of KPIs;
• Standardization of fail-safe options w.r.t. safety and quality;
• Standardization towards “Human-Co-working”
• Minimum acceptable standards for commercialization;
• Standard data set to independently validate the claims

• Complex and unpredictable assembly process due to imperfection of production steps, surface imperfection and other factors such as flexibility of parts.
• Accuracy of sensing
• Coworking with humans