Wilm Decré

Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty

This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity- based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.

Extending iTaSC to support inequality constraints and non-instantaneous task specification

In [1], we presented our constraint-based programming approach, iTaSC1, that formulates instantaneous sensor-based robot tasks as constraint sets, and subsequently solves a corresponding least-squares problem to obtain control set points, such as desired joint velocities or joint torques. This paper further extends this approach, (i) by explicitly supporting the inclusion of inequality constraints in the task and (ii) by supporting a broader class of objective functions for translating the task constraints into robot motion. These extensions are made while retaining a tractable mathematical problem structure (a convex program). Furthermore, first results on extending the approach to non-instantaneous tasks are presented. As illustrated in the paper, the power of the approach lies (i) at its versatility to specify a wide range of robot behaviors and the ease of making task adjustments, and (ii) at its generic nature, that permits using systematic procedures to derive the underlying control equations.

Extending the iTaSC Constraint-based Robot Task Specification Framework to Time-Independent Trajectories and User-Configurable Task Horizons

In constraint-based programming, robot tasks are specified and solved as optimization problems with sets of constraints and one or multiple objective functions. In our previous work, we presented (i) a generic modeling approach for geometrically complex robot tasks, including the modeling of parametric uncertainty, in order to allow the robot task programmer to specify the optimization problem without explicitly writing down the different (possibly numerous and involved) constraint equations, and (ii) methods for solving these optimization problem online in the instantaneous case (reactive control), and offline in the non-instantaneous case (trajectory planning). This paper has two contributions. First, it extends our framework to include task constraints (e.g. tracking a curve) that are not given as explicit functions of time. These constraints are highly relevant in practice, for example to facilitate time-optimal path planning combined with other constraints. Second, it extends our framework to user-configurable task horizons when solving the optimization problem, to allow task programmers to make a trade-off between computational speed and (global) task optimality. Both of these novel framework extensions are illustrated by a time-optimal laser tracing experiment.

An application of constraint-based task specification and estimation for sensor-based robot systems

This paper shows the application of a systematic approach for constraint-based task specification for sensor-based robot systems to a laser tracing example. This approach integrates both task specification and estimation of geometric uncertainty in a unified framework. The framework consists of an application independent control and estimation scheme. An automatic derivation of controller and estimator equations is achieved, based on a geometric task model that is obtained using a systematic task modeling procedure. The paper details the systematic modeling procedure for the laser tracing task and elaborates on the task specific choice of two types of task coordinates: feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and uncertainty coordinates to model geometric uncertainty. Furthermore, the control and estimation scheme for this specific task is studied. Simulation and real world experimental results are presented for the laser tracing example.

An Optimization-Based Estimation and Adaptive Control Approach for Human-Robot Cooperation

This paper presents a novel robot programming approach for actively assisting humans in human-robot cooperation tasks. First, the paper discusses an invariant description-based parametric modeling approach for six degree-of-freedom motion trajectories. This generic approach facilitates building a library of motion models in a systematic way. Second, the paper presents a constrained optimization-based parameter estimation technique for estimating the motion model parameters. Both batch and recursive schemes are presented. Third, the paper presents a control architecture based on our constraint-based task specification approach iTaSC that supports including secondary task objectives or inequality constraints (for example joint limits) in the robot task definition. The control architecture is exemplified using the KUKA LWR 4 robot and Orocos robot control software. Experimental results clearly indicate the potential of the approach by showing significant lower human-robot interaction forces compared to classical admittance control.

Force-Sensorless and Bimanual Human-Robot Comanipulation Implementation using iTaSC

This paper demonstrates the iTaSC approach to a force-sensorless and bimanual human-robot comanipulation task on a tree-structured robot, comprising (i) co-manipulation of an object with a person, (ii) dynamic and static obstacle avoidance with its base, (iii) maintaining visual contact with the operator, and (iv) unnatural pose prevention. The task is implemented in a structured way in a reusable software framework. The paper presents a simple sensorless wrench-nulling control scheme, enabling direct human-robot interaction without the use of a force sensor. A video shows the performance of the full task in different scenarios. The paper includes quantitative results for different scenarios, to validate the ability to activate and deactivate, as well as to change the weights of different parts of the task in a stable way.

Haptic coupling with augmented feedback between two KUKA Light-Weight Robots and the PR2 robot arms

This paper discusses the theoretical background and practical implementation of a large-scale, low-performance haptic remote control setup. The experimental system consists of a pair of KUKA Light Weight Robots (LWR) coupled to a Willow Garage Personal Robot (PR2) via two different robotic frameworks. The haptic “performance” is, of course, not comparable to dedicated haptic applications, but has its use as a test-bed for interaction between “legacy” service robot systems, that have not been especially designed for mutual haptic interaction. We discuss some major application problems, and the future work needed for nonuniform robot coupling. Beside haptic coupling, we provide the human operator with visual feedback. To this end, the head movements of the human operator are coupled to the head movement of the PR2 and the images of the eye cameras are displayed to the human operator using a wearable display. The presented teleoperation application is furthermore an example of the integration of two component-based robotic frameworks namely OROCOS (Open Robot Control Software)and ROS (Robot Operating System) Experimental results regarding the haptic coupling are presented using an “artistic” painting task for qualitative results, and a hard contact at the slave side for quantitative results.

Application of a Generic Constraint-Based Programming Approach to an Industrially Relevant Robot Task with Geometric Uncertainties

This paper shows the application of a generic constraint-based task specification approach for sensor-based robot systems to a laser tracing example. Key properties of the used approach are (i) its ability to specify complex robot tasks by introducing auxiliary task-oriented feature coordinates, defined with respect to user-defined object and feature frames, (ii) its support for both underconstrained and overconstrained robot tasks, and (iii) its ability to integrate sensor measurements in a unified way, using auxiliary uncertainty coordinates, to estimate geometric uncertainties in the robot system or its environment. Simulation and real world experimental results are presented.