Robot Pose Research Articles

Experimental-numerical approach to the estimation of uncertainty in robot pose measurement

Accurate pose measurements are crucial for the reliability of the kinematic calibration and performance evaluation of a robotic manipulator. Spherical coordinate measurement systems such as laser trackers and total stations are commonly used to collect sets of 3D points from which position and orientation of a moving coordinate frame attached to the end-effector in relation to a reference coordinate frame are calculated. Estimating the uncertainty of this specific measurement task is necessary to provide traceability to these measurements. In this paper we propose combing measurements of calibrated artefacts with Monte Carlo simulations to estimate this task-specific measurement uncertainty. The application of this method has been demonstrated for the performance evaluation of a Stewart Platform using a robotic total station. Expanded uncertainties between 0.40 mm and 0.63 mm have been estimated for position. Expanded uncertainty was 0.10° for all orientation results.

GR-LOAM: LiDAR-based sensor fusion SLAM for ground robots on complex terrain

Simultaneous localization and mapping is a fundamental process in robot navigation. We focus on LiDAR to complete this process in ground robots traveling on complex terrain by proposing GR-LOAM, a method to estimate robot ego-motion by fusing LiDAR, inertial measurement unit (IMU), and encoder measurements in a tightly coupled scheme. First, we derive a odometer increment model that fuses the IMU and encoder measurements to estimate the robot pose variation on a manifold. Then, we apply point cloud segmentation and feature extraction to obtain distinctive edge and planar features. Moreover, we propose an evaluation algorithm for the sensor measurements to detect abnormal data and reduce their corresponding weight during optimization. By jointly optimizing the cost derived from the LiDAR, IMU, and encoder measurements in a local window, we obtain low-drift odometry even on complex terrain. We use the estimated relative pose in the local window to reevaluate the matching distance across features and remove dynamic objects and outliers, thus refining the features before being fed to a mapping thread and increasing the mapping efficiency. In the back end, GR-LOAM uses the refined point cloud and tightly couples the IMU and encoder measurements with ground constraints to further refine the estimated pose by aligning the features on a global map. Results from extensive experiments performed in indoor and outdoor environments using real ground robot demonstrate the high accuracy and robustness of the proposed GR-LOAM for state estimation of ground robots.

A novel algorithm based on nonlinear optimization for parameters calibration of wheeled robot mobile chasses

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis- inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial measurement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and external parameters calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation.

Study of Neural-Kinematics Architectures for Model-Less Calibration of Industrial Robots

Modeless industrial robot calibration plays an important role in the increasing employment of robots in industry. This approach allows to develop a procedure able to compensate the pose errors without complex parametric model. The paper presents a study aimed at comparing neural-kinematic (N-K) architectures for a modeless non-parametric robotic calibration. A multilayer perceptron feed-forward neural network, trained in a supervised manner with the back-propagation learning technique, is coupled in different modes with the ideal kinematic model of the robot. A comparative performance analysis of different neural-kinematic architectures was executed on a two degrees of freedom SCARA manipulator, for direct and inverse kinematics. Afterward the optimal schemes have been identified and further tested on a three degrees of freedom full SCARA robot and on a Stewart platform. The analysis on simulated data shows that the accuracy of the robot pose can be improved by an order of magnitude after compensation.

A geometric nonlinear stochastic filter for simultaneous localization and mapping

Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks as it tackles simultaneous mapping of the unknown environment defined by multiple landmark positions and localization of the unknown pose (i.e., attitude and position) of the robot in three-dimensional (3D) space. The true SLAM problem is modeled on the Lie group of SLAMn(3), and its true dynamics rely on angular and translational velocities. This paper proposes a novel geometric nonlinear stochastic estimator algorithm for SLAM on SLAMn(3) that precisely mimics the nonlinear motion dynamics of the true SLAM problem. Unlike existing solutions, the proposed stochastic filter takes into account unknown constant bias and noise attached to the velocity measurements. The proposed nonlinear stochastic estimator on manifold is guaranteed to produce good results provided with the measurements of angular velocities, translational velocities, landmarks, and inertial measurement unit (IMU). Simulation and experimental results reflect the ability of the proposed filter to successfully estimate the six-degrees-of-freedom (6 DoF) robot's pose and landmark positions.

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems usually benefit from the high density of visual features. When using filter-based solutions to localize the robot, such an environment usually uses a high number of particles to perform accurately. These two facts can lead to computationally expensive localization algorithms that are intended to perform in real-time. This work proposes a refinement step to a standard high-dimensional filter-based localization solution through the novelty of downsampling the filter using an online clustering algorithm and applying a scan-match procedure to each cluster. Thus, this approach allows scan-matchers without high computational cost, even in high dimensional filters. Experiments using real data in an agricultural environment show that this approach improves the Particle Filter performance estimating the robot pose. Additionally, results show that this approach can build a precise 3D reconstruction of agricultural environments using visual scans, i.e., 3D scans with RGB information.

Particle swarm optimization for inverse kinematics solution and trajectory planning of 7-DOF and 8-DOF robot manipulators based on unit quaternion representation

Finding the inverse kinematic solution of a serial manipulator has always attracted the attention of optimization enthusiasts, as the solution space is highly nonlinear and, depending on the number of degrees of freedom, has multiple solutions. In the literature, one can find several proposed solutions using heuristic techniques; however, for highly redundant manipulators, e.g., seven or more, the discussions focused on minimizing the positional error. In this paper, a metaheuristic approach is presented to solve not only the inverse kinematics of a 7 and 8 DOF manipulators but the proposed algorithm is used to find the robot's poses for trajectory planning where the robot is required to meet the desired position and orientation based on quaternion representation of each point along the path. The metaheuristic approach used in this paper is particle swarm optimization (PSO), where the unit quaternion is used in the objective function to find the orientation error. The results prove that the use of the unit quaternion representation improved the performance of the algorithm and that our approach can be used not only for individual poses but for trajectory planning.

Modeling and Analysis of the Stiffness Distribution of Host–Parasite Robots

The stiffness distribution (SD) of robot has a great influence on the robot pose accuracy, but the calculation efficiency and accuracy of stiffness distribution are still low. This study presents a finite element fitting method with an extremely small number of computational cells. It was developed based on experimental results of robot stiffness. This method can be employed to establish single- and multi-source fitted SD (FSD) (S-FSD and M-FSD) models for host–parasite (H–P) robots. The computational efficiency and correctness of the FSD models were verified by case studies. The configurations of six evolutionary mechanisms of an H–P robot were subjected to an SD analysis. A comparison of the six configurations shows that adding parasitic branched chains can improve the SD of the H–P robot to varying degrees. In particular, the most notable improvement was for H–P mechanism. Specifically, by averaging the stiffness of all positions, the average-stiffnesses of H–P mechanism in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> -, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> -, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> -directions were 104.10%, 1427.78%, and 1101.62% of those of the host mechanism, respectively. In the SD diagram, the medium- and high-stiffness regions of mechanism F are large and distributed in a banded pattern between the highest pose point and the furthest pose point, whereas its low-stiffness region is small and concentrated near the nearest pose point.

Mobile Robot 6-D Localization Using 3-D Gaussian Mixture Maps in GPS-Denied Environments

The mobile robot 6D localization process in GPS-denied scenarios, e.g., in a cave or a mine, is a challenging problem. This paper presents the modification of a well-known literature method using Gaussian mixture maps to determine the robot pose in rough terrain and outdoor non-urbanized environment. To improve the accuracy in rough 3D terrain, I extended the terrain description from a 1D Gaussian mixture model (1DGMM) to a 3D Gaussian mixture model (3DGMM) combining terrain height and terrain inclination in two orthogonal directions. Using this approach, one can maintain centimeter accuracy even in the most demanding workspaces. Moreover, I replaced the 3D scan matching process combined with ground-based smoothing by 6D scan processing (therefore, the 3DGMM method is capable for both land vehicles and flying robots) using the point-by-point EKF based on [12]. Thanks to the point-by-point correction approach, it is possible to run the system in real-time and process a full point cloud without GPU-based computing up to 300 000 points per second using small computers dedicated to mobile robots.

A novel inverse kinematics method for 6-DOF robots with non-spherical wrist

A novel numerical method is proposed to solve the inverse kinematics for 6-DOF robots with non-spherical wrist in this paper, which firstly simplifies the non-spherical wrist structure to a spherical one. In each iteration of the algorithm, the inverse solution of simplified robot is firstly used to calculate the end pose of actual robot to estimate the end position error between the calculated end pose and the target pose. And then the inverse solution of simplified robot gradually approaches the desired solution by compensating for the end position error. Additionally, the failure of proposed method caused by the reduction of robot workspace due to structure simplification is analyzed and then the offsetting of known end pose is introduced to improve the robustness of algorithm. Finally, numerical experiments for two typical robots prove the effectiveness of proposed method; a comparative study shows the advantages of proposed algorithm over Newton-Raphson (NR) algorithm; and the performance in processing continuous trajectories demonstrates its good practicability.

Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias

Navigation solutions suitable for cases when both autonomous robot's pose (\textit{i.e}., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by concurrently mapping the environment and observing robot's pose with respect to the map. This work proposes a nonlinear observer for SLAM posed on the manifold of the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, characterized by systematic convergence, and designed to mimic the nonlinear motion dynamics of the true SLAM problem. The system error is constrained to start within a known large set and decay systematically to settle within a known small set. The proposed estimator is guaranteed to achieve predefined transient and steady-state performance and eliminate the unknown bias inevitably present in velocity measurements by directly using measurements of angular and translational velocity, landmarks, and information collected by an inertial measurement unit (IMU). Experimental results obtained by testing the proposed solution on a real-world dataset collected by a quadrotor demonstrate the observer's ability to estimate the six-degrees-of-freedom (6 DoF) robot pose and to position unknown landmarks in three-dimensional (3D) space. Keywords: Simultaneous Localization and Mapping, Nonlinear filter for SLAM, Nonlinear filter for SLAM on Matrix Lie group, pose, asymptotic stability, prescribed performance, adaptive estimate, feature, inertial measurement unit, inertial vision unit, IMU, SE(3), SO(3), noise.

Magnetic–type Climbing Wheeled Mobile Robot for Engineering Education

The tendency of developing wheeled mobile robots in different applications is growing every day. During the past decades, the need for robots capable for climbing vertical wall has been increased. As a result of that, this paper presents a light and low cost wall climbing robot WCR of wheeled locomotion and adopted neodymium magnets to provide adhesion force. This climbing system is intended to be used for educational and research purposes. The mechanical and electrical constructions are presented. The proposed design has the ability to achieve ground-wall transitioning. In order to provide sufficient information about robot mass properties, novel mechanisms to determine the coordinates of the center of mass COM and moment of inertia MOI of the proposed climbing robot have been introduced. In addition, the vision system was suggested to provide feedback signal of robot pose (position and orienting). Finally, to evaluate the performance of the robot design, a prototype was built and tested in the laboratory environment.

Hand-eye calibration method with a three-dimensional-vision sensor considering the rotation parameters of the robot pose

Hand-eye calibration is a fundamental step for a robot equipped with vision systems. However, this problem usually interacts with robot calibration because robot geometric parameters are not very precise. In this article, a new calibration method considering the rotation parameters of the robot pose is proposed. First, a constraint least square model is established assuming that each spherical center measurement of standard ball is equal in the robot base frame, which provides an initial solution. To further improve the solution accuracy, a nonlinear calibration model in the sensor frame is established. Since it can reduce one error accumulation process, a more accurate reference point can be used for optimization. Then, the rotation parameters of the robot pose whose slight errors cause large disturbance to the solution are selected by analyzing the coefficient matrices of the error items. Finally, the hand-eye transformation parameters are refined together with the rotation parameters in the nonlinear optimization solution. Some comparative simulations are performed between the modified least square method, constrained least square method, and the proposed method. The experiments are conducted on a 5-axis hybrid robot named TriMule to demonstrate the superior accuracy of the proposed method.

Torque-based methodology and experimental implementation for industrial robot standby pose optimization

This manuscript reports on a novel methodology and experimental implementation for industrial robot standby pose optimization. First, we analyze the influence of the standby pose of robots in the reduction of their useful life by conducting a preliminary study in an automotive assembly line. Afterwards, we propose a novel methodology to optimize the standby pose of industrial robots by minimizing the torque applied in the joints. The results show that the methodology can reduce by 31.37% the average torque applied by a 200-kg-payload, 6 degree-of-freedom industrial robot in normal production conditions. In addition, we demonstrate that the methodology is robot model and tool invariant, by implementing the presented solution in a Kuka KR3 and two ABB IRB 6400r robots with different tools. The benefits of optimizing the standby pose of industrial robots in manufacturing assembly lines are twofold. First, it reduces the stress and temperature of the joints, increasing the remaining useful life. Second, it offers the possibility of substantially reducing the energy consumption of the production line, as the time spent by industrial robots in a standby pose can reach up to 80% of their total operational time.

Estimación de la covarianza de ICP para la localización de un robot diferencial mediante odometrı́a y escaneo láser

En este trabajo se presenta un método probabilístico para resolver el problema de la localización de un robot diferencial. Se usa el Filtro Extendido de Kalman (EKF) para fusionar la información obtenida por registraciones de mediciones láser mediante ICP (IterativeClosest Point) con la información de odometría provista por encoders. Para utilizar EKF es necesario estimar la covarianza de cada fuente de información, sin embargo el algoritmo ICP no devuelve la covarianza asociada. En este artículo se describe una forma de calcular esta covarianza. Los resultados obtenidos muestran que el método de fusión de sensores resulta en una estimación más precisa de la pose del robot en comparación con las estimaciones que se podrían obtener mediante odometría e ICP individualmente.

State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment

Currently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical infrared navigational algorithm hybridization (HIRNAH) system is developed to provide better state estimation and localization for mobile robots. Two navigation subsystems (inertial navigation system (INS) and, using a novel infrared navigation algorithm (NIRNA), Odom-NIRNA) and an RPLIDAR-A3 scanner cooperation to build HIRNAH. The robot pose (position and orientation) errors are estimated by a system filtering module (SFM) and used to smooth the robot’s final poses. A prototype (two rotary encoders, one smartphone-based robot sensing model and one RPLIDAR-A3 scanner) has been built and mounted on a four-wheeled mobile robot (4-WMR). Simulation results have motivated real-life experiments, and obtained results are compared to some existent research (hardware and control technology navigation (HCTNav), rapid exploring random tree (RRT) and in stand-alone mode (INS)) for performance measurements. The experimental results confirm that HIRNAH presents a more accurate estimation and a lower mean square error (MSE) of the robot’s state than those calculated by the previously cited HCTNav, RRT and INS.

Surface Parameterization and Trajectory Generation on Regular Surfaces With Application in Robot-Guided Deposition Printing

In this work, we present a novel approach to design and carry out trajectories over regular curved surfaces. This has application in a number of robot path planning problems, including our primary interest in deposition printing. Existing solutions are often ad-hoc in terms of path generation and control of robot pose and velocity. Our approach provides a unified methodology for surface fitting from 3D surface measurements and mapping a curve from 2D onto a 3D surface with minimal distortion. Robot pose control is investigated to guide the end effector along the trajectory while maintaining a standoff distance and keeping the end effector normal to the surface. Simulations and experiments show the performance and necessity of our approach in the application of deposition printing on different 3D surfaces.

A novel SLAM framework based on 2D LIDAR

SLAM is a basic problem in many mobile robot applications. The most commonly used SLAM framework is too complex, and many 3D data algorithms are not suitable for 2D LIDAR. In order to solve this problem, we propose a novel SLAM framework which is more in line with embedded platform. In our framework, we use the classic ICP algorithm as the odometer to calculate the pose of the mobile robot, and use Kalman filter as optimization to remove the accumulating drift. In the aspect of map generation, we first generate the occupancy grid map, then transform occupancy grid map to binary map as the final environment map. We build a simulation platform based on MTLAB to verify the feasibility and effectiveness of our proposed framework.

A Measurement Method for Robot Peg-in-Hole Prealignment Based on Combined Two-Level Visual Sensors

Vision measurement plays a significant role in robot automatic assembly. In this study, a new measurement method for robot peg-in-hole prealignment via a combined two-level vision system is proposed. The assembly system and a global coordinate system calibration procedure based on a dynamic coordinate system are developed to construct an accurate transformation between the coordinate systems. The subsequent hole pose is obtained with the proposed image processing method and the hole edge matching method, followed by the 3-D hole reconstruction and spatial circle fitting algorithm. A low-cost pose determination is proposed based on hole edge point selection. The experiment and analysis indicate that the hybrid measurement system has high precision in the local hole pose and global robot pose measurement accuracy. The peg-in-hole assembly results verify the measurement and calibration accuracy sideways. Our hybrid measurement system enhances the vision measurement range, local accuracy, and robot automation.

A Deep-Learning-based Strategy for Kidnapped Robot Problem in Similar Indoor Environment

We present a deep-learning-based strategy that only uses a 2D LiDAR sensor to solve the kidnapped robot problem in similar indoor environments. First, we converted a set of 2D laser data into an RGB-image and an occupancy grid map and stacked them into a multi-channel image. Then, a neural network structure with five convolutional layers and four fully connected layers was designed to regress the 3-DOF robot pose. Finally, the network was trained using multi-channel images as input. We also improved the network structure to identify the scene where the robot is localized. Extensive experiments have been conducted in practice with a real mobile robot, verifying the effectiveness of the proposed strategy. Our network can obtain approximately 2m and 5∘ accuracy indoors, and the scene classification accuracy of our network reaches up to 98%.