Redundant Motion Research Articles

Novel Retractor–Camera System Facilitates Less Traumatic Minimally Invasive Procedures and Declutters the Operation Field

Objective: To develop a novel endoscopic system that reduces trauma to the patient and declutters the surgical field for the surgeon in minimally invasive heart valve surgery. Methods: We designed and developed a retractor–camera combination for minimally invasive heart surgery; the cable and camera were connected to the underbelly of the left atrial retractor blade to provide an illuminated, wide-angle view of the mitral valve. We conducted ex vivo, in vivo, and, ultimately, a first-in-man randomized, nonanonymized clinical trial on 20 patients who required minimally invasive mitral valve surgery. Data from the preoperative period and the immediate postoperative period were gathered, and patients were followed for 3 months. Results: Our camera–retractor combo demonstrated safe and efficient exposure and vision in all detailed studies. The total operation time for the intervention group (316.5 ± 65.1 min) was slightly shorter than for the control group (317.5 ± 50.8 min). There was no significant difference in the ability to provide exposure of the mitral valve and surrounding left atrial tissue for both control and test articles. The novel camera–retractor reduced clutter in the operation field substantially and eliminated chest penetration for the camera. We also observed that the camera did not fog, did not stain with blood, and did not require frequent corrections to its position. Conclusions: We launch a novel atrial retraction–imaging platform that is less invasive for the patient and eliminates clutter and redundant movements for the surgeon.

Corpus Callosum-Mediated Interhemispheric Interactions in Cervical Spondylotic Myelopathy.

The corpus callosum is crucial for interhemispheric interactions in the motor control of limb functions. Human and animal studies suggested spinal cord pathologies may induce cortical reorganization in sensorimotor areas. We investigate participation of the corpus callosum in executions of a simple motor task in patients with cervical spondylotic myelopathy (CSM) using transcranial magnetic stimulation. Twenty patients with CSM with various MRI grades of severity of cord compression were compared with 19 normal controls. Ipsilateral silent period, contralateral silent period, central motor conduction time, and transcallosal conduction time (TCT) were determined. In both upper and lower limbs, TCTs were significantly increased for patients with CSM than normal controls ( p < 0.001 for all), without side-to-side differences. Ipsilateral silent period and contralateral silent period durations were significantly increased bilaterally for upper limbs in comparison to controls ( p < 0.01 for all), without side-to-side differences. There were no significant correlations of TCT with central motor conduction time nor severity of CSM for both upper and lower limbs ( p > 0.05 for all) bilaterally. Previous transcranial magnetic stimulation studies show increased motor cortex excitability in CSM; hence, increased TCTs observed bilaterally may be a compensatory mechanism for effective unidirectional and uniplanar execution of muscle activation in the distal limb muscles. Lack of correlation of TCTs with severity of CSM or central motor conduction time may be in keeping with a preexistent role of the corpus callosum as a predominantly inhibitory pathway for counteracting redundant movements resulting from increased motor cortex excitability occurring after spinal cord lesions.

Assessment of the Suitability of Selected Linear Actuators for the Implementation of the Load-Adaptive Biological Principle of Redundant Motion Generation.

The load-adaptive behavior of the muscles in the human musculoskeletal system offers great potential for minimizing resource and energy requirements in many technical systems, especially in drive technology and robotics. However, the lack of knowledge about suitable technical linear actuators that can reproduce the load-adaptive behavior of biological muscles in technology is a major reason for the lack of successful implementation of this biological principle. In this paper, therefore, the different types of linear actuators are investigated. The focus is particularly on artificial muscles and rope pulls. The study is based on literature, on the one hand, and on two physical demonstrators in the form of articulated robots, on the other hand. The studies show that ropes are currently the best way to imitate the load-adaptive behavior of the biological model in technology. This is especially illustrated in the context of this paper by the discussion of different advantages and disadvantages of the technical linear actuators, where ropes, among other things, have a good mechanical and control behavior, which is very advantageous for use in an adaptive system. Finally, the next steps for future research are outlined to conclude how ropes can be used as linear actuators to transfer load-adaptive lightweight design into technical applications.

A novel redundant cooperative control strategy for robotic pollination

This paper proposes a novel Redundant Cooperative Control (RCC) strategy to address the pollination sequence planning problem for a mobile pollination robot in the greenhouse. Firstly, the RCC strategy divides the robot’s workspace into subspaces based on the distribution of flowers through clustering. Subsequently, the RCC strategy plans the traveling salesman problem within the visual field of these subspaces, seeking a compromise solution for an optimal path. Finally, the RCC strategy utilizes the redundant motion capabilities of the mobile chassis and robotic arm to accomplish the pollination task during the movement. The RCC strategy exploits the available redundancy in both the agricultural robot’s mobile platform and end-effector, carefully balancing computational complexity and path quality. To validate the effectiveness of the RCC strategy, we designed a series of experiments conducted on our pollination robot platform, measuring different strategies. The results demonstrate that the RCC strategy achieves an average pollination rate of 7.5 s per flower, representing a 36.4% improvement compared to the most common intermittence strategy. Furthermore, the RCC strategy can be extended to tasks such as harvesting and pruning, significantly enhancing the efficiency of robot task execution even in industrial and service sectors.

Near-Sensor Reservoir Computing for Gait Recognition via a Multi-Gate Electrolyte-Gated Transistor.

The recent emergence of various smart wearable electronics has furnished the rapid development of human-computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off-site digital computers typically result in serious latency and energy consumption problems. In this work, a multi-gate electrolyte-gated transistor (EGT)-based reservoir device for efficient multi-channel near-sensor computing is reported. The EGT, exhibiting rich short-term dynamics under voltage modulation, can implement nonlinear parallel integration of the time-series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on-site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near-sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics.

Stiffness-Performance-Based Redundant Motion Planning of a Hybrid Machining Robot

Large-scale components usually have complex structures with high local stiffness, and the holes on them are required to be machined with high precision, which makes it important and challenging to study how to efficiently and precisely drill in the large-scale components. This article presents mobile hybrid machining equipment that consists of a five-axis parallel module, a 2-degree-of-freedom (DoF) robotic, arm and an automated guide vehicle (AGV) connected in series. With the ability of wide-range positioning and precise local processing, it has potential advantages in the drilling processing of large-scale components. Stiffness is one of the most important performances for machining equipment, and it’s highly related to the its configuration. For the discussed equipment, the stiffness is determined by the two-stage-positioning hybrid machining robot, which comprises a five-axis parallel module and a two-DoF robotic arm. The redundant motion of the hybrid machining robot makes it possible to optimize its configuration by reasonably planning redundant motion. Therefore, a redundant motion-planning method based on stiffness performance is proposed. A kinematic analysis of the five-axis parallel module, the robotic arm, and the hybrid machining robot is carried out. A hybrid robot usually consists of several subsystems, and to take the compliance of each subsystem into consideration, the stiffness-modeling method for the hybrid robot with n subsystems connected in series is proposed. The stiffness model of the hybrid machining robot is established by using this method, and the variation of the stiffness magnitude has the same trend as that obtained by using FEA software. Stiffness magnitude and isotropy indices are proposed to evaluate the robot’s stiffness performance along the axis of the spindle and in the plane perpendicular to the axis of the spindle. The redundant motion of the hybrid machining robot is planned by maximizing the stiffness magnitude along the spindle axis, with priority to the stiffness isotropy index. Finally, the drilling experiment is carried out, and the results show that the relative error of the hole diameter obtained under the optimal configuration of the hybrid machining robot is 1.63%, which is smaller than those obtained under the other two configurations for comparison with relative errors of 3.75% and 3.50%, respectively. It proves the validity of the redundant motion-planning method. The proposed stiffness-modeling method and the stiffness-performance indices are also applicable to other hybrid machining robots.

Time-Synchronized Tracking Control for 6-DOF Spacecraft in Rendezvous and Docking

For the short-rangerelative motion of the on-orbit service spacecraft during rendezvous and docking missions, this article improves control performance with the describable straight line convergence trajectory and reduces energy consumption by proposing and solving the spacecraft time-synchronized control problem. This problem requires that the closed-loop system of the spacecraft is finite/fixed-time stable, all nonzero elements converge synchronously, and the trajectory of the closed-loop system tends to be the describable straight line. Based on the time-synchronized stability (TSS) and fixed-time-synchronized stability (FTSS), this article proposes the TSS and FTSS controllers for the 6-degree of freedom (DOF) spacecraft to solve the problem. For the spacecraft closed-loop system under these optimization-free methods, the derivative of the state is always opposite to the direction of the state, then the system trajectory will tend to a straight line, and the redundant motion components in the direction orthogonal to the state are significantly suppressed (performance improvement), and the excess energy loss is reduced considerably. Conversely, redundant motion components will cause excess transient motion and more energy consumption. Furthermore, this article proposes the linear combination theorem of the ratio persistence property, which further enriches the theoretical tools for the time-synchronized control method. The rigorous theoretical proof and comparative simulation verify the rationality, effectiveness, and advantages of the controllers proposed.

3D Flattering Amplified Neural Network-Based Segmentation of Amygdala and Hippocampus

Abstract Recent emergence in deep learning resulted in significant improvement in the segmentation accuracy of sub cortical brain structures like hippocampus and amygdala. The traditional methods of segmentation cannot produce an ideal segmentation result that exhibits issues like redundant computations, inconsistencies, coefficient variations and motion artifacts. Therefore, in this paper, an improved 3D Flatteringly Amplified Neural Network model for biomedical imaging is efficiently proposed, which can make full use of the 3D spatial information of MRI image itself to overcome the inconsistency of segmented images along with equalizing the coefficient variation of tiny region of brain image segmentation. Also while equalizing the coefficient, certain significant minute details are lost due to motion artifacts hence, the robust Amyg-Hippo Seg algorithm has been introducing that extracts the features through deep learning, and achieve high-precision segmentation, it reduced the computational complexity without neglecting minute features. In addition, the Daytona dropout function provides uncertainty information and reduces over-fitting problems. The outcome of the proposed work efficiently segments the most significant regions of hippocampus and amygdala with 97.4% accuracy.

Human-Computer Interaction of Networked Vehicles Based on Big Data and Hybrid Intelligent Algorithm

Intelligent connected vehicle (ICV) refers to realizing the exchange and sharing of intelligent information between vehicles, roads, people, clouds, and so on by carrying advanced on-board sensors, controllers, actuators, and other devices and combining modern communication and network technology. In the age of big data, the information of everything can be transformed into digital resources, and transportation big data has become a basic resource. This paper constructs a big data platform for traffic data processing to realize the function of real-time collection, processing, and analysis of traffic data. Based on the proposed big data platform, the parallel programming framework of MapReduce and HDFS distributed storage system are used to process the real-time vehicle dynamic information in parallel, and the output result is used as the input of running genetic algorithm simulated annealing (GA-SA) for parallel calculation. At the same time, it studies the impact of various elements on users’ interactive behavior, constructs the demand framework and design model of automobile human-computer interaction, and then realizes fast and comprehensive search. The experimental results show that the human-computer interaction method of intelligent networked vehicle can find the optimal driving path, transmit it to each networked vehicle through the human-computer interaction system, realize human-computer interaction, reduce the impact of user unintentional operation on redundant motion, reduce the motion error accumulation of the system, and improve the performance of human-computer interaction system.

SCVP: Learning One-Shot View Planning via Set Covering for Unknown Object Reconstruction

The view planning (VP) problem in robotic active vision enables a robot system to automatically perform object reconstruction tasks. Lacking prior knowledge, next-best-view (NBV) methods are typically used to plan a view sequence, with the goal of covering as many object surface areas as possible in an unknown environment. However, such methods have two problems: (1) they are unable to perform global path planning; and (2) the reconstruction process is inefficient because of time-consuming ray casting and high movement cost. We propose a neural network, SCVP, to pre-learn prior knowledge via set covering (SC) based training so as to achieve one-shot view planning. The SCVP network takes the volumetric occupancy grid as input and directly predicts a small (ideally minimum) number of views that cover all surface areas. Given object 3D models as a priori geometric knowledge, the training dataset is automatically labeled by the set covering optimization method. We propose a global path planning method to reconstruct objects without redundant movement. Comparative experiments on multiple datasets of 3D models show that, under the condition of similar or better surface coverage, the proposed method can outperform state-of-the-art NBV methods in terms of movement cost and inference time. Real-world experiments confirm that the proposed method can achieve faster object reconstruction than other methods.

Image-based visual servoing with depth estimation

For the depth estimation problem in the image-based visual servoing (IBVS) control, this paper proposes a new observer structure based on Kalman filter (KF) to recover the feature depth in real time. First, according to the number of states, two different mathematical models of the system are established. The first one is to extract the depth information from the Jacobian matrix as the state vector of the system. The other is to use the depth information and the coordinate point information of the two-dimensional image plane as the state vector of the system. The KF is used to estimate the unknown depth information of the system in real time. And an IBVS controller gain adjustment method for 6-degree-of-freedom (6-DOF) manipulator is obtained using fuzzy controller. This method can obtain the gain matrix by taking the depth and error information as the input of the fuzzy controller. Compared with the existing works, the proposed observer has less redundant motion while solving the Jacobian matrix depth estimation problem. At the same time, it will also be beneficial to reducing the time for the camera to reach the target. Conclusively, the experimental results of the 6-DOF robot with eye-in-hand configuration demonstrate the effectiveness and practicability of the proposed method.

Increasing the Efficiency of a Robotic Cell Using Simulation

Abstract The paper deals with the possibilities of increasing the efficiency of a robotic cell. The robot assembly station is a part of a multi-robotic workplace capable of performing solitary operations. The complete production cycle of the cell is subjected to a thorough analysis. Deficiencies, having a direct impact on cell efficiency, are identified. These shortcomings include redundant movements of the robotic arm, the absence of an inspection mechanism for the presence of assembly parts, and inefficient instructions of the production cycle algorithm. The identified deficiencies are eliminated using the CIROS simulation tool. The result of the adjustments is a global 11.4% increase in the efficiency of the robotic cell in terms of time performance.

Numerical Quantification of Controllability in the Null Space for Redundant Manipulators

Redundant motion, which is possible when robotic manipulators are over-actuated, can be used to control robot arms for a wide range of tasks. One of the best known methods for controlling redundancy is the null space projection, which assigns a priority while executing desired tasks. However, when the manipulator is projected into null space, its motion would be limited, since the motion is only permitted in the direction that does not interfere with the primary task. In this study, we have analyzed the null space projector matrix to derive the appropriate direction of the redundant motion by quantifying the allowed motion in each direction. As a result, we have found an ellipsoidal boundary, in which the redundant motion is permitted to move. We have named this ellipsoidal boundary as ’null space quality’ in directions. The proposed null space quality shows similar aspects with that of the robot manipulability, but it reveals a decisively different value when the manipulator operates within the null space. The experimental results showed that the robotic manipulator tracked the sinusoidal input trajectory with reduced root mean square (RMS) error by 33.84%. Furthermore, we have demonstrated the obstacle avoidance of a robotic arm utilizing the null space projector while considering the null space quality.

Adaptive Error-State Kalman Filter for Attitude Determination on a Moving Platform

This article presents a loosely coupled visual/inertial hybrid system for relative attitude determination on a moving platform. The inertial measurements are accomplished by two gyroscopes, of which the slave gyroscope removes the redundant motion of the platform from the total motion sensed by the master gyroscope. To correct integration drift error and compensate for the gyroscope biases, we fuse the inertial and visual measurements fused together through the error-state Kalman filter (ESKF). Furthermore, an improved ESKF is proposed by utilizing the variational Bayesian (VB) inference to adaptively estimate the process and measurement noise covariance. To realize direct estimation of the process noise covariance, a latent variable is introduced and inferred together with the noise covariances and error-state. The practical experimental setup is built up and experiments of two different motion trajectories are conducted. The ESKF, the cubature Kalman filter (CKF), the extended Kalman filter (EKF), the proposed filter, and the other two adaptive filters are implemented for comparison to verify the effectiveness of the proposed filter. The results demonstrate the proposed filter show superiority in reducing both the root mean square error (RMSE) and maximum error (ME) and improving the filter consistency.

Guiding functional reorganization of motor redundancy using a body-machine interface

BackgroundBody-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural problem of understanding the operation of the interface and b) the performance problem of controlling the external device with high efficiency. We hypothesize that these problems, while being distinct are connected in that aligning the space of body movements with the space encoded by the interface, i.e. solving the structural problem, facilitates redundancy resolution towards increasing efficiency, i.e. solving the performance problem.MethodsTwenty unimpaired volunteers practiced controlling the movement of a computer cursor by moving their arms. Eight signals from four inertial sensors were mapped onto the two cursor’s coordinates on a screen. The mapping matrix was initialized by asking each user to perform free-form spontaneous upper-limb motions and deriving the two main principal components of the motion signals. Participants engaged in a reaching task for 18 min, followed by a tracking task. One group of 10 participants practiced with the same mapping throughout the experiment, while the other 10 with an adaptive mapping that was iteratively updated by recalculating the principal components based on ongoing movements.ResultsParticipants quickly reduced reaching time while also learning to distribute most movement variance over two dimensions. Participants with the fixed mapping distributed movement variance over a subspace that did not match the potent subspace defined by the interface map. In contrast, participant with the adaptive map reduced the difference between the two subspaces, resulting in a smaller amount of arm motions distributed over the null space of the interface map. This, in turn, enhanced movement efficiency without impairing generalization from reaching to tracking.ConclusionsAligning the potent subspace encoded by the interface map to the user’s movement subspace guides redundancy resolution towards increasing movement efficiency, with implications for controlling assistive devices. In contrast, in the pursuit of rehabilitative goals, results would suggest that the interface must change to drive the statistics of user’s motions away from the established pattern and toward the engagement of movements to be recovered.Trial registrationClinicalTrials.gov, NCT01608438, Registered 16 April 2012.

A new image-based visual servoing method with velocity direction control

In image-based visual servoing (IBVS), for more satisfying trajectories, the target is always expected to move in straight lines to the desired positions. In this paper, a new IBVS controller for trajectory adjustment is obtained by optimizing a multi-objective function about adjusting a direction in the image plane. By applying the control law based on the optimized parameters, less redundant motion can be achieved in contrast to the traditional methods. Meanwhile, it will be beneficial for reducing the risk of the target leaving the field of view (FOV) and the robustness to the uncertainties of depth and camera internal parameters remains. The experimental results of the 6 degrees of freedom (DOF) robot with eye-in-hand configuration demonstrate the effectiveness and practicability of the proposed method.

Modal analysis on the additive manufactured robotic segments: Effect of printing densities on the vibrational properties

The high redundancy motion components play a significant role in the unmanned rescue process, especially in earthquake disaster management. Poor vicinity and reach, the chances of life rescue become challenging. Meantime, technological advancement in automation raise like anything, hence the technique espousal would support the rescue mechanism easier. Present study explores the design and characterization of a serpenoid robot, which can be used in finding the livelihoods put out of sight under the collapsed structures on a massive scale. Parametric models for the serpenoid robot segments were drafted with the use of software packages and printed in a 3D printer using (poly)lactic acid (PLA) material. Modal analysis was conducted on the printed segments to measure the natural frequency of the prismatic bar and damping response. Analysis were done on the differently printed segments at two different densities (25 and 50 units). Further, the crashworthiness study was simulated by developing the solid models, and impact hit was done at two different places. Results reveal that, natural frequency of the 3D printed segments less dependent to the excitation position. Segments printed at higher density sustain more than another segment.

Energy-efficient non-linear k-barrier coverage in mobile sensor network

K-barrier coverage is an important coverage model for achieving robust barrier coverage in wireless sensor networks. After initial random sensor deployment, k-barrier coverage can be achieved by moving mobile sensors to form k barriers consisting of k sensor chains crossing the region. In mobile sensor network, it is challenging to reduce the moving distances of mobile sensors to prolong the network lifetime. Existing work mostly focused on forming linear barriers, that is the final positions of sensors are on a straight line, which resulted in large redundant movements. However, the moving cost of sensors can be further reduced if nonlinear barriers are allowed, which means that sensors? final positions need not be on a straight line. In this paper, we propose two algorithms of forming non-linear k barriers energy-efficiently. The algorithms use a novel model, called horizontal virtual force model, which considers both the euclidean distance and horizontal angle between two sensors. Then we propose two barrier forming algorithms. To construct a barrier, one algorithm always chooses the mobile sensor chain with the largest horizontal virtual force and then flattens it, called sensor chain algorithm. The other chooses the mobile sensor with the largest horizontal virtual force to construct the barrier, other than the mobile sensor chain, called single sensor algorithm. Simulation results show that the algorithms significantly reduce the movements of mobile sensors compared to a linear k-barrier coverage algorithm. Besides, the sensor chain algorithm outperforms the single sensor algorithm when the sensor density becomes higher.

Testing the Configurations of Redundant Integrated Equipment Complexes

We consider one of the tasks of managing the redundancy in a heterogeneous integrated complex of technical equipment defined by a system of linear discrete equations. The task consists in testing a predefined configuration of the complex for implementability. We formulate both sufficient and necessary and sufficient conditions for testing configurations formalized by interface matrices. We illustrate the application and efficiency of the proposed testing conditions with an example based on a redundant aircraft motion control system.

A Computation of the Natural Arm Angle Trajectory for Redundant Manipulators

The several redundant manipulators have the self motion that rotate the axis between its shoulder and its wrist. The displacement parameter of this self motion are usually called Arm angle. This parameter need the reference frame. And its reference frame move when the wrist position are moving. The author proposed the Arm angle computation method as the natural self motion. First, the equivalent mechanism is determined for the Arm angle. Second, the inverse kinematics is solved for the axis of its joints. Third, the relationship of the derivative of the Arm angle are derived, and the angular velocity of the redundant motion axis set 0. Finally, its derivative are integrated using Gaussian quadrature. Also, the several considerations are discussed around the Arm angle singularity, and its neighborhood.