Kinematic Task Research Articles

Multi-AUV Kinematic Task Assignment Based on Self-Organizing Map Neural Network and Dubins Path Generator.

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV's yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.

A Method for Simulating the Positioning Errors of a Robot Gripper

The research is aimed at creating a methodology for increasing the positioning accuracy of an industrial robot and minimizing the vibration of the robot gripper by applying machine learning based on the developed mathematical model for estimating the positioning error. Two components of positioning accuracy are considered: geometric and kinematic errors and elastic static deformations. The dynamic error in the partial system of motion of the robot manipulator links is analyzed. The equation of partial motions is obtained from Lagrange’s differential equation of motion of the II kind. The system of differential equations for the positioning error was solved analytically by Euler’s method. An example of modeling the position and orientation error of the gripper due to temperature deformations of the third link for the manipulator scheme is given. An example of the modeling of static deformations and errors of the manipulator with elastic pliability of the robot links is given. An example of dynamic error modeling in a partial system of motion of the robot links is given. The proposed method of modeling robot gripper positioning errors makes it possible to increase the positioning accuracy of the industrial robot and minimize the vibration of the gripper. Having a mathematical model of positioning errors, it is possible to compensate for the positioning error by changing the speed of movement of the gripper reference point before determining the direct kinematic task.

EXPLORATION OF THE INVERSE AND FORWARD KINEMATICS OF A TWO-LINK ROBOT ARM USING MATLAB

This contribution focuses on solving the inverse and forward kinematics in the kinematic analysis of a two-link manipulator model. It involves determining the trajectory of the end effector through fifth-degree polynomial interpolation. Furthermore, given initial and final arm angle values, it computes not only the end effector trajectory but also the angular parameters of individual actuators in both arms. Additionally, it calculates the variations of angular parameters such as rotation angle, angular velocity, and angular acceleration in each kinematic pair. The direct kinematics task is utilized to define the manipulator's workspace. The task is carried out using Matlab software, and the results are presented in the form of graphs and tables.

Persistent avoidance of virtual food in anorexia nervosa-restrictive type: Results from motion tracking in a virtual stopping task.

Food avoidance is central to patients with anorexia nervosa-restrictive type (AN-R). Competing accounts in experimental psychopathology research suggest that food avoidance may result from automatic, habitual responses or from elevated inhibitory control abilities. This study investigated behavioral trajectories of food avoidance in a novel virtual reality stopping task. Sixty patients with AN-R and 29 healthy controls with normal weight were investigated using a novel, kinematic task in virtual reality. We recorded spatial displacement in stop- and go-trials to virtual food and control objects. Inhibitory control abilities were operationalized by the VR task in stopping performance (i.e., interrupted movement in stop-trials), whereas we also measured habitual avoidance of virtual food across both go- and stop-trials (i.e., delayed movement relative to nonfood objects). In patients with AN-R, hand displacements were shorter to food versus nonfood across stop- and go-trials, reflected in a Stimulus × Group interaction. Healthy controls showed no differences. Importantly, the food-specific effect in AN-R was identical across stop- and go-trials, indicating habitual food avoidance. Moreover, stop error rates (i.e., stop-trials with response) were lower in patients with AN-R. The findings suggest food-specific habitual avoidance and heightened generalized inhibitory control in AN-R. The continuously delayed displacements during active hand movements across stop- and go-trials indicated the persistence of patients' avoidance of food. Experimental research investigates the mechanisms underlying mental disorders such as anorexia nervosa. In this study, we measured interrupted hand movements in response to food pictures or neutral pictures (shoes) in patients with anorexia nervosa and healthy controls. A virtual reality scenario was used. Findings indicated that patients were slower at approaching food, interrupted or not. Key mechanisms of food avoidance can be translated into habit-based treatment options in future research.

Kinematic Modeling of a Trepanation Surgical Robot System

This paper presents the concept of a parallel medical robotic service system to assist in a surgical procedure involving precise exploratory trepanation holes in a patient’s skull. The target position and orientation of the trepanation tool in the cranial region is determined using a prior intracranial image analysis using an external medical imaging system. A trepanning actuation system is attached to the end-effector of the parallel robot. The end-effector will act as an accurate positioner for the trepanning drill in the medical intervention area. The conceptual design of the mechanical actuation subsystem of a trepanning robot was developed in the SolidWorks 2022 software environment. The virtual model of the kinematic chain of the robot and the assumed design parameters were used to analytically derive the equations describing the inverse kinematics task. An analysis of the forward kinematics task of the parallel manipulator was also carried out using analytical and numerical methods. A workspace analysis was performed using Matlab based on the kinematic model of the parallel robot. This paper significantly advances the field by presenting the conceptual design of the actuation subsystem, deriving the kinematics equations, conducting a thorough workspace analysis, and establishing a foundation for subsequent control-algorithm development.

Robust Multilegged Walking Robots for Interactions With Different Terrains

Abstract This paper explores the kinematic synthesis, design, and pilot experimental testing of a six-legged walking robotic platform able to traverse through different terrains. We aim to develop a structured approach to designing the limb morphology using a relaxed kinematic task with incorporated conditions on foot-environments interaction, specifically contact force direction and curvature constraints, related to maintaining contact. The design approach builds up incrementally starting with studying the basic human leg walking trajectory and then defining a “relaxed” kinematic task. The “relaxed” kinematic task consists only of two contact locations (toe-off and heel-strike) with higher-order motion task specifications compatible with foot-terrain(s) contact and curvature constraints in the vicinity of the two contacts. As the next step, an eight-bar leg image is created based on the “relaxed” kinematic task and incorporated within a six-legged walking robot. Pilot experimental tests explore if the proposed approach results in an adaptable behavior which allows the platform to incorporate different walking foot trajectories and gait styles coupled to each environment. The results suggest that the proposed “relaxed” higher-order motion task combined with the leg morphological properties and feet material allowed the platform to walk stably on the different terrains. Here we would like to note that one of the main advantages of the proposed method in comparison with other existing walking platforms is that the proposed robotic platform has carefully designed limb morphology with incorporated conditions on foot-environment interaction. Additionally, while most of the existing multilegged platforms incorporate one actuator per leg, or per joint, our goal is to explore the possibility of using a single actuator to drive all six legs of the platform. This is a critical step which opens the door for the development of future transformative technology that is largely independent of human control and able to learn about the environment through their own sensory systems.

A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks.

The purpose of this work was to develop an open-source musculoskeletal model of the hand and wrist and to evaluate its performance during simulations of functional tasks. The current model was developed by adapting and expanding upon existing models. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization was used to simulate maximum grip and pinch force. Active and passive hand opening were simulated to evaluate coordinated kinematic hand movements. The model's maximum grip force production matched experimental measures of grip force, force distribution amongst the digits, and displayed sensitivity to wrist flexion. Simulated lateral pinch strength replicated in vivo palmar pinch strength data. Additionally, predicted activations for 7 of 8 muscles fell within variability of EMG data during palmar pinch. The active and passive hand opening simulations predicted reasonable activations and demonstrated passive motion mimicking tenodesis, respectively. This work advances simulation capabilities of hand and wrist models and provides a foundation for future work to build upon. This is the first open-source musculoskeletal model of the hand and wrist to be implemented during both functional kinetic and kinematic tasks. We provide a novel simulation framework to predict maximal grip and pinch force which can be used to evaluate how potential surgical and rehabilitation interventions influence these functional outcomes while requiring minimal experimental data.

High-level motor planning allows flexible walking at different gait patterns in a neuromechanical model.

Humans can freely adopt gait parameters like walking speed, step length, or cadence on the fly when walking. Planned movement that can be updated online to account for changes in the environment rather than having to rely on habitual, reflexive control that is adapted over long timescales. Here we present a neuromechanical model that accounts for this flexibility by combining movement goals and motor plans on a kinematic task level with low-level spinal feedback loops. We show that the model can walk at a wide range of different gait patterns by choosing a small number of high-level control parameters representing a movement goal. A larger number of parameters governing the low-level reflex loops in the spinal cord, on the other hand, remain fixed. We also show that the model can generalize the learned behavior by recombining the high-level control parameters and walk with gait patterns that it had not encountered before. Furthermore, the model can transition between different gaits without the loss of balance by switching to a new set of control parameters in real time.

Playing with temptation: Stopping abilities to chocolate are superior, but also more extensive

Cue-specific inhibitory control is assumed to support balanced food intake, but previous studies with established measures showed inconsistent results. We developed a novel kinematic stop task in virtual reality (VR) and report results from trajectory recordings. The primary objective of this explorative study was to assess the interrelationships between validated measures of food-related inhibitory control and novel measures from the VR task. We hypothesized that healthy female participants show worse inhibitory control when grasping attractive virtual chocolate, compared to non-edible color-and-shape matched objects. We further aimed to quantify the construct validity of kinematic measures (e.g., reaching extent/spatial displacement, movement time after stop-signal, velocity) with established measures of inhibitory control in a keyboard-based adaptive stop-signal task (SST). In total, 79 females with varying levels of chocolate craving participated in an experimental study consisting of self-report questionnaires, subjective chocolate craving, the conventional SST and the kinematic task in VR. Results showed superior stopping ability to chocolate in both tasks. In VR, participants successfully interrupted an initiated approach trajectory but terminated slightly closer to chocolate targets. Stop-signal delay (SSD) was adapted relative to movement onset and appeared later in chocolate trials, during which participants still stopped faster, as was also confirmed by shorter stop-signal reaction time (SSRT) in the conventional task. Yet, SSRT did not correlate with stopping in VR. Moreover, SSRT was related to depressive symptoms whereas measures from VR were related to chocolate craving and subjective hunger. Thus, VR stopping can provide deeper insights into healthy weight individuals’ capacity to inhibit cue-specific approach behavior towards appetitive stimuli in simulated interactions. Furthermore, the results support a multi-faceted view of food-specific inhibitory control and behavioral impulsivity.

DESIGN OF AN AUTOMATED CONTROL SYSTEM OF A DELTA MANIPULATOR WITH MICROCONTROLLER BASED MACHINE VISION

The design and implementation of a microcontroller vision-based manipulator control system is described. The control system is divided into high and low-level loops. As high-level devices computers or minicomputers have been used when conducting most research. In this work, a microcontroller board has been used as a high-level device due to its mobility and efficiency. A solution to the inverse kinematics task of a delta manipulator is presented in this work which forms the main part of the low-level device algorithm. Sipeed Maixduino and Arduino Mega dev boards have been used to implement the control system. The objects have been classified by YOLO model for the testing purposes of the control system. This has made it possible to measure the average targeting time for the different objects, which is used to test the implemented control system. The empirical data has shown a suboptimal result. The reason for it has been analyzed and addressed through coding a more optimal solution. Potential improvements to the hardware used in the test setup are suggested to benefit future work.

Detecting Parkinson’s disease through an upper limb reaching task and a machine learning approach

Among the neurodegenerative diseases, Parkinson’s disease (PD) is the most common movement disorder. In the literature, different approaches to analyse both upper and lower limbs motion patterns have been proposed [1,2]. Referring to our previous work [3], we demonstrated, after a statistical analysis, several features, extracted from upper limb reaching movements acquired through goniometric sensors, confirmed helpful in discriminating healthy controls and PD patients. Following these encouraging results, in this work we explored the possibility of using such features as input to two different machine learning (ML) algorithms and assessed their capability to distinguish healthy controls and PD patients. Twelve subjects (six health controls and six PD patients), who were admitted at the Institute of Care and Scientific Research of Telese Terme of ICS Maugeri SPA SB (Telese Terme, Italy), performed a kinematic task – made up of four movements through the upper limb – twice. The signals of such movements (consisting in an angular displacement) were acquired through a goniometric sensor. Later, thirteen statistical features were extracted through the custom-made software described in [3]. They were given as input to J48 and K Nearest Neighbour (KNN) and a leave one out cross-validation was implemented. Algorithms performance was assessed in terms of accuracy, sensitivity, specificity and Area Under the Curve Receiver Operating Characteristics (AUC-ROC). The classification algorithms achieved promising results, with metrics that overcame the value of 90%. The tree-based algorithm J48 achieved higher accuracy and sensitivity (91.7% and 100% respectively), while KNN achieved the highest specificity and AUC-ROC (91.7% and 0.94). Table 1 summarizes the results achieved by the algorithms. This paper answers to the need of defining measurable, repeatable, and reliable indicators to monitor the effectiveness of a training program over time; moreover, the paper provides a quantitative measure of the rehabilitation outcome, which is a necessary step towards the design of personalized treatment programs. The results obtained in this investigative research are promising and allow us to think about additional ML algorithms to be considered in further studies and to enlarge the ML approach by considering/investigating additional features to extract.

A Simultaneous Learning and Control Scheme for Redundant Manipulators With Physical Constraints on Decision Variable and Its Derivative

In this article, a simultaneous learning and control scheme built on the joint velocity level with physical constraints on the decision variable and its derivative, i.e., joint angle, joint velocity, and joint acceleration constraints, is proposed for the redundant manipulator control. The scheme works when the structure parameters involved in the forward kinematics are unknown or implicit. The learning and control parts are incorporated simultaneously in the scheme, which is finally formulated as a quadratic programming problem solved by a devised recurrent neural network (RNN). The convergences of learning and control abilities of the RNN are proved theoretically. Simulations and physical experiments on a 7-degrees of freedom (DOFs) redundant manipulator show that, aided with the proposed scheme and the related RNN solver, a redundant manipulator with unknown structure parameters can perform a given inverse kinematics task with high accuracy while satisfying physical constraints on the decision variable and its derivative.

Optimal Kinematic Task Position Determination—Application and Experimental Verification for the UR-5 Manipulator

A method for determining the optimal position of a robotic task within a manipulator’s workspace considering the minimum singularity free paths in joint space in order to achieve a high kinematic performance is presented. The selected performance criterion was the minimization of the joint velocities during task execution under a given end effector velocity. The proposed method is applied to place kinematic tasks for a UR-5 manipulator. Joint speed measurements are compared for the optimal and the “bad” task positions and the results show that at the optimal position, lower joint speeds are exerted during task execution.

Inverse Kinematics Analysis of Novel 6-Dof Robotic Arm Manipulator for Oil and Gas Welding Using Grey Wolf Algorithm

his paper presents a grey-wolf algorithm for solving the inverse kinematics of a newly designed 6-degree-of-freedom robotic arm for oil and gas pipeline welding which has not been used in the literature. Consequently, due to the robot’s multiple joints with compounding combinatory possibilities of joint angles, analysis of the inverse kinematics is relatively complex. In this research, grey-wolf algorithm, a swarm-based meta-heuristic algorithm, has been used to solve for the inverse kinematics of the robotic arm with respect to tracking a rectangular trajectory with six sets of waypoints in the 3D [X, Y, Z] space. The results were further analyzed in terms of the accuracy of the position of end effector from the accurate position of the rectangular target trajectory via a mean squared error cost function. Furthermore, results of comparison between the grey-wolf algorithm and the particle swarm optimisation, an alternate swarm algorithm with respect to position error from the inverse kinematics task is also presented. The results obtained via simulation clearly demonstrates the superior performance of the grey-wolf algorithm compared to particle swarm optimisation with respect to the solving an inverse kinematics task

Effect of epidural spinal cord stimulation after chronic spinal cord injury on volitional movement and cardiovascular function: study protocol for the phase II open label controlled E-STAND trial

IntroductionSpinal cord injury (SCI) leads to significant changes in morbidity, mortality and quality of life (QOL). Currently, there are no effective therapies to restore function after chronic SCI. Preliminary studies...

Accelerated Reduced Gradient Algorithm with Constraint Relaxation in Differential Inverse Kinematics

The Moore-Penrose pseudoinverse-based solution of the differential inverse kinematic task of redundant robots corresponds to the result of a particular optimization underconstraints in which the implementation of Lagrange’s ReducedGradient Algorithm can be evaded simply by considering the zero partial derivatives of the ”Auxiliary Function” associated with this problem. This possibility arises because of the fact that the cost term is built up of quadratic functions of the variable of optimization while the constraint term is linear function of the same variables. Any modification in the cost and/or constraint structure makes it necessary the use of the numerical algorithm. Anyway, the penalty effect of the cost terms is always overridden by the hard constraints that makes practical problems in the vicinity of kinematic singularities where the possible solution stillexists but needs huge joint coordinate time-derivatives. While in the special case the pseudoinverse simply can be deformed, inthe more general one more sophisticated constraint relaxation can be applied. In this paper a formerly proposed acceleratedtreatment of the constraint terms is further developed by the introduction of a simple constraint relaxation. Furthermore, thenumerical results of the algorithm are smoothed by a third order tracking strategy to obtain dynamically implementable solution.The improved method’s operation is exemplified by computation results for a 7 degree of freedom open kinematic chain

Sub-optimal Solution of the Inverse Kinematic Task of Redundant Robots without Using Lagrange Multipliers

In the paper a novel approach is suggested for solving the inverse kinematic task of redundant open kinematic chains. Traditional approaches as the Moore-Penrose generalized inverse-based solutions minimize the sum of squares of the timederivative of the joint coordinates under the constraint that contains the task itself. In the vicinity of kinematic singularities where these solutions are possible the hard constraint terms produce high time-derivatives that can be reduced by the use of a deformation proposed by Levenberg and Marquardt. The novel approach uses the basic scheme of the Receding Horizon Controllers in which the Lagrange multipliers are eliminated by direct application of the kinematic model over the horizon in the role of the ”control force”, and no reduced gradient has to be computed. This fact considerably decreases the complexity of the solution. If the cost function contains penalty for high joint coordinate time-derivatives the kinematic singularities are ab ovo better handled. Simulation examples made for a 7 degree of freedom robot arm demonstrate the operation of the novel approach. The computational need of the method is still considerable but it can be further decreased by the application of complementary tricks.

PROSPECTS AND PROBLEMS OF ROBOTIC DELIVERY OF THE LAST MILE. THE RELEVANCE OF THE PROBLEM AND THE CONCEPT OF THE SOLUTION. PART I.

The express delivery market in recent years has been growing at the level of 3-4%, and even in these conditions, not only is it not saturated, but the demand for it is growing. According to Oxford Economics, the growth of the air cargo market, which determines the volume of the express delivery market, accelerated at times up to 7% per year from 2013 to 2018 [1].
 The biggest changes took place in 2016-17 due to a technological breakthrough in the field of logistics with the introduction of services such as drone delivery, processing orders on the blockchain, calculation of the delivery mode using artificial intelligence, etc. It was expected that due to the growing demand on fast delivery guaranteed, the number of express delivery employees worldwide will grow to 4.5 million over the next few years. But the coronavirus pandemic has accelerated this process.
 In the study “The Future of Freight Transportation. How new technologies and new thinking can change the movement of goods”, presented by the international network of consulting companies Deloitte in 2017, states that carriers have already solved many of the problems associated with the transportation of goods. But the “last mile delivery” stage has remained limiting the development of the delivery service. At this stage, companies suffer losses due to the concentration of logistics, algorithmic and kinematic tasks that cannot be automated with modern means and technologies for replacing human labor.
 Consequently, the use of alternative, unconventional technologies at this stage is a key condition for the mass development of delivery.

Accuracy and precision: A new view on kinematic assessment of solid-state hinges and compliant mechanisms

Compliant mechanisms are alternatives to conventional mechanisms which exploit elastic strain to produce desired deformations instead of using moveable parts. They are designed for a kinematic task (providing desired deformations) but do not possess a kinematics in the strict sense. This leads to difficulties while assessing the quality of a compliant mechanism’s design. The kinematics of a compliant mechanism can be seen as a fuzzy property. There is no unique kinematics, since every deformation need a particular force system to act; however, certain deformations are easier to obtain than others. A parallel can be made with measurement theory: the measured value of a quantity is not unique, but exists as statistic distribution of measures. A representative measure of this distribution can be chosen to evaluate how far the measures divert from a reference value. Based on this analogy, the concept of accuracy and precision of compliant systems are introduced and discussed in this paper. A quantitative determination of these qualities based on the eigenvalue analysis of the hinge’s stiffness is proposed. This new approach is capable of removing most of the ambiguities included in the state-of-the-art assessment criteria (usually based on the concepts of path deviation and parasitic motion).

Development of Task-Space Nonholonomic Motion Planning Algorithm Based on Lie-Algebraic Method

In this paper, a Lie-algebraic nonholonomic motion planning technique, originally designed to work in a configuration space, was extended to plan a motion within a task-space resulting from an output function considered. In both planning spaces, a generalized Campbell–Baker–Hausdorff–Dynkin formula was utilized to transform a motion planning into an inverse kinematic task known for serial manipulators. A complete, general-purpose Lie-algebraic algorithm is provided for a local motion planning of nonholonomic systems with or without output functions. Similarities and differences in motion planning within configuration and task spaces were highlighted. It appears that motion planning in a task-space can simplify a planning task and also gives an opportunity to optimize a motion of nonholonomic systems. Unfortunately, in this planning there is no way to avoid working in a configuration space. The auxiliary objective of the paper is to verify, through simulations, an impact of initial parameters on the efficiency of the planning algorithm, and to provide some hints on how to set the parameters correctly.