André Crosnier

Color Constrained ICP for Registration of Large Unstructured 3D Color Data Sets

In this paper, we address the problem of pair-wise registration of large unstructured 3D/color datasets. Our purpose is to improve the classical ICP (Iterative closest point) algorithm by using color information, in order to deal with large datasets and with objects for which the geometric information is not significant enough. After a brief presentation of classical ICP (iterative closest point) algorithm and of the research works developed to improve its performance, we propose a new strategy to improve the selection of points. Color information is used to reduce the search space during the matching step. Experimental results obtained with real range images show that the algorithm provides an accurate estimation of the rigid transformation

Proactive behavior of a humanoid robot in a haptic transportation task with a human partner

In this paper, we propose a control scheme that allows a humanoid robot to perform a complex transportation scenario jointly with a human partner. At first, the robot guesses the human partners intentions to proactively participate to the task. In a second phase, the human-robot dyad switches roles: the robot takes over the leadership of the task to complete the scenario. During this last phase, the robot is remotely controlled with a joystick. The scenario is realized on a real HRP-2 humanoid robot to assess the overall approach.

Human-humanoid haptic joint object transportation case study

In this paper, we propose a control scheme that allows a humanoid robot to perform a transportation task jointly with a human partner. From the study of how human dyads achieve such a task, we have developed a control law for physical interaction that unifies standalone and collaborative (leader and follower) modes for trajectory-based tasks. We present it in the case of a linear impedance controller but it can be generalized to more complex impedances. Desired trajectories are decomposed into sequences of elementary motion primitives. We implemented this model with a Finite State Machine associated with a reactive pattern generator. First experiments conducted on a real HRP-2 humanoid robot assess the overall approach.

Multimodal control for human-robot cooperation

For intuitive human-robot collaboration, the robot must quickly adapt to the human behavior. To this end, we propose a multimodal sensor-based control framework, enabling a robot to recognize human intention, and consequently adapt its control strategy. Our approach is marker-less, relies on a Kinect and on an on-board camera, and is based on a unified task formalism. Moreover, we validate it in a mock-up industrial scenario, where human and robot must collaborate to insert screws in a flank.

Swept volumes generated from deformable objects application to NC verification

The paper suggests a representation of swept volumes generated by the motion of deformable objects. This representation is based on the topological properties of n-dimensional manifolds. The algorithm developed for the evaluation of the boundary exploits these properties in such a way that the n-dimensional object (swept volume) is generated only from the (n-1)-dimensional components resulting from a family of deformations. The deformation, analytically expressed, integrates physical properties, especially the nonlinearities of deformable objects. The representation is applied to the modeling of the deformations of a milling cutter being fed into a workpiece and it constitutes an efficient tool for NC verification. Many other applications may be concerned by this work, especially in robotics (modeling of flexible robots, collision detection) or in mechanical engineering.

A unified multimodal control framework for human-robot interaction

In human-robot interaction, the robot controller must reactively adapt to sudden changes in the environment (due to unpredictable human behaviour). This often requires operating different modes, and managing sudden signal changes from heterogeneous sensor data. In this paper, we present a multimodal sensor-based controller, enabling a robot to adapt to changes in the sensor signals (here, changes in the human collaborator behaviour). Our controller is based on a unified task formalism, and in contrast with classical hybrid visicn-force-position control, it enables smooth transitions and weighted combinations of the sensor tasks. The approach is validated in a mock-up industrial scenario, where pose, vision (from both traditional camera and Kinect), and force tasks must be realized either exclusively or simultaneously, for human-robot collaboration. A unified multimodal sensor-based control framework is proposed.Pose, vision and force tasks can be realized either exclusively or simultaneously.Self-adapting gains and homotopies between the tasks guarantee safe operation.The approach is validated in an industrial task: collaborative screwing.

Optimal Exciting Dance for Identifying Inertial Parameters of an Anthropomorphic Structure

Knowledge of the mass and inertial parameters of a humanoid robot or a human being is crucial for the development of model-based control, as well as for monitoring the rehabilitation process. These parameters are also important for obtaining realistic simulations in the field of motion planning and human motor control. For robots, they are often provided by computer-aided design data, while averaged anthropometric table values are often used for human subjects. The unit/subject-specific inertial parameters can be identified by using the external wrench caused by the ground reaction. However, the identification accuracy intrinsically depends on the excitation properties of the recorded motion. In this paper, a new method for obtaining optimal excitation motions is proposed. This method is based on the identification model of legged systems and on optimization processes to generate excitation motions while handling mechanical constraints. A pragmatic decomposition of this problem, the use of a new excitation criterion, and a quadratic program to identify inertial parameters are proposed. The method has been experimentally validated onto an HOAP-3 humanoid robot and with one human subject.

Parsimonious Kinematic Control of Highly Redundant Robots

When a robot is highly redundant in comparison to the task to be executed, current control techniques are not “economic” in the sense that they demand, most of the time unnecessarily, all the joints to move. Such behavior can be undesirable for some applications. In this direction, this work proposes a new control paradigm based on linear programming that intrinsically provides a parsimonious control strategy, i.e, one in which few joints move. In addition to a formal stability proof, this letter presents simulation and experimental results on the HOAP-3 humanoid robot. Finally, a comparison is made with a least-square method based on the pseudoinverse of the task Jacobian, showing that the proposed method indeed uses fewer joints than the classic one.

Real-time environment for mission programming of telerobotics systems

This paper is focussed on the presentation of the functionalities and the architecture of a real time environment (RTE) dedicated to the mission programming of telerobotics systems. This environment constitutes a framework providing the human operator with tools for the preparation, the simulation and the execution of missions in non- cooperative environment. To overcome the delay time and to realize a mission with a minimum cost and the minimum safety, the preparation phase and the simulation phase of a mission are essential to obtain a well-tried scenario dedicated to the execution phase. An application that consists of teleoperating, in known and structured outdoor environment, a mobile manipulator is also presented.

Hierarchical segmentation for unstructured and unfiltered range images

We present a method for the segmentation of unstructured and unfiltered 3D data. The core of this approach is based on the construction of a local neighborhood structure and its recursive subdivision. 3D points will be organized into groups according to their spatial proximity, but also to their similarity in the attribute space. Our method is robust to noise, missing data, and local anomalies thanks to the organization of the points into a minimal spanning tree in attribute space. We assume that the 3D image is composed of regions homogeneous according to some criterion (color, curvature, etc.), but no assumption about noise, nor spatial repartition/shape of the regions or points is made. Thus, this approach can be applied to a wide variety of segmentation problems, unlike most existing specialized methods. We demonstrate the performance of our algorithm with experimental results on real range images.