Author name cluster

Lars-Peter Ellekilde

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

ICRA Conference 2018 Conference Paper

Adapting Parameterized Motions Using Iterative Learning and Online Collision Detection

Johan Sund Laursen
Lars Carøe Sørensen
Ulrik Pagh Schultz
Lars-Peter Ellekilde
Dirk Kraft

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.

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ICRA Conference 2018 Conference Paper

Optimisation of Trap Design for Vibratory Bowl Feeders

Simon Faarvang Mathiesen
Lars Carøe Sørensen
Dirk Kraft
Lars-Peter Ellekilde

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.

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IROS Conference 2016 Conference Paper

Kernel density estimation based self-learning sampling strategy for motion planning of repetitive tasks

Thomas Fridolin Iversen
Lars-Peter Ellekilde

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.

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IROS Conference 2015 Conference Paper

Automatic error recovery in robot assembly operations using reverse execution

Johan Sund Laursen
Ulrik Pagh Schultz
Lars-Peter Ellekilde

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.

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IROS Conference 2012 Conference Paper

Applying a learning framework for improving success rates in industrial bin picking

Lars-Peter Ellekilde
Jimmy A. Jørgensen
Dirk Kraft
Norbert Krüger
Justus H. Piater
Henrik Gordon Petersen

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.

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ICRA Conference 2009 Conference Paper

Control of mobile manipulator using the dynamical systems approach

Lars-Peter Ellekilde
Henrik I. Christensen

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.

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IROS Conference 2006 Conference Paper

Design and Test of Object Aligning Grippers for Industrial Applications

Lars-Peter Ellekilde
Henrik Gordon Petersen

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

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