Adam Wolniakowski

Automatic Evaluation of Task-Focused Parallel Jaw Gripper Design

In this paper, we suggest gripper quality metrics that indicate the performance of a gripper given an object CAD model and a task description. Those, we argue, can be used in the design and selection of an appropriate gripper when the task is known. We present three different gripper metrics that to some degree build on existing grasp quality metrics and demonstrate these on a selection of parallel jaw grippers. We furthermore demonstrate the performance of the metrics in three different industrial task contexts.

Task and context sensitive optimization of gripper design using dynamic grasp simulation

In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both gripper parameters and parameters of the finger geometry. We demonstrate our gripper optimization on a parallel jaw type gripper which we have parametrized in a 11 dimensional space. We furthermore present a parametrization of the grasping task and context, which is essential as input to the computation of gripper performance. We exemplify the feasibility of our approach by computing several optimized grippers on a real world industrial object in three different scenarios.

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both selected gripper mechanism parameters, and parameters of the finger geometry. We suggest six gripper quality indices that indicate different aspects of the performance of a gripper given a CAD model of an object and a task description. These quality indices are then used to learn task-specific finger designs based on dynamic simulation. We demonstrate our gripper optimization on a parallel finger type gripper described by twelve parameters. We furthermore present a parametrization of the grasping task and context, which is essential as an input to the computation of gripper performance. We exemplify important aspects of the indices by looking at their performance on subsets of the parameter space by discussing the decoupling of parameters and show optimization results for two use cases for different task contexts. We provide a qualitative evaluation of the obtained results based on existing design guidelines and our engineering experience. In addition, we show that with our method we achieve superior alignment properties compared to a naive approach with a cutout based on the “inverse of an object”. Furthermore, we provide an experimental evaluation of our proposed method by verifying the simulated grasp outcomes through a real-world experiment.

Optimizing grippers for compensating pose uncertainties by dynamic simulation

Gripper design process is one of the interesting challenges in the context of grasping within industry. Typically, simple parallel-finger grippers, which are easy to install and maintain, are used in platforms for robotic grasping. The context switches in these platforms require frequent exchange of gripper fingers to accommodate grasping of new products, while subjected to numerous constraints, such as workcell uncertainties due to the vision systems used. The design of these fingers consumes the man-hours of experienced engineers, and involves a lot of trial-and-error testing. In our previous work, we have presented a method to automatically compute the optimal finger shapes for defined task contexts in simulation. In this paper, we show the performance of our method in an industrial grasping scenario. We first analyze the uncertainties of the used vision system, which are the major source of grasping error. Then, we perform the experiments, both in simulation and in a real setting. The experiments confirmed the validity of our approach. The computed finger design was employed in a real industrial assembly scenario.

Compensating Pose Uncertainties Through Appropriate Gripper Finger Cutouts

Abstract The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method (Wolniakowski et al., 2013, 2015). We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

Application of Unfalsified Control Theory in Controlling MAV

Controlling the flight of Micro Aerial Vehicles (MAV) is a highly challenging task, mostly due to nonlinearity of their models and highly varying longitudinal and lateral derivatives coefficients [. As such, it requires a proper form of robust control. The demand for such control is very high, as it is required in many applications. The following paper presents the application of Unfalsified Control Theory developed by Michael G. Safonov [1, 2, 6, . This interesting approach is based on the adaptive switching control, and does not require any previous knowledge of the controlled plant. The controlled dynamics is decoupled due to longitudinal and lateral motion of the Bell 540 single-delta wing micro aerial vehicle. The work involves design and simulation of the proper robust controller. The simulation is based on already obtained nominal model of the Bell 540 vehicle [. The developed controllers were proved to be efficient, based on performed calculations and simulation in Matlab.

Compensating Position Measurement Errors for the IR Static Triangulation System

Determination of an object’s position in a given reference frame is the main purpose of a navigation system. This can be done in many different ways and depending on the chosen method, and measurement equipment, produce more or less accurate measurements. Precise indoor navigation is particularly important due to the ever more dynamic development of autonomous systems in many areas of industry. Unfortunately, the measurement accuracy of indoor navigation systems is reduced due to the influences of walls and other obstacles that interfere with the measurement signals causing so called multipathing. Multipathing is often reduced by creating error maps, which is a labor-intensive task. In this paper, we present a method in which a robot manipulator is used as the reference positioning system to determine such mapping. In the next step the mapping, using a second order polynomial, is determined which maps the measured disturbed object’s position from the triangulation system into real object’s position.

Designing Fingers in Simulation based on Imprints

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.

Using Online Modelled Spatial Constraints for Pose Estimation in an Industrial Setting

We introduce a vision system that is able to on-line learn spatial constraints to improve pose estimation in terms of correct recognition as well as computational speed. By making use of a simulated industrial robot system performing various pick and place tasks, we show the effect of model building when making use of visual knowledge in terms of visually extracted pose hypotheses as well as action knowledge in terms of pose hypotheses verified by action execution. We show that the use of action knowledge significantly improves the pose estimation process.

Erratum: Automatic Evaluation of Task-Focused Parallel Jaw Gripper Design

Part of the acknowledgement for the paper starting on page 450 of this volume was inadvertently omitted. The full acknowledgement should read as follows: Acknowledgments. The research leading to these results has received funding from the European Communitys Seventh Framework Programme FP7/2007-2013 (Programme and Theme: ICT-2011.2.1, Cognitive Systems and Robotics) under grant agreement no. 600578, ACAT. The research has furthermore received founding from the Danish Council for Strategic Research under the grant agreement no. 12-131860, CARMEN. The research has also received funding from a project MB/WM/21/2013 realised by Bialystok University of Technology.