Henrik Gordon Petersen

A new method for estimating parameters of a dynamic robot model

The need for open model-based robot controllers becomes more and more important with the increasing quality of autonomous joint motion planners and the increasing amount of applications for these. An important item in a model-based controller is a precise dynamical model of the robot. Such a model is designed in terms of various inertial and friction parameters that must be either measured directly or determined experimentally. In this paper, we present a new method for determining parameters of a dynamical robot model based on experiments. We compare the method with existing methods both theoretically and experimentally, and show that we achieved some improvements in the parameter values by using the new method. The experiments were carried out on the two first links of a seven-axis Mitsubishi PA-10 robot.

Task curve planning for painting robots. I. Process modeling and calibration

This paper reports the first phase of a project whose aim is the automatic generation of tool center trajectories for robots engaged in spray painting of arbitrary surfaces. The first phase consists of proposing a mathematical model for the paint flux field within the spray cone. We have called this quantity the paint flux field partly to emphasize that it is a vector field and partly to distinguish it from a paint flux distribution function, which describes the angular variation of the flux field within the spray cone. It is shown that this flux field can be derived from experimental measurements performed by robots, of coverage profiles of paint strips on flat plates by solving a singular integral equation. This flux field is derived both for published experimental data as well as two sets of data from experiments performed by the authors. The correctness of the model is demonstrated by using the underlying distribution function to predict coverage profiles for other experiments in which the spray gun is no longer vertical to the plane surface.

Pose estimation using local structure-specific shape and appearance context

We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape information for both edge and texture structures. This is achieved by defining feature space relations which describe the neighborhood of a descriptor. By quantitative evaluations, we show that our descriptors provide high discriminative power compared to state of the art approaches. In addition, we show how to utilize this for the estimation of the alignment pose between two point sets. We present experiments both in controlled and real-life scenarios to validate our approach.

In Search of Inliers: 3D Correspondence by Local and Global Voting

We present a method for finding correspondence between 3D models. From an initial set of feature correspondences, our method uses a fast voting scheme to separate the inliers from the outliers. The novelty of our method lies in the use of a combination of local and global constraints to determine if a vote should be cast. On a local scale, we use simple, low-level geometric invariants. On a global scale, we apply covariant constraints for finding compatible correspondences. We guide the sampling for collecting voters by downward dependencies on previous voting stages. All of this together results in an accurate matching procedure. We evaluate our algorithm by controlled and comparative testing on different datasets, giving superior performance compared to state of the art methods. In a final experiment, we apply our method for 3D object detection, showing potential use of our method within higher-level vision.

An Adaptable Robot Vision System Performing Manipulation Actions With Flexible Objects

This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g, for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape. Note to Practitioners - The aim of this work was to facilitate the setup of robot-based automation of delicate handling of flexible objects consisting of a uniform material. As examples, we have considered how to optimally maneuver flexible objects through a hole without colliding and how to place flexible objects on a flat surface with minimal introduction of internal stresses in the object. Given the material properties of the object, we have demonstrated in these two applications how the system can be programmed with minimal requirements of human intervention. Rather than being an integrated system with the drawbacks in terms of lacking flexibility, our system should be viewed as a library of new technologies that have been proven to work in close to industrial conditions. As a rather basic, but necessary part, we provide a technology for determining the shape of the object when passing on, e.g., a conveyor belt prior to being handled. The main technologies applicable for the manipulated objects are: A method for real-time tracking of the flexible objects during manipulation, a method for model-based offline prediction of the static deformation of grasped, flexible objects and, finally, a method for optimizing specific tasks based on both simulated and real-world executions.

Transfer of assembly operations to new workpiece poses by adaptation to the desired force profile

In this paper we propose a new algorithm that can be used for adaptation of robot trajectories in automated assembly tasks. Initial trajectories and forces are obtained by demonstration and iteratively adapted to specific environment configurations. The algorithm adapts Cartesian space trajectories to match the forces recorded during the human demonstration. Experimentally we show the effectiveness of our approach on learning of Peg-in-Hole (PiH) task. We performed our experiments on two different robotic platforms with workpieces of different shapes.

Motion planning efficient trajectories for industrial bin-picking

This paper presents an algorithm for planning efficient trajectories in a bin-picking scenario. The presented algorithm is designed to provide paths, which are applicable for typical industrial manipulators, and does not require customized research interfaces to the robot controller. The method provides paths (almost) instantaneously, which is important for running efficiently in production. To achieve this, the method utilizes that all motions start and end within sub-volumes of the work envelope. A database of paths can thus be pre-computed, such that all paths are optimized with respect to a specified cost function, thereby ensuring close to optimal solutions. When queried, the method searches the database for a feasible path candidate and adapts it to the specific query. To achieve an efficient execution on the robot, blends are added to ensure a smooth transition between segments. Two algorithms for calculating feasible blends based on the clearance between robot and obstacles are therefore provided. Finally, the method is tested in a real bin-picking application where it solves queries efficiently and provides paths, which are significantly faster than those currently used for bin-picking in the industry.

Local shape feature fusion for improved matching, pose estimation and 3D object recognition

We provide new insights to the problem of shape feature description and matching, techniques that are often applied within 3D object recognition pipelines. We subject several state of the art features to systematic evaluations based on multiple datasets from different sources in a uniform manner. We have carefully prepared and performed a neutral test on the datasets for which the descriptors have shown good recognition performance. Our results expose an important fallacy of previous results, namely that the performance of the recognition system does not correlate well with the performance of the descriptor employed by the recognition system. In addition to this, we evaluate several aspects of the matching task, including the efficiency of the different features, and the potential in using dimension reduction. To arrive at better generalization properties, we introduce a method for fusing several feature matches with a limited processing overhead. Our fused feature matches provide a significant increase in matching accuracy, which is consistent over all tested datasets. Finally, we benchmark all features in a 3D object recognition setting, providing further evidence of the advantage of fused features, both in terms of accuracy and efficiency.

Inverse kinematics by numerical and analytical cyclic coordinate descent

Cyclic coordinate descent (CCD) inverse kinematics methods are traditionally derived only for manipulators with revolute and prismatic joints. We propose a new numerical CCD method for any differentiable type of joint and demonstrate its use for serial-chain manipulators with coupled joints. At the same time more general and simpler to derive, the method performs as well in experiments as the existing analytical CCD methods and is more robust with respect to parameter settings. Moreover, the numerical method can be applied to a wider range of cost functions.

Robust Control for High-Speed Visual Servoing Applications

This paper presents a new control scheme for visual servoing applications. The approach employs quadratic optimization, and explicitly handles both joint position, velocity and acceleration limits. Contrary to existing techniques, our method does not rely on large safety margins and slow task execution to avoid joint limits, and is hence able to exploit the full potential of the robot. Furthermore, our control scheme guarantees a well-defined behavior of the robot even when it is in a singular configuration, and thus handles both internal and external singularities robustly. We demonstrate the correctness and efficiency of our approach in a number of visual servoing applications, and compare it to a range of previously proposed techniques.