Surface finishing is a time-consuming, dangerous task, difficult to automate despite its necessity in many manufacturing processes. Its automation, particularly through robotics, increases productivity and relieves workers from health-critical tasks. However, challenges remain, as automated offline planning tools can result in certain areas being either neglected or overly processed. Ergodic control offers the possibility to cover target probability distributions in an online manner, by taking into account the observed coverage history. However, existing ergodic control approaches provide little flexibility in designing and adapting coverage strategies. Moreover, they come with simplifying assumptions, such as point-based dynamics, which are no longer valid for tasks where the robot is in contact with strongly varying curvatures on non-trivial surface geometries. In this work, we introduce a closed-form ergodic control framework that includes the tool imprint in the system modeling while simultaneously permitting the intuitive transfer of finish strategies, namely preferred motion directions. We build on the Spectral Multiscale Coverage (SMC) approach, augmenting it with a tool imprint model, as well as both target distributions and state-dependent movement directions extracted from human demonstrations. Through evaluations in a surface finishing task using a torque-controlled, 7-DoF, robot arm we show that our approach optimally covers surfaces according to the tool contact area, with robust error convergence.

Learning from Demonstration (LfD) is a widely used approach for teaching robot motion, but more sophisticated strategies are required to address complex tasks such as surface processing. Sanding is an example where comprehensive strategies are necessary to ensure complete and efficient coverage of the surface of a workpiece. In this paper, we present a system that captures human motions and contact forces during surface processing using a powered sanding tool. We provide a publicly available dataset that consists of demonstrations for various geometric shapes with the goal to extract robot execution strategies through LfD from a variety of users. This is in contrast to conventional LfD, which generates a policy directly from one or multiple trajectories provided by a single user. Further, we provide a data analysis that reveals key insights into how humans adapt their strategies to different surface geometries and extract robot execution strategies from it. Finally, we conduct two basic robotic experiments justifying the approach of strategy extraction. Our findings contribute to the understanding of human surface-processing behavior and lay the foundation for developing more effective robotic surface processing strategies.

The initial design of a robotic workcell layout has a large impact on the feasibility and performance of the intended robotic tasks. We define this layout design as a constrained nonlinear optimization problem that aims to optimize the placement of workcell components by minimizing the distance traveled between task sequences while maximizing the robot’s manipulability. Suitable constraints guarantee the reachability as well as the absence of collisions. We solve this optimization problem via a genetic algorithm, and demonstrate it in three scenarios for a dual-arm robotic system that assembles product variants out of aluminum profiles.

Technology has sufficiently matured to enable, in principle, flexible and autonomous robotic assembly systems. However, in practice, it requires making all the relevant (implicit) knowledge that system engineers and workers have – about products to be assembled, tasks to be performed, as well as robots and their skills – available to the system explicitly. Only then can the planning and execution components of a robotic assembly pipeline communicate with each other in the same language and solve tasks autonomously without human intervention. This is why we have developed the Factory of the Future (FoF) ontology. At its core, this ontology models the tasks that are necessary to assemble a product and the robotic skills that can be employed to complete said tasks. The FoF ontology is based on existing standards. We started with theoretical considerations and iteratively adapted it based on practical experience gained from incorporating more and more components required for automated planning and assembly. Furthermore, we propose tools to extend the ontology for specific scenarios with knowledge about parts, robots, tools, and skills from various sources. The resulting scenario ontology serves us as world model for the robotic systems and other components of the assembly process. A central runtime interface to this world model provides fast and easy access to the knowledge during execution. In this work, we also show the integration of a graphical user front-end, an assembly planner, a workspace reconfigurator, and more components of the assembly pipeline that all communicate with the help of the FoF ontology. Overall, our integration of the FoF ontology with the other components of a robotic assembly pipeline shows that using an ontology is a practical method to establish a common language and understanding between the involved components.

We present an approach for the autonomous, robust execution of peg-in-hole assembly tasks. We build on a sampling-based state estimation framework, in which samples are weighted according to their consistency with the position and joint torque measurements. The key idea is to reuse these samples in a motion generation step, where they are assigned a second task-specific weight. The algorithm thereby guides the peg towards the goal along the configuration space. An advantage of the approach is that the user only needs to provide: the geometry of the objects as mesh data, as well as a rough estimate of the object poses in the workspace, and a desired goal state. Another advantage is that the local, online nature of our algorithm leads to robust behavior under uncertainty. The approach is validated in the case of our robotic setup and under varying uncertainties for the classical peg-in-hole problem subject to two different geometries.

In fast changing assembly scenarios, it is required to adapt the task execution to the current state of the setup without extensive calibration routines. Therefore, it is important to estimate the geometric uncertainties and contact states during the assembly execution. We use a sequential Monte Carlo (SMC) method to track the relative poses between workpieces during a robotic assembly based on joint torque and position measurements only. In contrast to existing approaches, we focus on assembly tasks where the workpiece is not fixed in the workcell, but can, for example, slide on a table surface. We propose a new constraint-based propagation model for the SMC approach: a compensation motion for the samples dependent on the violation of contact constraints is derived. This allows us to track the motion of the workpieces in cases where a common random diffusion model fails. The method is evaluated with experiments using an assembly scenario with two KUKA LBR iiwa robot arms and shows accurate tracking performance.

The observation of robotic assembly tasks is required as feedback for decisions and adaption of the task execution on the current situation. A sequential Monte Carlo observation algorithm is proposed, which uses a fast and accurate collision detection algorithm as a reference model for the contacts between complex shaped parts. The main contribution of the paper is the extension of the classic random motion model in the propagation step with sampling methods known from the domain of probabilistic roadmap planning in order to increase the sample density in narrow passages of the configuration space. As a result, the observation performance can be improved and a risk of sample impoverishment reduced. Experimental validation is provided for a peg-in-hole task executed by a lightweight-robot arm equipped with joint torque sensors.