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

Robotics handbook. Version 1: For the interested party and professional

This publication covers several categories of information about robotics. The first section provides a brief overview of the field of Robotics. The next section provides a reasonably detailed look at the NASA Robotics program. The third section features a listing of companies and organization engaging in robotics or robotic-related activities; followed by a listing of associations involved in the field; followed by a listing of publications and periodicals which cover elements of robotics or related fields. The final section is an abbreviated abstract of referred journal material and other reference material relevant to the technology and science of robotics, including such allied fields as vision perception; three-space axis orientation and measurement systems and associated inertial reference technology and algorithms; and physical and mechanical science and technology related to robotics

The power of physical representations

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations

Physics-, social-, and capability- based reasoning for robotic manipulation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 124-128).Robots that can function in human-centric domains have the potential to help humans with the chores of everyday life. Moreover, dexterous robots with the ability to reason about the maneuvers they execute for manipulation tasks can function more autonomously and intelligently. This thesis outlines the development of a reasoning architecture that uses physics-, social-, and agent capability-based knowledge to generate manipulation strategies that a dexterous robot can implement in the physical world. The reasoning system learns object affordances through a combination of observations from human interactions, explicit rules and constraints imposed on the system, and hardcoded physics-based logic. Observations from humans performing manipulation tasks are also used to develop a unique manipulation repertoire suitable for the robot. The system then uses Bayesian Networks to probabilistically determine the best manipulation strategies for the robot to execute on new objects. The robot leverages this knowledge during experimental trials where manipulation strategies suggested by the reasoning architecture are shown to perform well in new manipulation environments.by Kenton J. Williams.S.M

Large Scale Qualitative Spatio-Temporal Reasoning

This thesis considers qualitative spatio-temporal reasoning (QSTR), a branch of artificial intelligence that is concerned with qualitative spatial and temporal relations between entities. Despite QSTR being an active area of research for many years, there has been comparatively little work looking at large scale qualitative spatio-temporal reasoning - reasoning using hundreds of thousands or millions of relations. The big data phenomenon of recent years means there is now a requirement for QSTR implementations that will scale effectively and reason using large scale datasets. However, existing reasoners are limited in their scalability, what is needed are new approaches to QSTR. This thesis considers whether parallel distributed programming techniques can be used to address the challenges of large scale QSTR. Specifically, this thesis presents the first in-depth investigation of adapting QSTR techniques to work in a distributed environment. This has resulted in a large scale qualitative spatial reasoner, ParQR, which has been evaluated by comparing it with existing reasoners and alternative approaches to large scale QSTR. ParQR has been shown to outperform existing solutions, reasoning using far larger datasets than previously possible. The thesis then considers a specific application of large scale QSTR, querying knowledge graphs. This has two parts to it. First, integrating large scale complex spatial datasets to generate an enhanced knowledge graph that can support qualitative spatial reasoning, and secondly, adapting parallel, distributed QSTR techniques to implement a query answering system for spatial knowledge graphs. The query engine that has been developed is able to provide solutions to a variety of spatial queries. It has been evaluated and shown to provide more comprehensive query results in comparison to using quantitative only techniques