Lars-Peter Ellekilde

Dense 3D Map Construction for Indoor Search and Rescue

The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three-dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstructured indoor environments. A key challenge in this scenario is that the robot moves in 6D and no odometry information is available. An extended information filter (EIF) is used to estimate the state vector containing the sequence of camera poses and some selected 3D point features in the environment. Data association is performed using a combination of scale invariant feature transformation (SIFT) feature detection and matching, random sampling consensus (RANSAC), and least square 3D point sets fitting. Experimental results are provided to demonstrate the effectiveness of the techniques developed.

Analysis of human peg-in-hole executions in a robotic embodiment using uncertain grasps

In this paper, we perform a quantitative and qualitative analysis of human peg-in-hole operations in a tele-operating setting with a moderate degree of dexterity. Peg-in-hole operation with different starting configurations are performed with the aim to derive a strategy for performing such actions with a robot. The robot is a 6 DoF robot arm with the dexterous 3 finger SDH-2 gripper. From the extracted data, we can distill important insights about (1) feasible grasps depending on the pegs pose, (2) the object trajectory, (3) the occurrence of a particular force-torque pattern during the monitoring of the action and (4) an appropriate insertion strategy. At the end of the paper, we discuss consequences for using these insights for deriving algorithms for robot execution of peg-in-hole actions with dexterous manipulators.

Tool Center Trajectory Planning for Industrial Robot Manipulators Using Dynamical Systems

In this paper we generalize previous work in which the fixed points of dynamical systems were used to construct obstacle-avoiding, goal-attracting trajectories for robots to more complex attractors such as limit cycles in the form of closed planar curves. Following the development of a formalism for dealing with a mechanical system, some of whose coordinates are constrained to follow the trajectories of a set of coupled differential equations, we discuss how to construct, analyze, and solve a planar dynamical system whose limit set is one or more user-specified closed curves or limit cycles. This work finds its relevance in a wide range of applications. Our focus has mainly been on planning tool trajectories for industrial robot manipulators with applications such as welding and painting. However, the generalization from fixed points to limit cycles is also applicable when controlling automatic guided vehicles.

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.

Control of mobile manipulator using the dynamical systems approach

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.

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.

Towards a Domain-Specific Language for Reversible Assembly Sequences

Programming industrial robots for small-sized batch production of assembly operations is challenging due to the difficulty of precisely specifying general yet robust assembly operations. We observe that as the complexity of assembly increases, so does the likelihood of errors. We propose that certain classes of errors during assembly operations can be addressed using reverse execution, allowing the robot to temporarily back out of an erroneous situation, after which the assembly operation can be automatically retried. Moreover, reversibility can be used to automatically derive a disassembly sequence from a given assembly sequence, or vice versa.

Automatic Grasp Generation and Improvement for Industrial Bin-Picking

This paper presents work on automatic grasp generation and grasp learning for reducing the manual setup time and increase grasp success rates within bin-picking applications. We propose an approach that is able to generate good grasps automatically using a dynamic grasp simulator, a newly developed robust grasp quality measure and post-processing methods. In addition we present an offline learning approach that is able to adjust grasp priorities based on prior performance. We show, on two real world platforms, that one can replace manual grasp selection by our automatic grasp selection process and achieve comparable results and that our learning approach can improve system performance significantly. Automatic bin-picking is an important industrial process that can lead to significant savings and potentially keep production in countries with high labour cost rather than outsourcing it. The presented work allows to minimize cycle time as well as setup cost, which are essential factors in automatic bin-picking. It therefore leads to a wider applicability of bin-picking in industry.

Technologies for the Fast Set-Up of Automated Assembly Processes

In this article, we describe technologies facilitating the set-up of automated assembly solutions which have been developed in the context of the IntellAct project (2011–2014). Tedious procedures are currently still required to establish such robot solutions. This hinders especially the automation of so called few-of-a-kind production. Therefore, most production of this kind is done manually and thus often performed in low-wage countries. In the IntellAct project, we have developed a set of methods which facilitate the set-up of a complex automatic assembly process, and here we present our work on tele-operation, dexterous grasping, pose estimation and learning of control strategies. The prototype developed in IntellAct is at a TRL4 (corresponding to ‘demonstration in lab environment’).

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

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.