Task scheduling for dual-arm industrial robots through Constraint Programming

In a society where more and more production becomes automated it demands robots that are as flexible and versatile as humans. Such flexibility demands automatic scheduling of tasks. In this thesis we approach the problem using Constraint Programming and through a case study we present a model for a dual-armed robot that is able to deal with a more flexible workload. We also introduce filters to cut down the runtime of the solver. To evaluate the model we tested it on 6 solvers; G12/FD, JaCoP, Gecode, or-tools, Opturion CPX and Choco3. The results show that the model can produce a solution as good as the one manually implemented for the case study. We introduce filters on the domains of some of the variables and they made an improvement on the runtime for many of the solvers. We also found that the runtime of the solvers varied a lot and could range from several hours to just a few milliseconds using the same data. Unfortunately, in many of the tests the solvers did not complete their searches within the time limit of 4 hours. In some cases when using MiniZinc version 2.0.1, the solvers were not able to read the FlatZinc files. The fastest solver in our tests was Gecode using MiniZinc version 2.0.1

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