This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and ...safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.
This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed ...R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg-Marquardt (LM) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from -15 mm to 15 mm for the performance of capturing scans.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Improving the capacity of intersections is the key to enhancing road traffic systems. Benefiting from the application of Connected Automated Vehicles (CAVs) in the foreseeing future, it is promising ...to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs. Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries. However, it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles (HVs). To address the research gap, this paper proposes a spatiotemporal-restricted A∗ algorithm to obtain efficient and flexible lane-free trajectories for CAVs. First, we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors. Second, we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm. Third, we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs. The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A∗ algorithm, while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency. The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.
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•Apply multiple spatiotemporal constraints to lane-free trajectory planning.•Present a spatiotemporal sparse sampling method to enhance the heuristic search.•Provide an effective decision-making scheme for mixed flows at intersections.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Optical tracking technique based on multiple cameras is widely employed in robots, virtual reality, and industrial measurements due to its excellent position accuracy. However, its 6-D tracking ...practicability is impaired by the line-of-sight issue and the complicated tracker based on multiple markers. Hence, we implemented a custom-built hybrid tracking system consisting of four-camera equipment and a 6-D tracker comprising a single optical marker and an automatic heading reference system (AHRS). AHRS provides the 3-D orientation of the tracked object directly and compensates for the 3-D position when the optical tracking is occluded. A two-stage cascaded adaptive unscented Kalman filtering (CAUKF) was proposed to enhance real-time fusion tracking performance. The CAUKF not only provides a reliable frequency enhancement solution for the optical tracking adapting to various frequencies, but also improves the continuality of prediction on fusion state variables and the corresponding covariance matrix, which helps initialize the occlusion tracking accurately. When an occlusion occurs, a learning-based adaptive unscented Kalman filter (LAUKF) module can adaptively adjust noise estimation matrices in the unscented transformation according to the AHRS data, thereby significantly reducing the position estimation error. Experimental results reveal that the proposed tracking approach achieved 6-D tracking at 100 Hz with 0.32-mm position error (mean absolute error) and 0.1° orientation error. This study furnishes a novel implementation method for the full 6-D pose tracking with a simplified tracker structure and improved accuracy, continuity, and stability.
Robotic manipulation and automation have gained increasing popularity in the food manufacturing industry due to their potential benefits for enhancing hygiene standards, enforcing quality ...consistency, promoting product traceability, and reducing labor costs. As a majority of robotic manipulation, the pick-and-place operation plays a crucial role in food handling applications. However, the reproducibility and comparability of results have put a dilemma that hinders further advancement in this field, especially for those unstructured scenarios. To tackle such thorny issues, this article proposes a benchmarking framework for system-level evaluation of robotic-assisted food handling under the line production environment. A typical food handling scenario, including a pick-and-place operation and a packing operation, is presented as the benchmark task, where food items are supposed to be picked from the tray and placed in the serving dish. A robotic system incorporating a high-speed Delta robot, vision system, conveyor belt, and end-effector is developed as the testbed for the benchmarking implementation. Finally, five variants of the robotic system with different end-effectors are evaluated using the proposed benchmarking framework. Comparative studies illustrate the performance of various benchmarked systems and validate the applicability of the benchmarking strategy for the food handling context. Videos of our experiments are available at https://youtu.be/SBAOoswnjWM .
Social robots have gained widespread attention for their potential to assist people in diverse domains, such as living assistance and logistics transportation. Human-accompanying, i.e., walking ...side-by-side with a person, is an expected and essential capability for social robots. However, due to the complexity of motion coordination between the target person and the mobile robot, the accompanying action is still unstable. In this study, we propose a human-accompanying control strategy to improve the motion coordination for better practicability of the human-accompanying robot. Our approach allows the robot to adapt to the motion variations of the target person and avoid obstacles while accompanying them. First, a human-robot interaction model based on the separation-bearing-orientation scheme is developed to ascertain the relative position and orientation between the robot and the target person. Then, a human-accompanying controller based on behavioral dynamics and model predictive control (MPC) is designed to avoid obstacles and simultaneously track the direction and velocity of the target person. Experimental results indicate that the proposed method can effectively achieve side-by-side accompanying by simultaneously controlling the relative position, direction, and velocity between the target person and robot.
The optical tracking system (OTS) plays a vital role in the computer-assisted surgical navigation process, whereas the performance of the commonly used binocular stereo vision is affected by the ...line-of-sight problem and limited workspace. Thus, this article proposed a prior knowledge-based multicamera reconstruction model (PKRM) to both expand the tracking workspace and improve the tracking robust and computational efficiency of OTS when working in unstructured clinical conditions. This reconstruction model inherits the advantages of the geometrical method, data-driven method, and gating technique (GT). First, we added the geometric principle as the prior knowledge to optimize the training of the multicamera OTS reconstruction model through the Lagrange multiplier method; hence, the prior knowledge feedforward NN (PKFNN) was built. Second, besides the training features, the state of camera (SOC) was extracted in advance to determine the NN structure using GT. According to the SOC feature, the OTS can be self-adaptive to the changing field of view (FOV) caused by optical occlusion, which is frequently occurred in surgery. Furthermore, experiments were carried out to verify the performance of the proposed model, whose accuracy and runtime performed 0.4627 mm and 0.0016 ms, respectively. Results demonstrate that the proposed reconstruction model can achieve higher accuracy and computational efficiency than both the geometrical model and the data-driven model. Especially, by considering SOC as the state prior knowledge, the tracking robustness is enhanced when one or two of the four cameras are not working properly. Note to Practitioners -The original motivation for this article derives from both the line-of-sight limitation and robust demand for optical tracking of surgical instruments. The performance of the multicamera optical tracking system (OTS) depends on its reconstruction model. However, the geometric reconstruction model requires more calculation to obtain high accuracy, which will enlarge the latency and reduce the update rate. In our previous work, the reconstruction model based on the neural network (NN) has achieved accurate tracking in real-time, while the training of the model tends into local optimal values. Hence, we proposed the prior knowledge feedforward NN model to improve the accuracy and computational efficiency. Moreover, to guarantee the line-of-sight in the optical occlusion, the state of camera combining with the gating technique enables the OTS to be self-adaptive for changing the field of view, which greatly ensures the robust tracking process with larger workspace in case of line-of-sight obstructions.
U.S. Manufacturing sector consumes remarkable amount of energy while the energy efficiency is quite low. Energy consumption of CNC machines is significant and various empirical models have been ...developed to model the Specific Energy Consumption (SEC) of CNC machines. However, most of the models are developed for specific machines, hence have limited applications in manufacturing industry. In this research, a general empirical SEC model for milling machine at certain power level is developed based on actual cutting experimental data. In this model, stand-by power and spindle power are used in the SEC model for the first time. The Material Removal Rate (MRR) is used to represent cutting parameter. The proposed model is fitted by regression analysis and validated using experimental data. Results show that the proposed model can be applied on various milling machines with an average absolute residual ratio of 6%. The model is also validated through a series of cutting experiments on a machine center, with an accuracy of 91.5%, for the SEC calculation. Compressed Air Systems (CAS) are the 3rd energy source in industrial facilities and has a significant impact on the energy efficiency of manufacturing systems. This thesis provides an overview of all typical energy conservation measures (ECM) for CAS as well as all the energy savings calculations methods. To provide a simple guideline for decision maker, an economic benchmark analysis is presented for typical ECMs using the baseline conditions from Technical Reference Manuals (TRM) of multiple States in the US. Due to the ECMs correlate with each other, the comprehensive savings from multiple ECMs is not the simple summation of each individual measure. An integrated model is proposed to investigate the interrelationships of all measures and obtain combined savings. Meanwhile, the dryer’s impact to the other ECMs is included for the first time in the proposed model. CAS is a dynamic system with changing load, operations, and specifications etc. Therefore, the savings is a variable depending on system situations. The reliabilities of the ECMs are analyzed to obtain their dynamic characteristics. The optimization of the ECMs is discussed to demonstrate the interrelationships and dynamic of the savings mechanisms. While the above studies focus on the energy modeling and savings of important system of manufacturing activities, it is important to have an overall understanding of the energy efficiency and saving potentials. Energy intensity is commonly used as an indicator for the energy efficiency. Encourage the implementation of proposed ECMs is the main strategy for energy efficiency improvement programs to influence the plant’s energy intensity. Study the trends of energy intensity of SMEs and the acceptance of proposed ECMs could draw outlines of the changes of energy usage, understand the flavor of plant managers towards energy savings projects and reflect the shift of technologies in the past decades. This thesis found that the industry structure of SMEs had limited effects on the energy usage while the fluctuation of producing activities and improvement of energy efficiency were the main contributors over the past three decades. Compared with the manufacturing plants with best energy efficient practices, an average of 15.71% of electricity and 14.51% of natural gas could be saved. However, the saving potentials of each subsectors varies dramatically due to the differences of production processes and energy use strategies. This discrepancy also reflected on the implementation of ECMs. Special planning and stimulations should be developed to accommodate the unique saving demands for different industries, ECM types and regions.
Purpose
This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global ...feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering.
Design/methodology/approach
The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering.
Findings
No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans.
Originality/value
A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.
Scan registration is the fundamental of several advanced 3D data processing techniques, and the majority of existing global registration methods depend on explicit features. This paper presents a ...global feature-less scan registration strategy based on the Spherical Entropy Image (SEI) and the Generalized Convolution Theorem. The structure of the scan is described by a spherical function named SEI in this paper. The 3D rotation is then estimated by aligning the corresponding SEIs. After that, the Phase Only Matched Filtering (POMF) is adopted for translation recovery. No particular features in the input data are prerequisite to our method. Unlike the feature-based methods, the performance of our method does not reply on specific proper parameters. The algorithm is validated using the challenging data captured by our custom-built platform and publicly available datasets. The experimental results illustrate the parameter-independence, high reliability and efficiency of our novel algorithm in registration of feature-less scans.
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