Robotics and Autonomous Systems最新文献

This research presents a novel robot system that combines active and passive components to enhance compliance and dependability. The system is based on a continuous variable stiffness wrist. A wrist was created that met the requirements and a combination of active and passive control methods was suggested to insert and regulate forces effectively. The control strategy is based on the Cosserat rod model, with the fundamental concept being calculating the position and orientation of the component using data on the force exerted during contact between the parts and the stiffness of the contact between the shaft and hole components. This process converts the hard assembly into a flexible contact. Compliance is monitored via force and vision sensors, which allows for the shaft-hole assembly operation to be carried out even with attitude alignment problems, resulting in a notable decrease in the precision needed for component alignment. Initially, the camera supplies the first positional data of the shaft component for the robotic system. In addition, the performance of the wrist with variable stiffness is evaluated in terms of stiffness. Additionally, the calculation of relative deformation between components is examined using contact force information. Moreover, a robust active/passive hybrid insertion control technique, which relies on contact force, is proposed. Finally, the shaft-hole assembly task substantiates the necessity for contact force monitoring in the insertion assembly process. This control technique has demonstrated its efficacy in ensuring passive-compliant assembly performance. Furthermore, the variable stiffness wrist has been employed in robotic grinding for surfaces with curved contours to validate its effectiveness.

High-temperature industrial inspection has several challenges, especially if it is an autonomous inspection through mobile robots. This paper introduces the mobile robot CRAS (Climbing Robot for Advanced inSpection) for autonomous non-destructive testing (NDT) of weld beads from industrial super-duplex stainless steel vessels. It covers the design process, previous works, main challenges, and field testing. The main objective of the robot is to perform ultrasonic inspection over a heated separator tank while it operates. The metallic surfaces of the structure to be inspected are under constant high temperatures (80 °C–135 °C) when in operation. CRAS presents magnetic wheels as an adhesion method and a perception system able to identify and follow weld beads. The NDT method uses the phased-array ultrasonic technique. This paper approaches and proposes a solution for three challenges due to the high temperature: the loss of robot adhesion, ultrasound signal deformation, and the risk of damaging sensitive equipment such as sensors, cameras, and any electronic component. The CRAS adopted solutions are detailed and future steps of CRAS development are also addressed.

Accurate depth information is crucial for autonomous systems to navigate and interact safely with their surroundings. Passive stereo-vision cameras, such as the ZED 2i, obtain depth information through stereo-image analysis and triangulation. The study measures and assesses the true capabilities of the ZED 2i camera in a real indoor office environment. Furthermore, the study provides a standard test setup to reproduce similar benchmarks with different depth cameras. To achieve the set goals, an experiment was devised and carried out in an office environment to determine the camera depth error and Root Mean Square Error (RMSE) of the depth estimates at different distances using four different image resolutions. The results reveal that the depth error has heavy tails, implying that outliers substantially impact accuracy. Hence, depth errors should not be modeled as normally distributed errors. Moreover, only two out of four resolutions provided the capability of acquiring depth data up to 18 m. These insights provide guidelines for understanding the ZED 2i camera's true capabilities, determining its suitability for different applications and environments, and giving baselines for future tests of other competing sensor units. Furthermore, the study offers a simple, inexpensive, and laboratory space-free, yet effective setup that does not need extensive equipment or complex configurations to facilitate the benchmarking of depth cameras in different working environments.

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.

This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a combination of fixed-lag smoothing and keyframe-based point cloud matching that makes it possible to deal with a few seconds of completely degenerated range data while efficiently reducing trajectory estimation drift. It also incorporates multi-camera visual feature constraints in a tightly coupled way to further improve the stability and accuracy. The global trajectory optimization module directly minimizes the registration errors between submaps over the entire map. This approach enables us to accurately constrain the relative pose between submaps with a small overlap. Although both the odometry estimation and global trajectory optimization algorithms require much more computation than existing methods, we show that they can be run in real-time due to the careful design of the registration error evaluation algorithm and the entire system to fully leverage GPU parallel processing.

Although industrial robots have been successfully used in a wide spectrum of applications for production automation, they still face challenges for many high precision tasks especially in low-volume high-mix production due to their low absolute positioning accuracy. To respond to such rapidly changing production tasks, an efficient means is required to determine the pose relationship between the robot and the workpiece without human intervention such as teaching the robot. For this purpose, the paper proposes the use of the Extended Kalman Filter (EKF) with an optical tracking system to improve the robot positioning accuracy with a particular focus on the target point tracking of the end-of-arm tool, which is an essential part for many robotic tasks. To this end, a comprehensive kinematic error model is first derived for the end-of-arm tool that accounts for the errors in the Denavit-Hartenberg (D-H) parameters, the positioning errors of the robot base and the end-of-arm tool installation. Then, by using the optical tracking system, the pose of the end-of-arm tool relative to the workpiece can be determined in an efficient way. Based on the EKF algorithm, the kinematic parameter errors of the system can be estimated online to compensate the positioning error of the target point during the robot movement. Simulation and experimental tests have been performed to demonstrate the effectiveness of the proposed method. The proposed approach utilizes the given trajectory to design a compensation scheme where the kinematic parameter errors of the robot are estimated during the motion and then the positioning error of the end-of-arm tool is compensated at the target point. As a result, this approach can improve the target point accuracy of the robot without continuous feedback to reduce the tracking error along the trajectory in real time. It is easy to implement and suitable for low-volume, high-mix scenarios to determine the pose relationship between the robot and the workpiece without human intervention.

In this paper, the performance criteria for various four-wheeled mobile robots that are crucial for assessing a robot’s fitness for mobility to successfully complete missions are introduced. The seven proposed performance indices, the root mean squared acceleration (RMSA), posture variance index (PVI), static stability margin (SSM), force angle stability margin (FASM), energy stability margin (ESM), friction requirement (${\mu }_r$), and velocity constraint violation (VCV), address the fluctuation, rollover, and slippage problems in four-wheeled mobile robots. The simulations considered a square bump-shaped obstacle, and the dimensions of the robot were based on nine simulation cases in a 3D environment. Additionally, a methodology for evaluating these seven criteria is outlined. To streamline the simulation process, Taguchi’s catalog of orthogonal arrays (OAs) was used for the experimental design, specifically L9 OA with four factors and three levels was used. Analysis of means (ANOM) was applied to assess the influence of each design factor on the seven criteria, leveraging the OA orthogonality. Finally, the sensitivity analysis and potential for evaluating general mobile robots in the future are discussed.

Snake robots with limbless structure and rich locomotion gaits have been designed and built for wide application in various fields including military reconnaissance, pipeline operation, disaster search and rescue, etc. However, the problem how to flexibly and smoothly control switch and change of different locomotion gaits is still facing enormous challenges. A novel unified design rule of the CPG network model composed of improved Hopf oscillators is proposed, based on which a variety of different network structures can be created by designing connection distances and coupling weights among all oscillator units. Through the relationships between the control parameters of the Hopf oscillator, decoupling of the bifurcation parameters is achieved to solve inconsistent output waveform amplitude when the bifurcation parameters are not completely equal. Furthermore, five typical movement modes of biological snake are designed and smooth switch between different locomotion gaits is realized. A control system is constructed based on the Robot Operating System (ROS) and a prototype of snake robot is built, and the effectiveness of the proposed CPG model in controlling locomotion gaits was verified through simulations and experiments. The CPG modeling approach has important theoretical significance and practical instructive value for motion planning and gait control of snake robots in complex environments.

Work-related Musculoskeletal Disorders (WMSDs) are the most common occupational diseases caused by the prolonged performance of strenuous work, such as manual handling of loads or long-term maintenance of incongruous postures. Different safety protocols are implemented to reduce WMSDs and optimize the working environment, but one of the most promising solutions is using occupational exoskeletons (OEs). However, to truly acknowledge the benefits of OEs and be able to introduce them into daily business use, devices must pass several development and testing stages that determine the Technology Readiness Level (TRL). This review study aims to present an up-to-date collection of the most advanced assessments of exoskeletons for upper and back support, ranging from laboratory real-task simulations to operational scenarios in industrial sites. To identify relevant studies, we conducted comprehensive searches across different electronic databases, i.e., PubMed, Scopus, and Web of Science. Different keywords were used for the literature search, e.g., occupational exoskeleton, industrial exoskeleton, etc. Studies were included if they investigated the assessment of exoskeletons in the laboratory with real tasks or an industrial environment. We identified 45 research articles that fulfilled this selection criterion. Several features are compared and discussed in detail, such as industrial environment, experimental protocol, task performed, and exoskeleton typology. These data allowed us to formulate results that report the correspondence or discrepancy between the number of papers testing exoskeletons and WMSDs in different industrial sectors, the type of assessment performed, and the impact of exoskeletons on workers and industries at different TRLs. Among the results, the incidence of WMSDs in the manufacturing industry is 21.13%, while the adoption of exoskeletons in the same field is the highest with respect to the other industrial fields, at 44.45%. Electromyography (EMG) and Questionnaires were the most evaluated typologies across all development and testing stages (with an incidence of 64% across the selected articles). Additionally, an average reduction of EMG activity was reported, with 24% for Upper Limb and 20% for Back Support. Regarding the subjective assessment reported in the questionnaires, 68% of the studies reported a positive evaluation. Based on these outcomes, this work provides a framework for an effective evaluation process for the OEs to raise TRL with recommendations for future research activities.

Wearable rehabilitation robots have become an important auxiliary tool in rehabilitation therapy, providing effective rehabilitation training and helping to recover damaged muscles and joints. In response to the difficulty of traditional control methods in solving various constraints in the trajectory tracking process of the Upper Limb Rehabilitation Robot (ULRR), this study uses model predictive control to study the trajectory tracking problem of the upper limb rehabilitation robot. Firstly, based on the Lagrangian dynamic model of wearable rehabilitation robots, an extended state space model with pseudo linearization of the system was established. Given the performance indicators and various constraints of the system, a corresponding model predictive controller is designed based on the Laguerre model to ensure system performance while greatly reducing the computational complexity of predictive control. Secondly, the stability of the model predictive controller is demonstrated, and a disturbance observer is introduced into the controller to achieve compensation for slow-varying perturbations; a joint space sliding mode variable is also introduced to achieve simultaneous tracking of the joint’s desired position and desired velocity. Finally, taking a planar two bar robot as an example, comparative simulation verification was conducted on unconstrained joint trajectory tracking and constrained joint trajectory tracking. The simulation results show that the model predictive controller can achieve simultaneous tracking of joint expected trajectory and expected speed while meeting various constraints. It has good effects in improving patient motion control ability and reducing patient fatigue, providing new research ideas and methods for the field of rehabilitation therapy.