Operation

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Automate operation - Robotization

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Robotic task automation: insertion

Large Companies

NLP - Text summarization

For:

Media Intelligence Analysts

Scope:

Using Abstractive Text Summarization to reduce analysis costs for media monitoring.

Goal:

Improve Operation Efficiency

Robotic prehension of objects

For:

Customers, 3 rd parties, end users, community

Scope:

Outputting the end effector velocity and rotation vector in response to the view from a red green blue depth (RGB-D) camera located on a robot's wrist.

Goal:

Improve Operation Efficiency

Adaptable factory

For:

Component suppliers (sensors, actuators), machine builders, system integrators, plant operators (manufacturer)

Scope:

(Semi-)Automatic change of a production systems capacities and capabilities from a behavioural and physical point of view.

Goal:

Improve Operation Efficiency

Empowering autonomous flow meter control - reducing time taken for proving of meters

For:

Process industries; humans

Scope:

Calibration of control devices

Goal:

Other

Device control using AI consisting of cloud computing and embedded system

For:

Equipment users, manufacturers, distributors

Scope:

Learn the user's preferred temperature in each situation for the control of home appliances (air conditioning equipment).

Goal:

Other

Next Century Workforce: Partnering humans & robots to drive efficiency & growth

For:

Financial advisors Bank employees

Goal:

Improved Employee Efficiency

Powering remote drilling command centre

For:

Oil and gas upstream sector; environment, humans

Scope:

Oil and gas upstream (Deployed in 150 oil rigs and 2,5 billion+ data points each)

Goal:

Other

Order-controlled production

For:

Customer, producing companies, broker

Scope:

Automatic distribution of production jobs across dynamic supplier networks

Goal:

Other

Robotic task automation: insertion

For:

Incorrect AI system use; new security threats

Robotic vision scene awareness

For:

Customers, 3 rd parties, end users, community

Scope:

Determining the environment the robot is in and which actions are available to it.

Goal:

Improve Operation Efficiency

Value-based service

For:

Customer (product user), platform provider, service provider, product provider

Scope:

Process and status data from production and product use sources are the raw materials for future business models and services.

Goal:

Other

Robotic task automation: insertion

For:

Incorrect AI system use; new security threats

Goal:

Other

Problem addressed

Simple programing/instruction and flexibility in usage

Automation of tasks lacking analytic description

Reliability and efficiency

Scope of use case

Robotic assembly

Description

The case described here is a common step in assembly

processes in the manufacturing industry and includes

matching and properly connecting two parts when one needs

to be inserted into another. Successful and efficient insertion

usually needs action that involves feeling the parts. This is

difficult to describe in terms of mathematical algorithms and

therefore is difficult to program. Complexities in

programming, or a high degree of operational failure, make

usage of robots, or automation, unattractive. Use of machine

learning and artificial intelligence is one of the promising

methods to overcome such difficulties.

As described below, there are several different phases in the

process, where different methodologies can and are

expected to be used. To make the methodology usable in a

practical case, it is expected to be utilizable by operators

without deep technical knowledge at a level of effort that is

acceptable on a production line. Ultimately, such methods

are necessary to remove the need for programming

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 problematic portion of the

process concerns the following steps:

identification and picking the first part (A);

moving A to the vicinity of the second part (B); alignment of the two parts;

exertion of force with simultaneous movement for

smooth insertion;

termination of the task when insertion is complete.

The above task, with all possible challenges, can easily be

performed by a human operator. An operator in the majority

of cases needs a very limited amount of information. Using

prior knowledge, experiences and the sensory system, a

human operator can complete the task and handle all

possible exceptions. 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 and trained, to perform the task, and

improve to a high level of reliability and efficiency. The

process can be divided into the following steps.

Localization of parts: Image processing, object

identification, classification and localization.

Alignment of parts: Control and optimization with

(mainly) vision inputs. Insertion through exertion of force: Control and

optimization with (at least) vision and force sensor

feedback .

Sensing the termination of the process: Pattern

recognition in time series.

Continuous improvement: Reinforcement learning.

Vision and force sensors are the most commonly used

sensors in such processes. The objects and environment are

expected to be observed at moderate as well as at very close

distances. Force sensors are needed but have the weakness

of not being active before complete contact. Therefore, use of

other sensors can be helpful.

This 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 is necessary to be minimal and high

reliability is necessary to be achieved in a short time. This

implicitly means that the system is necessary to be useful

with a limited amount of data and after a limited amount of

time. After an initial relatively stable state is reached,

reinforcement can be used o improve the efficiency of the

system.

The solution would become more attractive if transfer

learning is used to further reduce the initial training time.

For benchmarking purposes, the specific set of objects to be

assembled together is necessary to be defined, and the

performance of the method can be measured by the

necessary training time, the need for computing power and

memory, as well as the time costed 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.

Image Processing

optimization

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