Hand-Eye Calibration of EOD Robot by Solving the AXB = YCZD Problem

Nowadays, the existing EOD robots usually are operated manually; it is very hard to operate. According to the theory of stereo vision, a new explosive ordnance disposal (EOD) robot with a binocular vision system is developed. The vision system captures real-time images, makes camera calibrations with Zhang's method, preprocesses images, matches feature point, then gets the stereo coordinate of the feature point of the suspicious objects, programming with Matlab7.0 and the pattern matching library EasyMatch of machine vision software eVision, it displays the real-time images on the console, and finally, controls EOD robot to grasp the suspicious object automatically and accurately. Experimental results under variable lighting condition, variable shapes and variable distances indicate that the validity in real-time performance and precision of the vision system.

In order to improve the autonomy of the explosive ordnance disposal (EOD) robot, the paper makes a research on the target recognition and path planning algorithm based on laser range finder and Time of Flight (TOF) camera. The target position of the explosive is selected by the operator from the grey-scale image, then EOD robots use Kernelised Correlation Filters (KCF) tracking algorithm and distance constraint to realise the real-time explosives identification. In the case of A* algorithm, it does not take the heading information of the robot into consideration, which may lead to the problem of heading shakes in the vicinity of the obstacle. In this paper, the heading constraint is introduced to improve the A* algorithm so as to eliminate the problem of heading shakes and improve the stability of the robot. The simulation and experiment results demonstrate the feasibility of the algorithm proposed in the paper.

Today, terrestrial robots are used in a multitude of fields and for performing multiple missions. This paper introduces the novel development of a family of crawling terrestrial robots capable of changing very quickly depending on the missions they have to perform. The principle of novelty is the use of a load-bearing platform consisting of two independent propulsion systems. The operational platform, which handles the actual mission, is attached (plug and play) between the two crawler propulsion systems. The source of inspiration is the fact that there are a multitude of intervention robots in emergency situations, each independent of the other. In addition to these costs, there are also problems with the specialization of a very large number of staff. The present study focused on the realization of a simplified, modular model of the kinematics and dynamics of the crawler robot, so that it can be easily integrated, by adding or removing the calculation modules, into the software used. The designed model was integrated on a company controller, which allowed us to compare the results obtained by simulation with those obtained experimentally. We appreciate that the analyzed Explosive Ordnance Disposal (EOD) robot solution represents a premise for the development of a family of EOD robots that use the same carrier platform and to which a multitude of operational platforms should be attached, depending on the missions to be performed.

To solve the problem of inflexibility of offline hand–eye calibration in “eye-in-hand” modes, an online hand–eye calibration method based on the ChArUco board is proposed in this paper. Firstly, a hand–eye calibration model based on the ChArUco board is established, by analyzing the mathematical model of hand–eye calibration, and the image features of the ChArUco board. According to the advantages of the ChArUco board, with both the checkerboard and the ArUco marker, an online hand–eye calibration algorithm based on the ChArUco board is designed. Then, the online hand–eye calibration algorithm, based on the ChArUco board, is used to realize the dynamic adjustment of the hand–eye position relationship. Finally, the hand–eye calibration experiment is carried out to verify the accuracy of the hand–eye calibration based on the ChArUco board. The robustness and accuracy of the proposed method are verified by online hand–eye calibration experiments. The experimental results show that the accuracy of the online hand–eye calibration method proposed in this paper is between 0.4 mm and 0.6 mm, which is almost the same as the offline hand–eye calibration accuracy. The method in this paper utilizes the advantages of the ChArUco board to realize online hand–eye calibration, which improves the flexibility and robustness of hand–eye calibration.

Augmented reality (AR) allows the surgeon to represent holographic patient-specific anatomical information and surgical instruments in the physical world. To correctly superimpose virtual and physical objects, a hand-eye (HE) calibration method for mapping the virtual and physical spaces was proposed. Mathematical relationships between the virtual camera and the physical space were derived. Finally, the accuracy and robustness of the proposed HE calibration method were qualitatively and quantitatively evaluated. The proposed calibration method allows us to determine an optimal invariant spatiotemporal mapping between the virtual camera and the physical space. Qualitatively and quantitatively reliable and accurate estimates for the physical-virtual mapping transformation were verified. Consequently, imaging data and surgical instruments holograms can be precisely represented in the physical space.

Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as “I=AXB.” To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at https://github.com/leihui6/LRBO.

The replacement of humans by machines has gradually become a technological trend. In this study, a dual robotic arm was used in the belt conveyor operation system to track the screw and nut assembly using mutual visual tracking and positioning technology. Moreover, this study simulated the automatic assembly process using a dual robotic arm in a smart factory. An inverse kinematics operation was constructed using a geometric method to control the dual robotic arm to track the screw and nut assembly on the conveyor belt in real time using mutual visual tracking and positioning technology based on a single-lens charge-coupled device of a robotic arm. This study utilized a dual robotic arm to grab the screw and nut using fuzzy visual tracking control. After completing the grabbing of the screw and nut with tracking and positioning errors of 8%, the dual robotic arms continued to complete the assembly of the screw and nut. Therefore, through the establishment technology of mutual visual tracking and positioning of the dual robotic arm in this study, assembly tasks can be efficiently completed in related fields in the future.

This paper details a study performed on a new proposed twelve degree-of-freedom dual robot arm, which is very light but capable of handling heavy loads. The proposed robot arm has a higher value for the ratio of the load capacity/robot weight than conventional robot arms, which are actuated by motors with speed reducers, such as a harmonic drives, since it adopts a new type of robot actuator based on a closed chain mechanism. Because of the high value of the ratio of the payload capacity/robot weight, it can be used as a robot arm for mobile robots and for walking robots. Analyses of the design scheme and of the mechanism of the joint actuator used for the robot arm are presented. Also, the control system developed for the robot arm is introduced. The superior characteristics of the new proposed robot arm, handling heavy payloads with light weight links compared to industrial robots, are presented through carrying out various payload capacity tests. Since the robot arm is designed with light links, it has some deflections and these deflections of the links are analyzed using the Finite Element Method (FEM). The results of performance tests are presented to check the correctness of the FEM analysis and to demonstrate the actual capability of handling heavy payloads applied to the robot arm.

With the promotion of Industry 4.0 reform, the trend of intelligent and precise production in the production workshop is gradually highlighted. This directly leads to higher requirements for robot hand eye coordination accuracy in automated workshops. In order to achieve more precise robot hand eye coordination control, this study designed a new mean calculation method based on the probability density theory, and designed a new mean robot hand eye calibration algorithm based on this. After the test, it is found that the translation error and rotation error calculated by the new mean algorithm are 0.26 and 0.92 respectively, which are significantly lower than other comparison algorithms when using all test samples of normal distribution. And the calculation time of the algorithm when using all the test samples is 2115 ms, which is also significantly lower than the comparison algorithm. The simulation results show that the new mean hand eye calibration method designed in this study can achieve more accurate hand eye coordination control of robots, and has certain application potential in high-precision industrial production scenarios.

This letter describes a novel hand–eye calibration technique based on reprojection error minimization. In contrast to traditional hand–eye calibration methods, the proposed method directly takes images of the calibration pattern and does not require to explicitly estimate the camera pose for each input image. The proposed method is implemented as a pose graph optimization problem, so that it can solve the estimation problem efficiently and robustly, and it can be easily extended for different projection models. It can deal with different camera models (e.g, X-ray cameras with a source-detector projection model) by changing the projection model. Through simulations, we validated that the proposed method shows a good estimation accuracy, and it can be applied to hand–eye calibration with a source-detector camera model. The experimental results with real robots show that the proposed method is applicable to real environments, and it improves the quality of a task that requires accurate hand–eye estimation, such as three-dimensional reconstruction.

Aiming at the problem of low accuracy of industrial robot hand-eye calibration, a robot hand-eye calibration method based on an intermediate measurement system is proposed. The checkerboard calibration board with the marker points pasted is photographed by the camera, and the coordinates of the marker points in the checkerboard calibration board coordinate system are calculated according to the principle of cross-ratio invariance, and the coordinate system matrix between the C-Track optical dynamic tracking system and the industrial robot is established. The transformation relationship finally obtains the closed-loop coordinate system matrix transformation equation with the marker point, the checkerboard calibration board and the C-Track optical dynamic tracking system as the intermediate measurement system, thereby avoiding the introduction of the mobile industrial robot manipulator in the traditional hand-eye calibration process Kinematic error. The experimental results show that the translation error in the hand-eye relationship matrix is 0.6 mm, and the rotation error is 0.3°. Compared with the traditional hand-eye calibration method, this method improves the accuracy of hand-eye calibration, simplifies the hand-eye calibration process, and meets the needs of actual processing and production.

ABSTRACTThe manipulator of Explosive Ordnance Disposal (EOD) Robot was taken as the research object; the research was conducted aiming at planning of fetching motion trajectory of manipulator in linkage control. Combined with route requirements of manipulator's fetching operation, the key route points of manipulator end trajectory were set to create a fetching operation trajectory of point-to-point. Based on manipulator positive and inverse kinematics model, using the fold lines trajectory and 6-spline trajectory movement set up and operate a space movement to joint space kinematic characteristics of mapping relations. Under the circumstance of the same location of key points of motion trajectory and the same motion time, computer simulation was performed on the two trajectory planning methods to compare and analyze the motion characteristics like joint angle, angular velocity and angular acceleration of the manipulator. Results revealed that that 6-spline trajectory motion owns more obvious advantages in aspects like reducing manipulator's vibration and improving motion accuracy in the motion process.

Nowadays medical interventions are often supported by localization systems using different measurement tools (MT). This requires to register the MT coordinate sytem to the world coordinate system used by the medical device. The hand-eye calibration is a well-known method from robotics to estimate the transformation between the gripper of a robot (hand) and a MT (eye) rigidly attached to the robot. Using a calibration tool (e.g. checker board) one can obtain the hand-eye transformation using known relative movements of the robot and the data from the MT. The approach can also be used for MT located elsewhere * using markers on the device. The position of the markers is not required to be known since they are rigid during the motions. Based on prior work using dual quaternions to represent transformations in the SE(3) we not only took into account movements between immediate neighbour positions Pi and Pj , but combined all positions to gain (P/2) submotions in every subset Σ p/n=3 (p/n) t without increasing the number of positions conducted during the calibration. We took into account the unity constraint for dual quaternions since only those represent rigid motions in space. We performed simulations that show the advantage of our algorithm. Additionally we gained experimental data which supported the outcome of the simulations. We can outline that our approach achieves more accurate results estimating the hand-eye transformation than the aforementioned algorithms.

Automatic explosive disposal robot have great potential to replace manual Explosive ordnance disposal(EOD) robots. In order to face the practical application scenarios of complex transformations, it is of great practical significance to study a fully automatic and autonomous EOD robot that can transfer dangerous goods to a safe area. In this paper, a mechanical arm algorithm based on the inverse kinematics of robot is presented. The relative position of the bomb is input by the electromagnetic induction device to guide the mechanical arm to the corresponding position. After obtaining the bomb, the deviation angle of the mechanical arm is input in real time by the gyroscope to achieve the relative stability of the object transportation. The experimental results indicate that the explosive disposal robot has certain practical significance.

The traditional hand-eye calibration needs to take the calibration target image acquired by the vision system and the corresponding robot pose information as input, the process is complicated, and there is a risk of calibration failure when the data are not corresponding. To solve these problems, a fast hand-eye calibration method is proposed. According to the principle that a certain point in space has the same three-dimensional (3D) coordinates in its coordinate system after the motion of the coordinate system, to control the end of the robot to carry out multiple translation and rotational motion, the traditional hand-eye calibration model AX=XB is simplified into AX=B. The hand-eye calibration matrix can be quickly solved only by the transformation between the camera coordinate system and the calibration target coordinate system. The experimental results show that the proposed method is simple, fast and high accuracy. It can be applied to eye-in-hand system and eye-to-hand system.