Robot Pose Research Articles

Motion Planning of Mobile Manipulator for Navigation Including Door Traversal

Door traversal is an essential task for a mobile manipulator to achieve autonomous navigation in indoor environment. However, it remains challenging since it requires the coupled motion of the robot and door and is composed of several sub-problems such as approaching, opening, passing through, and closing the door. This paper proposes a planning framework that formulates these sub-problems as a single problem and computes a path for the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area indicator</i> is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the Inverse Kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator.

Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic remapping, updating the map is recommended. However, in the case of incorrect robot pose estimation, updating the map can lead to errors that prevent the robot's localisation and jeopardise map accuracy. In this paper, we propose a safe Lidar-based occupancy grid map-updating algorithm for dynamic environments, taking into account uncertainties in the estimation of the robot's pose. The proposed approach allows for robust long-term operations, as it can recover the robot's pose, even when it gets lost, to continue the map update process, providing a coherent map. Moreover, the approach is also robust to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

CULTURAL HERITAGE DIGITAL PRESERVATION THROUGH AI-DRIVEN ROBOTICS

Abstract. This paper introduces a novel methodology developed for creating 3D models of archaeological artifacts that reduces the time and effort required by operators. The approach uses a simple vision system mounted on a robotic arm that follows a predetermined path around the object to be reconstructed. The robotic system captures different viewing angles of the object and assigns 3D coordinates corresponding to the robot's pose, allowing it to adjust the trajectory to accommodate objects of various shapes and sizes. The angular displacement between consecutive acquisitions can also be fine-tuned based on the desired final resolution. This flexible approach is suitable for different object sizes, textures, and levels of detail, making it ideal for both large volumes with low detail and small volumes with high detail. The recorded images and assigned coordinates are fed into a constrained implementation of the structure-from-motion (SfM) algorithm, which uses the scale-invariant features transform (SIFT) method to detect key points in each image. By utilising a priori knowledge of the coordinates and SIFT algorithm, low processing time can be ensured while maintaining high accuracy in the final reconstruction.The use of a robotic system to acquire images at a pre-defined pace ensures high repeatability and consistency across different 3D reconstructions, eliminating operator errors in the workflow. This approach not only allows for comparisons between similar objects but also provides the ability to track structural changes of the same object over time.Overall, the proposed methodology provides a significant improvement over current photogrammetry techniques by reducing the time and effort required to create 3D models while maintaining a high level of accuracy and repeatability.

A matrix-solving hand-eye calibration method considering robot kinematic errors

Hand-eye calibration is one of the practical solutions for enhancing the robotic machining accuracy of complex freeform surfaces. The hand-eye parameters identification, however, is inevitably affected by the robot kinematic errors. In this paper, a matrix-solving hand-eye calibration method is proposed to overcome the challenge by taking into consideration the position constraint based robot kinematics identification. In the calibration framework, the calibration of hand-eye parameters is performed for solving the rotation matrix and translation vector, and the corresponding calibration strategies are proposed based on the axial translation under robot end-effector coordinate frame and the robot relocalization, respectively. Then the hand-eye parameters are corrected in the form of matrix compensation by using the differential matrix of the robot pose errors. A uniform sampling method for robot pose screening is further designed to automatically acquire spherical point clouds by the laser scanner when the robot poses change. Based on these steps, the combined identification model of robot kinematic parameter errors and hand-eye parameters is developed according to the position constraint that the position of the criterion sphere in the robot base coordinate frame is invariant. By virtue of the least square method, the kinematic parameter errors of the robot are calculated to compensate the hand-eye calibration accuracy. The calibration experiments are investigated by robotic scanning of the criterion sphere and two typical complex workpiece of car bodywork and blade, and the results are particularly compared with the uncompensated method and the state-of-the-art methods, showing the effectiveness and applicability of the proposed method.

Learning-Based Visual-Strain Fusion for Eye-in-Hand Continuum Robot Pose Estimation and Control

Image processing has significantly extended the practical value of the eye-in-hand camera, enabling and promoting its applications for quantitative measurement. However, fully vision-based pose estimation methods sometimes encounter difficulties in handling cases with deficient features. In this article, we fuse visual information with the sparse strain data collected from a single-core fiber inscribed with fiber Bragg gratings (FBGs) to facilitate continuum robot pose estimation. An improved extreme learning machine algorithm with selective training data updates is implemented to establish and refine the FBG-empowered (F-emp) pose estimator <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> . The integration of F-emp pose estimation can improve sensing robustness by reducing the number of times that visual tracking is lost given moving visual obstacles and varying lighting. In particular, this integration solves pose estimation failures under full occlusion of the tracked features or complete darkness. Utilizing the fused pose feedback, a hybrid controller incorporating kinematics and data-driven algorithms is proposed to accomplish fast convergence with high accuracy. The online-learning error compensator can improve the target tracking performance with a 52.3%–90.1% error reduction compared with constant-curvature model-based control, without requiring fine model-parameter tuning and prior data acquisition.

Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain

We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrain. The system includes: a multilayer local map for assessing traversability; an anytime omnidirectional control Lyapunov function for use with a rapidly exploring random tree star (RRT*) that generates a vector field for specifying motion between nodes; a subgoal finder when the final goal is outside of the current map; and a finite-state machine to handle high-level mission decisions. The system also includes a reactive thread that copes with robot deviations via a vector field, defined by a closed-loop feedback policy. The vector field provides real-time control commands to the robot's gait controller as a function of instantaneous robot pose. The system is evaluated on various challenging outdoor terrains and cluttered indoor scenes in both simulation and experiment on Cassie Blue, a bipedal robot with 20 degrees of freedom. All implementations are coded in C++ with the robot operating system and are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UMich-BipedLab/CLF_reactive_planning_system</uri> .

A novel hand-eye calibration method for industrial robot and line laser vision sensor

PurposeThe purpose of this study is solving the hand–eye calibration issue for line structured light vision sensor. Only after hand–eye calibration the sensor measurement data can be applied to robot system.Design/methodology/approachIn this paper, the hand–eye calibration methods are studied, respectively, for eye-in-hand and eye-to-hand. Firstly, the coordinates of the target point in robot system are obtained by tool centre point (TCP), then the robot is controlled to make the sensor measure the target point in multiple poses and the measurement data and pose data are obtained; finally, the sum of squared calibration errors is minimized by the least square method. Furthermore, the missing vector in the process of solving the transformation matrix is obtained by vector operation, and the complete matrix is obtained.FindingsOn this basis, the sensor measurement data can be easily and accurately converted to the robot coordinate system by matrix operation.Originality/valueThis method has no special requirement for robot pose control, and its calibration process is fast and efficient, with high precision and has practical popularized value.

Optimization Design of Mountain and Water Landscape of Traditional Mountain Village Based on New Robot Visual Technology

The reasonable application of the random sampling consistent algorithm can be used to eliminate the theoretically possible error points and then complete the initial aiming pairing. The interior points obtained after this algorithm are used to estimate the relative adjacent poses of the new robot in the operation of the least squares method, so as to make certain improvements to the ICP (iterative closest point) algorithm, making it useful for accurate aiming pairing. Finally, by using the optimization method of G20, the posture of the new mobile robot relative to the adjacent position is optimized to a certain extent. In order to reasonably plan and design the landscape of mountain villages, this paper proposes the corresponding solution by analyzing the multidimensional value and landscape design data of traditional mountain villages, which not only needs to adapt to modern economic development but also needs to live in harmony with the natural ecological environment. Taking a village as a case for analysis, the village houses, roads, services, greening, ecology, and other aspects were reconstructed, and then, the detailed landscape design of the multimedia multidimensional resource village was analyzed and implemented.

Path planning and pose correction of robot laser cleaning process for specific surfaces of parts

The purpose of this paper is to solve a series of problems, such as the difficulty in accurately controlling the specific surfaces, and the interference between the laser beam and the part, when the laser is used to clean mechanical parts with complex surfaces by using robots. Firstly, a region segmentation algorithm of the stereo lithography (STL) model was designed, and the feature surfaces of the model were obtained by using the dihedral angle as the criterion. Then the path points which could easily cause interference between the laser beam and the part to be cleaned were detected by the method of ray intersection, and the pose of the robot was modified according to the normal vectors of two non-interference points that were adjacent to the continuous interference points respectively. Finally, the proposed method was validated by scanning path visualization and robot laser cleaning process experiments. The generated path in the unit of feature surface according to the variation components of normal vectors proved the effectiveness of the STL model region segmentation algorithm based on dihedral angle division criteria; the change of the normal vectors of the path points on the current feature surface was continuous and smooth, which meant that the path points did not cause sudden change of the robot pose after the pose correction. The robot cleaned the specific surfaces of the test workpiece safely and effectively in the actual laser cleaning experiments, and the effective cleaning area reached 96.87%, 99.56%, 94.53% and 91.62% of the target cleaning area, which further proved the effectiveness of this study. The method is also useful for the path planning of robots in other processing fields.

VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR

For the existing visual–inertial SLAM algorithm, when the robot is moving at a constant speed or purely rotating and encounters scenes with insufficient visual features, problems of low accuracy and poor robustness arise. Aiming to solve the problems of low accuracy and robustness of the visual inertial SLAM algorithm, a tightly coupled vision-IMU-2D lidar odometry (VILO) algorithm is proposed. Firstly, low-cost 2D lidar observations and visual–inertial observations are fused in a tightly coupled manner. Secondly, the low-cost 2D lidar odometry model is used to derive the Jacobian matrix of the lidar residual with respect to the state variable to be estimated, and the residual constraint equation of the vision-IMU-2D lidar is constructed. Thirdly, the nonlinear solution method is used to obtain the optimal robot pose, which solves the problem of how to fuse 2D lidar observations with visual–inertial information in a tightly coupled manner. The results show that the algorithm still has reliable pose-estimation accuracy and robustness in many special environments, and the position error and yaw angle error are greatly reduced. Our research improves the accuracy and robustness of the multi-sensor fusion SLAM algorithm.

Localization on a-priori information of plane extraction.

Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall's surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

Kinematic modeling and motion characterization of an articulated mobile robot

In response to the outstanding problems of poor motion flexibility and poor terrain adaptability of conventional mobile robots in unstructured environments, a multi-vehicle modular articulated mobile robot capable of adapting to complex and variable obstacle terrain was developed by designing and introducing a flexible articulation device. By constructing a kinematic model of the single body module of the robot, integrating the motion constraint relations among the modules, and establishing the overall robot attitude equations, we propose a robot soft motion control method. Based on the analysis of robot motion characteristics under special obstacle terrain, the relationship model between obstacle parameters and robot pose is constructed, and the correction strategy under unilateral unstable motion state is proposed through the analysis of robot motion state under unilateral unstable condition. Theoretical analysis and experimental results show that the robot has good motion flexibility and complex terrain adaptability.

Design And Implementation Of Pose Recording System With Denavit Hartenberg Method In A 6-Dof Robot Rotaric

Robot developments have been integrated into several industrial and home appliances. To keep up with the development, UNIKA Atma Jaya created a group called PATRIOT as a head start, and started the development on ROTARIC robot. One of the early robot research discussed in this paper is to design and implement ROTARIC 6-DOF robot pose recording system which is drawn using forward kinematics with Denavit Hartenberg method. The system has a GUI with several function inside such as robot pose recording, 2-D display of the robot recorded pose using Forward Kinematics analysis, and playback recorded pose. The system uses several hardware that have been connected serially such as 6 Dynamixel MX-28 servo, OpenCM 9.04 +485 EXP microcontroller, and a Computer. The Software used in the system is C# .NET with WinForms App as the GUI and Arduino IDE. From the test done in this research, the recording and 2-D drawing system achieved error &lt;±1cm or 6.67% relative error caused by the resolution value while reading the servo position sensor and the value rounding inside the program. Meanwhile, the playback recording function that uses queue system achieved error of &lt;±1cm and error of ±5cm or 33.3% relative error when the recorded pose is played using real time delay used in recording. The error is caused by the robot failure to keep up with the speed of the recorded pose and the limited torque in servo number 2 in the robot.

Active Pose Relocalization for Intelligent Substation Inspection Robot

Intelligent inspection robots widely applied in substations are required to capture the inspection image consistent with the calibration image during routine inspection of electrical equipment. However, it is a challenging work for the inspection robot to capture the inspection image meeting the requirement due to navigation error and mechanical wear. To address this problem, an active pose relocalization (APR) method is proposed in this article. Specifically, an error model describing the relationship between the pixel error in the image plane and the robot pose error is established. Then, a decoupling three-stage PI control strategy based on the error model is provided to relocate the robot to the calibration pose, wherein, a translation error estimation algorithm based on homography transformation is proposed to compute the absolute translation scale between calibration and inspection poses, which avoid the degradation problem of the classic 2D-2D pose estimation algorithm. Finally, the performance of the proposed APR method is demonstrated through comparative relocalization experiments of 10 calibration points in virtual and real-world environments respectively.

Motion and Trajectory Planning and Simulation of a High Frequency Transformer Winding Robot Pose

The application of high-frequency transformers is related to the development of all walks of life in the national economy and people’s livelihood, among which the fast and agile performance of winding knots in the system operation platform is crucial to the production efficiency and quality assurance of transformers. Aiming at the problems of slow attitude movement and trajectory planning time and complicated path trajectory points in the offline system of robots in the traditional production line, this paper proposes an optimization trajectory optimization algorithm based on the combination of accurate unit scanning and Cartesian spatial trajectory planning. When the known effective working space range is determined, the end effector of the robotic arm conducts trajectory search according to a specified time series after obstacle avoidance to find an optimal trajectory that meets the user parameters. At the same time, for the polyline trajectory, this paper uses the multi-dimensional interpolation method, that is, the adopted Cartesian space trajectory planning for smooth processing. Finally, through the MatlabRobot toolbox to end effector arm end angular velocity, angular acceleration for simulation research, according to the simulation results, it can be seen that the arm end angular velocity in three-dimensional space is controlled at −1m/s ~ 1m/s, angular acceleration is basically stable in the Z axis, and controlled in the X axis at −3m/s 2 ~1m/s2, so the control of the fusion algorithm on the motion and trajectory planning of the robot system is reasonable, and it also meets the quantitative needs of users.

Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses

In this paper a cost and time efficient approach to setup a compliance model for industrial robots is presented. The compliance model is distinctly determined by the gear’s stiffness parameters which are tuned by an optimal design of experiments approach. The experimental setup consists of different poses of the robot’s axes together with the applied force at the tool center point (TCP). These robot poses represent together with defined forces the experimental setup where the deviation of the robot under defined force is measured. Based on measurements of the displacement of the TCP the stiffness parameters for the compliance model are estimated and afterwards validated in new experiments. The efficiency of this approach lies in the reduced amount of experiments that are needed to identify the stiffness parameters that are parameters inherent to the compliance and the less complex experimental setup.

Pose-Dependent Cutting Force Identification for Robotic Milling

AbstractCutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach.

The use of robots for machining operations has become very popular in the last few decades. However, the challenge of the robotic-based machining process, such as surface finishing on curved surfaces, still persists. Prior studies (non-contact- and contact-based) have their own limitations, such as fixture error and surface friction. To cope with these challenges, this study proposes an advanced technique for path correction and normal trajectory generation while tracking a curved workpiece's surface. Initially, a key-point selection approach is used to estimate a reference workpiece's coordinates using a depth measuring tool. This approach overcomes the fixture errors and enables the robot to track the desired path, i.e., where the surface normal trajectory is needed. Subsequently, this study employs an attached RGB-D camera on the end-effector of the robot for determining the depth and angle between the robot and the contact surface, which nullifies surface friction issues. The point cloud information of the contact surface is employed by the pose correction algorithm to guarantee the robot's perpendicularity and constant contact with the surface. The efficiency of the proposed technique is analyzed by carrying out several experimental trials using a 6 DOF robot manipulator. The results reveal a better normal trajectory generation than previous state-of-the-art research, with an average angle and depth error of 1.8 degrees and 0.4 mm.

ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots

This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain-awareness. We use the 90 kg HyQ and 140 kg HyQReal quadruped robots to validate ViTAL, and show that they are able to climb various obstacles including stairs, gaps, and rough terrains at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds, and show that ViTAL outperforms the baseline.

Tightly Coupled 3D Lidar Inertial SLAM for Ground Robot

This paper proposes a robotic state estimation and map construction method. The traditional lidar SLAM methods are affected by sensor measurement noise, which causes the estimated trajectory to drift, especially along the altitude direction caused by lidar noise. In this paper, ground parameters in the environment are extracted to construct the ground factors to compress the trajectory estimation drifting along the altitude direction using the characteristics of constant robot pose relative to the ground. Our method uses tightly coupled lidar and inertial to obtain low-drift lidar odometry factors by factor graph optimization. The optimized lidar odometry factors are then added to a global factor graph, together with ground, loop closure, and GPS factors to obtain accurate robot state estimation and mapping after factor graph optimization. The experimental results show that our method has comparable results with advanced lidar SLAM methods, and even performs better in some complex and large-scale environments.