Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

ROTEX-TRIIFEX: Proposal for a joint FRG-USA telerobotic flight experiment

The concepts and main elements of a RObot Technology EXperiment (ROTEX) proposed to fly with the next German spacelab mission, D2, are presented. It provides a 1 meter size, six axis robot inside a spacelab rack, equipped with a multisensory gripper (force-torque sensors, an array of range finders, and mini stereo cameras). The robot will perform assembly and servicing tasks in a generic way, and will grasp a floating object. The man machine and supervisory control concepts for teleoperation from the spacelab and from ground are discussed. The predictive estimation schemes for an extensive use of time-delay compensating 3D computer graphics are explained

Human-Robot Collaboration in Automotive Assembly

In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robots’ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC

Human-Robot Collaborative Force-Controlled Micro-Drilling for Advanced Manufacturing and Medical Applications

Robotic drilling finds applications in diverse fields ranging from advanced manufacturing to the medical industry. Recent advances in low-cost, and human-safe, collaborative robots (e.g., Sawyer) are enabling us to rethink the possibilities in which robots can be deployed for such tedious and time-consuming tasks. This thesis presents a robotic drilling methodology with features of force-control enabled micro-drilling and human-robot collaboration to reduce programming efforts and enhance drilling performance. A Sawyer robot from Rethink Robotics, which offers safe physical interactions with a human co-worker, kinesthetic teaching, and force control, is used as the test bed. The robot’s end-effector was equipped with a Dremel drill fit into a housing, which was custom designed and 3D-printed using an Object Prime 3D-printer. The proposed approach applies human-robot collaboration in two cases. First, a human kinesthetically teaches a set of drill coordinates by physically holding the robot and guiding it to those locations. The robot then executes the drilling task by moving to these recorded locations. This thereby avoids the need to specify the drill coordinates with respect to a fixed reference frame, leading to reduction in programming effort and setup time while transitioning between different drilling jobs. Second, drilled hole quality is shown to be enhanced when a human provides nominal physical support to the robot during certain drilling tasks. An experimental analysis of the impact of force control on micro-drilling revealed that the proposed robotic system is capable of successfully drilling holes with a drill bit of 0.5 mm diameter with an error of +/- 0.05 mm, without breaking it for more than 100 holes. The proposed robotic drilling was validated in the following application domain: micro-drilling for composite repairs based on the through-thickness reinforcement (TTR) technique. For this purpose, sandwich beam samples were prepared by using pre-preg unidirectional carbon fabric face sheets with a honeycomb core, and they were subjected to four-point static loading until de-bonding occurred between the face sheet and the core. The samples were then repaired using the TTR technique, where the proposed robotic drilling was used to drill holes of 0.75 mm diameter in the damaged area of the sample and carbon fiber rods and with low-viscosity epoxy, were manually inserted into these drilled holes. The results revealed that the sandwich beam regained effective compressive strength after going through the TTR technique. Experiments also reveal the potential of the proposed robotic drilling technique in aerospace and automotive manufacturing involving drilling in complex postures and micro-drilling for orthopedic applications

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