Daniel Kubus

On-line rigid object recognition and pose estimation based on inertial parameters

This paper proposes an object recognition and gripping pose estimation approach based on on-line estimation of the complete set of inertial parameters, i.e. the mass, the coordinates of the center of mass, and the elements of the inertia matrix, of an object gripped by or attached to a manipulator. A multi-sensor fusion approach combining 6D force/torque, 6D acceleration, 3D angular velocity, and joint angle data to estimate these parameters is presented. In order to facilitate practical implementation, approaches to handling force/torque sensor offsets and to compensating the forces/torques caused by the distal mounting plate of the force/torque sensor and the gripper are incorporated. Regarding the joint angle signals, preprocessing steps to derive the angular velocity, linear acceleration and angular acceleration vector w.r.t. the sensor frame are addressed. The estimation of the complete set of inertial parameters employing the recursive instrumental variables (RIV) method is discussed. The extraction of features that are invariant w.r.t. translation and rotation, i.e. the mass and the principal moments of inertia, as well as a recognition approach based on the Kullback-Leibler divergence are presented. Experimental results show very low errors in the estimates of the inertial parameters, good pose estimation accuracy, and the viability of the recognition approach.

On-line estimation of inertial parameters using a recursive total least-squares approach

The estimation of the ten inertial parameters of rigid loads, which are attached to manipulators, may benefit several robotics applications, e.g.: force control, object recognition, and pose estimation. These applications require sufficiently accurate, robust, and fast estimation of the inertial parameters. Existing approaches, however, do not allow for robust on-line estimation, since they use standard batch least-squares techniques, which ignore noise in the data matrix. The proposed approach, however, estimates the inertial parameters on-line and very fast (approx. 1.5s), while explicitly considering noise in the data matrix by a total least-squares approach. Apart from estimation equations and estimation approaches, the design of estimation trajectories is addressed in this paper. The performance of the proposed estimation approach is compared with the recursive ordinary least-squares (RLS) and the recursive instrumental variables (RIV) method. Experimental results clearly recommend the proposed recursive total least-squares approach (RTLS).

Improving force control performance by computational elimination of non-contact forces/torques

Regarding manipulators with wrist-mounted force/torque sensors a major issue is the high execution time of force-guided and force-guarded motions compared to purely position-controlled tasks. An important factor that aggravates the reduction of the execution time is the influence of non-contact forces, e.g. inertial forces, centrifugal forces, Coriolis forces, and associated torques, which are exerted onto the sensor by a load attached to it. Considering force-guided or force-guarded motions, these non-contact forces may significantly deteriorate contact detection and force control performance when executing dynamic movements. In addition to these disturbance forces/torques, resets of the force/torque sensor consume execution time. This paper presents an approach to eliminating all non-contact forces and associated torques from force/torque sensor measurements thus enabling pure contact force control. Apart from facilitating pure contact force control, the presented approach renders resets of the force/torque sensor unnecessary. To achieve this aim, the ten inertial parameters (mass, coordinates of the center of mass, and the elements of the inertia matrix) of the load attached to the sensor as well as the force/torque sensor offsets are estimated on-line employing a variant of the recursive instrumental variables method. These parameters are used to calculate the non- contact forces/torques acting upon the sensor. The current non-contact forces/torques and sensor offsets are subtracted from the force/torque measurements thus yielding the contact forces/torques. Experimental results show that both contact detection and force control performance are improved significantly by this approach.

Force and acceleration sensor fusion for compliant manipulation control in 6 degrees of freedom

This paper focusses on sensor fusion in robotic manipulation: six-dimensional (6-D) force/torque signals and 6-D acceleration signals are used to extract forces and torques caused by inertia. As result, only forces and torques established by environmental contact(s) remain. Apart from an improvement of hybrid force/pose control behavior, an additional major benefit is that regular resetting/zeroing of force/torque sensors before free space/contact transitions can be omitted. All essential equations, transformations and calculations that are required for this 6-D fusion approach are derived. To highlight the meaning for practical implementations, numerous experiments with a six-joint Staeubli RX60 industrial manipulator are presented and the achieved results are discussed.

6D Force and Acceleration Sensor Fusion for Compliant Manipulation Control

This paper focusses on sensor fusion in robotic manipulation: 6D force/torque signals and 6D acceleration signals are used to extract forces and torques caused by inertia. As result, only forces and torques established by environmental contact(s) remain. Beside an improvement of hybrid force/pose control behavior, an additional major benefit is that regular resetting/zeroing of force/torque sensors before free space/contact transitions can be omitted. All essential equations, transformations, and calculations that are required for this 6D fusion approach are derived. To highlight the meaning for practical implementations, numerous experiments with a six-joint Staeubli RX60 industrial manipulator are presented, and the achieved results are discussed

12D force and acceleration sensing: A helpful experience report on sensor characteristics

The potential of six-axis acceleration sensors in the field of robotic manipulation applications is quite high and most of it has not been used yet - neither in theoretic literature nor in research experiments. When considering six-joint industrial manipulators with six-axis force/torque and six-axis acceleration sensing, many new possibilities arise: all ten inertial parameters of any object can be identified and objects can be recognized based on these parameters, position control behavior can be improved; non-contact forces can be extracted and force control performance can be improved; visual-servoing methods can use acceleration signals to become more robust. The authors made numerous experiments in the mentioned fields and recognized major weaknesses during the realization of prototypic research setups with six-axis acceleration sensors. These problems regard sensor drift, undesired sensor-internal dependencies as the influence of any distal sensor part, noise, and undesired crosstalk behavior. In order to benefit from acceleration signals, it is important to clearly overcome these problems. This paper analyzes typical systematic errors, characterizes them, and suggests important solution methods for a successful usage of acceleration information.

1kHz is not enough — How to achieve higher update rates with a bilateral teleoperation system based on commercial hardware

Teleoperation has a long history in the robotics community and numerous bilateral teleoperation systems employing manipulators have been proposed in the literature. On the one hand, systems have been designed which employ commercial hardware and hence generally suffer from low update rates and high delays due to restrictions of commercial manipulator controllers and haptic device controllers. On the other hand, bilateral teleoperation systems designed by research institutions often provide only few degrees of freedom. Our 6DoF bilateral teleoperation system, however, combines the amenities of commercial hardware with a high performance distributed control architecture which enables us to achieve update rates of more than 2kHz and delays in the range of only 100µs. This paper focuses on the architecture of our system and demonstrates how to achieve this performance using commercial hardware. Moreover, we show why update rates of more than 1kHz are essential for certain teleoperation tasks. Especially with high approach velocities and stiff environments, high update rates and low delays are key requirements for stability and thus for realistic haptic perception. We present experimental results demonstrating the influence of the update rate on system stability. These results not only highlight the benefits of high update rates but also give hints on how to estimate the update rate necessary to achieve stable teleoperation for a given environment stiffness.

An efficient parallel approach to Random Sample Matching (pRANSAM)

This paper introduces a parallelized variant of the Random Sample Matching (RANSAM) approach, which is a very time and memory efficient enhancement of the common Random Sample Consensus (RANSAC). RANSAM exploits the theory of the birthday attack whose mathematical background is known from cryptography. The RANSAM technique can be applied to various fields of application such as mobile robotics, computer vision, and medical robotics. Since standard computers feature multi-core processors nowadays, a considerable speedup can be obtained by distributing selected subtasks of RANSAM among the available cores. First of all this paper addresses the parallelization of the RANSAM approach. Several important characteristics are derived from a probabilistic point of view. Moreover, we apply a fuzzy criterion to compute the matching quality, which is an important step towards real-time capability. The algorithm has been implemented for Windows and for the QNX RTOS. In an experimental section the performance of both implementations is compared and our theoretical results are validated.

Kinesthetic Teaching in Assembly Operations A User Study

Kinesthetic teaching is a commonly employed method for programming robots using the Programming by Demonstration (PbD) paradigm. It is widely regarded as an intuitive approach to robot programming, which can be performed by shop-floor workers. Much research in this area has focused on pick-and-place tasks while demanding assembly tasks have received less attention so far. Nonetheless, in various contributions kinesthetic teaching is utilized to gain insight into human assembly strategies by deriving trajectories, mating forces, etc. To evaluate the discrepancies between kinesthetic teaching and manual assembly in the context of industrial assembly tasks, we conducted a user study with 78 participants featuring four different tasks. Our results confirm the ease of learning attributed to kinesthetic teaching but also suggest that trying to transfer human assembly strategies using this method may suffer from a substantial flaw.

Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery

In this video we show our current prototype for robot assisted endoscopy. The system requires only few and simple instructions from the surgeon, in order to guide the endoscope in an intelligent, autonomous, and safe way: The surgeon tells what to do and the robot decides how to carry out the task by choosing the best manipulation primitive in every control cycle. Several sensors are integrated into the decision process: The endoscope camera, a stereo camera system, a force/torque sensor, a biomechanical model based on CT data and statistical knowledge, a position and velocity sensor for the manipulator, and interface devices like a foot switch. The executed manipulation primitive is handled by a hybrid controller allowing to switch the control mode (e.g. trajectory following or force control) for each degree of freedom of the task frame individually.