Achieving both the flexibility and robustness required to advance the use of robotics in small and medium-sized productions is an essential but difficult task. A fundamental problem is making the robot run blindly without additional sensors while still being robust to uncertainties and variations in the assembly processes. In this paper, we address the use of parameterized motions suitable for blind execution and robust to uncertainties in the assembly process. Collisions and incorrect assemblies are detected based on robot motor currents while motion parameters are updated based on Bayesian Optimization utilizing Gaussian Process learning. This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups. The result is a simple and straightforward system which helps the user automatically find robust and uncertainty-tolerant motions. We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system.

Vibratory bowl feeders (VBFs) are a widely used option for industrial part feeding, but their design is still largely manual. A subtask of VBF design is determining an optimal parameter set for the passive devices, called traps, which the VBF uses to ensure correct part orientation. This paper proposes a fast and robust strategy for optimising traps, which makes use of dynamic simulation to efficiently evaluate the performance of parameter sets. The optimisation strategy is based on Bayesian Optimisation and selects new parameter sets to evaluate, using a modified Upper Confidence Bound with regression by Kernel Density Estimation as function estimator. The optimisation is run for four different traps with an industrial part and the best parameter sets are tested for robustness in simulation. The traps are then combined to create two sequences performing orientation of the parts and the designs are prototyped and tested on a real VBF.

The main contribution of this paper is a method for optimizing the layout of workcells taking into consideration both the reachability of the robot as well as the expected cycle time. To analyse the reachability for systems using sensors to pose estimate objects, the method uses a combination of discrete samples over the space in which objects are located and a manipulability measure based on the determinant of the manipulator Jacobian. To compute the expected cycle time for the robot, the method includes a simulated controller, which is optimized to estimate the performance of the physical robot. For the optimization of the workcell layout the proposed method based on applying Gaussian penalties in local minima is compared to three existing methods for global optimization. For the optimization of the simulated controller three different local methods are compared along with one global.

Vibratory parts feeders with mechanical orienting devices are used extensively in the assembly automation industry. Even so, the design process is based on trial-and-error approaches and is largely manual. In this paper, a methodology is presented for automatic design of this type of feeder. The approach uses dynamic simulation for generating the necessary data for configuring a feeder with a sequence of mechanical orienting devices called traps, with the goal of reorienting all parts from a random to fixed orientation. Then, a fast algorithm for facilitating this configuration task automatically is developed from domain specific knowledge. Finally, the algorithm is validated on three industrial cases and its drawbacks and strengths are discussed in detail.

Gripper design is nowadays an area of ongoing research activity. The problem of creating a generic and automated gripper design approach tailored for a specific task is still far from solved. In this paper, we propose a new method of generating finger cut-outs aimed at simplifying the design process of doing so. This method takes root in the idea of using the imprint to produce the finger geometry. We furthermore provide a verification of our newly introduced imprinting method and a comparison to the previously introduced parametrized geometry method. This verification is done through a set of grasping experiments performed in simulation on two objects with geometry features based on those found in industrial setting.

This paper introduces a new sampling strategy and shows that superior performance can be obtained for a range of sampling based robotic motion planners, used in scenarios with low task variance, as found in many vision guided pick and place operations. The strategy uses kernel density estimation to identify regions with high probability of containing configurations being part of feasible solutions, and use the estimation to bias sampling. The kernel densities are initialized with a uniform distribution and are continuously updated, whenever paths are successfully planned and optimized. The system is thereby self-learning and improves performance over time. The sampler is tested on a variety of planners and against other sampling methods in two different scenarios containing robotic arms with 6 degrees of freedom and compared to a state-of-the-art optimization based planning algorithm. Tests show that the sampler learns fast and improves both time taken for solving problems and the quality of the resulting paths compared to other samplers.

Robotic assembly tasks are in general difficult to program and require a high degree of precision. As the complexity of the task increases it becomes increasingly unlikely that tasks can always be executed without errors. Preventing errors beyond a certain point is economically infeasible, in particular for small-batch productions. As an alternative, we propose a system for automatically handling certain classes of errors instead of preventing them. Specifically, we show that many operations can be automatically reversed. Errors can be handled through automatic reverse execution of the control program to a safe point, from which forward execution can be resumed. This paper describes the principles behind automatic reversal of robotic assembly operations, and experimentally demonstrates the use of a domain-specific language that supports automatic error handling through reverse execution. Our contribution represents the first experimental demonstration of reversible computing principles applied to industrial robotics.

In this paper, we present what appears to be the first studies of how to apply learning methods for improving the grasp success probability in industrial bin picking. Our study comprises experiments with both a pneumatic parallel gripper and a suction cup. The baseline is a prioritized list of grasps that have been chosen manually by an experienced engineer. We discuss generally the probability space for success probability in bin picking and we provide suggestions for robust success probability estimates for difference sizes of experimental sets. By performing grasps equivalent to one or two days in production, we show that the success probabilities can be significantly improved by the proposed learning procedure.

The combination of a mobile platform and a manipulator, known as a mobile manipulator, provides a highly flexible system, which can be used in a wide range of applications, especially within the field of service robotics. One of the challenges with mobile manipulators is the construction of control systems, enabling the robot to operate safely in potentially dynamic environments. In this paper we will present work in which a mobile manipulator is controlled using the dynamical systems approach. The method presented is a two level approach in which competitive dynamics are used both for the overall coordination of the mobile platform and the manipulator as well as the lower level fusion of obstacle avoidance and target acquisition behaviors.

In this paper we present a new concept for gripping objects in industrial applications. We assume that a priori, the object pose is only known with a relative low accuracy. Despite this, our method can lead to high accuracy gripping suitable for e.g. industrial assembly. Our concept is to augment a simple parallel gripper by mounting a set of object specific jaws. Given the right shapes these jaws enable the gripper to automatically align the object, and thereby compensate for errors in the original object pose estimation. We introduce a couple of automatic and semi-automatic design strategies for deriving these shapes and describe how these can be verified by simulations using rigid body dynamics. The output of our system is a CAD model for the jaws. We prove our concept with a number of experiments, which are also used to verify the coherence between the simulation and real world