Industrial Robot Skills

When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and expertise. One central concept in knowledge modeling for robots is action representation. In this paper, we describe our representation of robot skills. The skills have resource requirements, logical and procedural information from which executable code can be generated

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

A Helping Hand: Industrial Robotics, Knowledge and User-Oriented Services

In this paper we discuss AI in industrial robotics. In automatic control, computer vision and optimization, ma- chine learning and data mining algorithms are widely used. However, cognition enabling mechanisms, such as high-level logic and symbolic reasoning, are still limited. This is not due to the lack of available algorithms, rather the bottleneck is knowledge representation, acquisition and transformation between different formalisms. In industrial robotics, cognition is not self-serving, AI tech- nologies are rather a tool to make the user interaction, the system configuration and the task execution as cost efficient as possible. Autonomy is a mean to minimize the human workload. In our approach, we use an online knowledge base that provides libraries with object models and task specifications, and offer services to support the user (and the robot) during programming, deployment and execution

Natural language programming of industrial robots

In this paper, we introduce a method to use written natural language instructions to program assembly tasks for industrial robots. In our application, we used a state-of-the-art semantic and syntactic parser together with semantically rich world and skill descriptions to create highlevel symbolic task sequences. From these sequences, we generated executable code for both virtual and physical robot systems. Our focus lays on the applicability of these methods in an industrial setting with real-time constraints

Robotic Gift Wrapping or a Glance at the Present State in Santa's Workshop

This work presents a robotic implementation of Christmas gift wrapping. Handling paper is a challenging task for an industrial robot as it easily tears and folds in unexpected ways. In this application, a dual-arm industrial robot with simple two-finger grippers was used, and the robot was programmed using a standard position-based approach. The wrapping was accomplished with the help of plastic spatulas, and although the speed of the final wrapping was slower than an average human, the gift wrapper application became a success on its tour around Sweden during the Christmas commerce 2015

Instructing Industrial Robots Using High-Level Task Descriptions

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they have to become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor- controlled robot tasks. The robot motions are expressed using constraints, and a number of simple constrained motions can be combined into a robot skill. The skill can be stored in a database together with a semantic description, which en- ables reuse and reasoning. The main contributions of the thesis are 1) develop- ment of ontologies for robot devices and skills, 2) a user interface that provides programming support for task descriptions in unstructured natural language and 3) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user. These parameters are described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in four peer-reviewed papers. The first covers knowledge-based instruction and the system architecture. The two following papers describe the natural language programming feature of the system as well as a description of the user interface. The fourth and last paper describes the code generation step, thus connecting the high-level language instructions to real-time executable code