Hand-eye calibration made easy through a closed-form two-stage method

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksAn analysis of the existing hand-eye calibration methods reveals that most of them are far from trivial. And, what is worse, their intrinsic complexity makes it difficult to elucidate under which circumstances they fail to provide an accurate solution. Thus, although it might seem that hand-eye calibration problem is uninspiring because it is assumed to be well-solved, we show in this paper that there was still room for improvement, both in terms of simplicity and robustness. After reviewing the most representative methods, we analyze the situations in which they fail, and we introduce a simpler closed-form alternative that accurately solves the problem in all the identified critical circumstances. Its performance is evaluated using simulated and real experimental data.Peer ReviewedPostprint (author's final draft

Robot sensor calibration via neural network and particle swarm optimization enhanced with crossover and mutation

U cilju određivanja položaja i orijentacije nekog predmeta u zglobu za robot, treba procijeniti odnos transformacije sustava ruka-oko, što se opisuje kao rotacijska matrica i vektor translacije. Predlaže se novi pristup koji integrira neuronsku mrežu i algoritam optimaizacije roja čestica s operacijom križanja i mutacije za kalibraciju osjećaja robota. Najprije se strukturira neuronska mreža s matricom rotacijske težine gdje su težine elementi rotacijskog dijela homogenog prijenosa sustava ruka-oko. Tada se algoritam optimalizacije roja čestica integrira u program rješavanja, gdje se faktori težine inercije i vjerojatnosti mutacije sami podešavaju prema putanji gibanja čestica u longitudinalnom pravcu i lateralnom pravcu. Kad je zadovoljen kriterij terminacije, rotaciona matrica se dobiva iz nepromjenljivih težina neuronske mreže. Tada se rješava vektor translacije i postiže se položaj i orijentacija slike s kamere u odnosu na sliku sa zgloba. Predloženi pristup pruža novu šemu za kalibraciju robota tehnikom samo-adaptacije, što garantira ortogonalnost riješenih rotacijskih komponenti homogenog transforma.In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of hand-eye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle swarm optimization algorithm with crossover and mutation operation for robot sense calibration is proposed. First the neural network with rotational weight matrix is structured, where the weights are the elements of rotational part of homogeneous transform of the hand-eye system. Then the particle swarm optimization algorithm is integrated into the solving program, where the inertia weight factor and mutation probability are tuned self-adaptively according to the motion trajectory of particles in longitudinal direction and lateral direction. When the termination criterion is satisfied, the rotational matrix is obtained from the neural network’s stable weights. Then the translational vector is solved, so the position and orientation of camera frame with respect to wrist frame is achieved. The proposed approach provides a new scheme for robot sense calibration with self-adaptive technique, which guarantees the orthogonality of solved rotational components of the homogeneous transform

Robot sensor calibration via neural network and particle swarm optimization enhanced with crossover and mutation

U cilju određivanja položaja i orijentacije nekog predmeta u zglobu za robot, treba procijeniti odnos transformacije sustava ruka-oko, što se opisuje kao rotacijska matrica i vektor translacije. Predlaže se novi pristup koji integrira neuronsku mrežu i algoritam optimaizacije roja čestica s operacijom križanja i mutacije za kalibraciju osjećaja robota. Najprije se strukturira neuronska mreža s matricom rotacijske težine gdje su težine elementi rotacijskog dijela homogenog prijenosa sustava ruka-oko. Tada se algoritam optimalizacije roja čestica integrira u program rješavanja, gdje se faktori težine inercije i vjerojatnosti mutacije sami podešavaju prema putanji gibanja čestica u longitudinalnom pravcu i lateralnom pravcu. Kad je zadovoljen kriterij terminacije, rotaciona matrica se dobiva iz nepromjenljivih težina neuronske mreže. Tada se rješava vektor translacije i postiže se položaj i orijentacija slike s kamere u odnosu na sliku sa zgloba. Predloženi pristup pruža novu šemu za kalibraciju robota tehnikom samo-adaptacije, što garantira ortogonalnost riješenih rotacijskih komponenti homogenog transforma.In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of hand-eye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle swarm optimization algorithm with crossover and mutation operation for robot sense calibration is proposed. First the neural network with rotational weight matrix is structured, where the weights are the elements of rotational part of homogeneous transform of the hand-eye system. Then the particle swarm optimization algorithm is integrated into the solving program, where the inertia weight factor and mutation probability are tuned self-adaptively according to the motion trajectory of particles in longitudinal direction and lateral direction. When the termination criterion is satisfied, the rotational matrix is obtained from the neural network’s stable weights. Then the translational vector is solved, so the position and orientation of camera frame with respect to wrist frame is achieved. The proposed approach provides a new scheme for robot sense calibration with self-adaptive technique, which guarantees the orthogonality of solved rotational components of the homogeneous transform

Kinematics parameters estimation for an AFM/Robot integrated micro-force measurement system.

International audienceThis paper introduces a novel atomic force microscope (AFM) and parallel robot integrated micro-force measurement system whose objective is the measurement of adhesion force between planar micro-objects. This paper is mainly focused on the kinematics parameters estimation between the objects to be measured, the parallel robot and the AFM system in order to position both objects during measurement. A substrate is placed on the end-platform of the parallel robot system, on which three markers are utilized as the reference information to the kinematics parameters estimation. The markers are identified by the AFM scanning in order to identify the kinematics parameters of the whole system. Based on the classic Gauss-Newton algorithm, the position and orientation can be solved. Finally, the effectiveness of the proposed method is demonstrated through the experiments on the prototype of the micro-force measurement system. The parameters estimation methodology outlined is generic and also can be extended to a variety of applications in calibration of micro-robots

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Hand-eye calibration enables proper perception of the environment in which a vision guided robot operates. Additionally, it enables the mapping of the scene in the robots frame. Proper hand-eye calibration is crucial when sub-millimetre perceptual accuracy is needed. For example, in robot assisted surgery, a poorly calibrated robot would cause damage to surrounding vital tissues and organs, endangering the life of a patient. A lot of research has gone into ways of accurately calibrating the hand-eye system of a robot with different levels of success, challenges, resource requirements and complexities. As such, academics and industrial practitioners are faced with the challenge of choosing which algorithm meets the implementation requirements based on the identified constraints. This review aims to give a general overview of the strengths and weaknesses of different hand-eye calibration algorithms available to academics and industrial practitioners to make an informed design decision, as well as incite possible areas of research based on the identified challenges. We also discuss different calibration targets which is an important part of the calibration process that is often overlooked in the design process

A regularization-patching dual quaternion optimization method for solving the hand-eye calibration problem

The hand-eye calibration problem is an important application problem in robot research. Based on the 2-norm of dual quaternion vectors, we propose a new dual quaternion optimization method for the hand-eye calibration problem. The dual quaternion optimization problem is decomposed to two quaternion optimization subproblems. The first quaternion optimization subproblem governs the rotation of the robot hand. It can be solved efficiently by the eigenvalue decomposition or singular value decomposition. If the optimal value of the first quaternion optimization subproblem is zero, then the system is rotationwise noiseless, i.e., there exists a ``perfect'' robot hand motion which meets all the testing poses rotationwise exactly. In this case, we apply the regularization technique for solving the second subproblem to minimize the distance of the translation. Otherwise we apply the patching technique to solve the second quaternion optimization subproblem. Then solving the second quaternion optimization subproblem turns out to be solving a quadratically constrained quadratic program. In this way, we give a complete description for the solution set of hand-eye calibration problems. This is new in the hand-eye calibration literature. The numerical results are also presented to show the efficiency of the proposed method

Vision-assisted robotic finishing of friction stir-welded corner joints

One required process in the fabrication of large components is welding, after which there may be a need for machining to achieve final dimensions and uniform surfaces. Friction stir-welding (FSW) is a typical example after which a series of deburring and grinding operations are carried out. Currently, the majority of these operations are carried out either manually, by human workers, or on machine tools which results in bottlenecks in the process flows. This paper presents a robotic finishing system to automate the finishing of friction stir-welded parts with minimum human involvement. In a sequence, the system can scan and reconstruct the 3D model of the part, localise it in the robot frame and generate a suitable machining path accordingly, to remove the excess material from FSW without violating process constraints. Results of the cutting trials carried out for demonstration have shown that the developed system can consistently machine the corner joints of an industrial scale part to desired surface quality which is around 1.25 μm in, Ra, the arithmetic average of the surface roughness