Cartesian Position Research Articles

Collision-free optimal trajectory generation for a space robot using genetic algorithm

Future on-orbit servicing and assembly missions will require space robots capable of manoeuvring safely around their target. Several challenges arise when modelling, controlling and planning the motion of such systems, therefore, new methodologies are required. A safe approach towards the grasping point implies that the space robot must be able to use the additional degrees of freedom offered by the spacecraft base to aid the arm attain the target and avoid collisions and singularities. The controlled-floating space robot possesses this particularity of motion and will be utilised in this paper to design an optimal path generator. The path generator, based on a Genetic Algorithm, takes advantage of the dynamic coupling effect and the controlled motion of the spacecraft base to safely attain the target. It aims to minimise several objectives whilst satisfying multiple constraints. The key feature of this new path generator is that it requires only the Cartesian position of the point to grasp as an input, without prior knowledge a desired path. The results presented originate from the trajectory tracking using a nonlinear adaptive H∞ controller for the motion of the arm and its base simultaneously.

Toward Orientation Learning and Adaptation in Cartesian Space

As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. Despite recent advances in learning orientations from demonstrations, several crucial issues have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this article, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-points and end-points), where both orientation and angular velocity are considered. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations, which allows for learning orientation trajectories associated with high-dimensional inputs. In addition, we extend our approach to the learning of quaternions with angular acceleration or jerk constraints, which allows for generating smoother orientation profiles for robots. Several examples including experiments with real 7-DoF robot arms are provided to verify the effectiveness of our method.

Real-Time Thermospheric Density Estimation from Satellite Position Measurements

In this paper, a new data-driven method is demonstrated for real-time neutral density estimation via model–data fusion in quasi-physical ionosphere–thermosphere models. The proposed method has two main components: 1) the use of a quasi-physical reduced-order model (ROM) to represent the dynamics of the upper atmosphere, and 2) the calibration of the ROM coefficients using satellite position measurements. The ROM is developed using dynamic mode decomposition with control. Previous work required direct density measurements (accelerometer-derived densities), and the current work extends this approach to satellite position measurements. This work is a new approach to dynamic calibration of the atmosphere. This work proposes combining the orbit determination process with the ROM coefficient calibration through the use of the square-root unscented Kalman filter (SQUKF). The proposed SQUKF allows for new potential data sources to be incorporated into the density calibration process. This is demonstrated with simulated Global Positioning System position measurements with 5 min resolution and 10 m Cartesian position error. The proposed method is demonstrated to be simple, robust, and accurate through simulation scenarios. The proposed method can provide real-time estimates of the state of the upper atmosphere while having inherent forecasting/predictive capabilities.

Distributed Full-Consensus Control of Nonholonomic Vehicles Under Non-Differentiable Measurement Delays

We address the problem of consensus control of second-order nonholonomic systems via distributed control under the assumption that each vehicle receives measured states from a set of neighbors, with a bounded, time-varying, but non-differentiable delay. The controller that we propose guarantees full consensus, in the sense that a common consensus point may be reached both in the Cartesian positions on the plane and the orientations of all robots referred to a fixed frame. Our controller is smooth, hence time-varying. Notably, it relies on a property of persistency of excitation. Our main statement guarantees uniform global asymptotic stability. Also, we provide some simulation results to illustrate our theoretical findings.

Distributed consensus-formation of force-controlled nonholonomic robots with time-varying delays

We solve the leaderless consensus-formation control problem for nonholonomic robots under the influence of time-varying communication delays and via smooth feedback. The control objective is to ensure that both the Cartesian positions of the vehicles and their orientations on the plane converge to a given position in a formation pattern, relatively to an a priori unknown barycenter and a common orientation, respectively. Our controller, being of proportional-plus-damping type and smooth time-varying, is easy to implement. Furthermore, it relies on δ-persistency of excitation to overcome the well-known obstacle that for nonholonomic systems a set-point is not stabilizable by smooth time-invariant feedback. We establish asymptotic convergence of the tracking errors and we provide some simulation results that support our theoretical findings.

Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

Accurate and precise trajectory tracking is crucial for unmanned aerial vehicle (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) controller and Improved Particle Swarm Optimization algorithm (IPSO) model based on functional inertia weight. The controller is implemented in a three degrees of freedom (3 DOF) quadrotor symbolized with its non-linear dynamical mathematical model. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular ANFIS controller to guarantee fast convergence rapidity and IPSO aims to facilitate convergence to the ANFIS’s optimal parameters to accurately reproduce a desired reference trajectory. Simulation results are given to confirm the advantages of the proposed intelligent control, compared with ANFIS and PID control methods.

Goat Farming Development Strategy in Benjor Village, Malang Regency, Indonesia

The research aims were to determine strategies in farmers' social capital strengthening to increase trust between farmers and stakeholders, in market structure, and technical-social farmers' empowerment. The study was conducted in Benjor Village, Tumpang District, Malang Regency, and the number of respondents was 30 people. Data analysis methods used in research are SWOT analysis with IFAS (Internal Strategic Factors Analysis Summary) and EFAS (External Strategic Factors Analysis Summary) and continued with AHP (Analytic Hierarchy Process) analysis. The results of the study that the cartesian diagram position of local goat development in Benjor Village in quadrant IV, which means there are more weak points than strength points, more threat points than the opportunity points. Therefore, developing the local goat business needs to change the strategy through strengthening social capital in the trust of both other farmers with stakeholders.

FR/CoG multi-attribute-based comparison methods for selection of the location of a research institute

Purpose The determination of the appropriate site for the location of a research institute represents a multi-criteria problem which requires a scientific approach for decision-making. The research centre in this study is an institute that intends to carry out the state-of-the-art research activities and provide the requisite skills to expedite and optimize the manufacturing of rail cars in South Africa. Hence, the selection of a suitable and conducive location capable of achieving these aforementioned objectives in an effective manner is a problem which requires scientific justification for the allocation of the weights and biases. In light of this, using various decision techniques, this paper aims to establish a suitable framework for the location selection of the research institute which is capable of meeting the short- and long-term objectives of the institute. Design/methodology/approach This aim was achieved by ascertaining the suitability of potential location alternatives using the factor rating (FR) and centre of gravity (CoG) technique. Findings The CoG revealed that any location within the longitude of 28.28 and latitude of −25.75 (with a Cartesian coordinate position of 5053.62; 2718.69) is suitable for the research institute, while the result of the FR/weighted score matrix revealed that location J3 with a weighted score of 72.6% is the most suitable location for the research institute with the longitude of 5053.62 and latitude of 2718.69. Practical implications The results of this paper helped decision-makers in locating the given research institute which is currently operational. Originality/value The present study is focussed on the application of location decision techniques in the research institute scenario. The combination of FR and CoG techniques for the selection of the most suitable location for a research institute amidst conflicting criteria has not been widely reported by the existing literature.

The characteristics of final mathematics test items based on classical test theory

Final Examination is the part of an evaluation that exists in school to assess student’s knowledge. This research aimed to show the characteristic of the final mathematics test in Junior High school, grade 8. This research was an explorative descriptive using a quantitative approach. Data collection was gathered using the documentation of mathematics’ test in Binjai, North Sumatera including student’s answers. The characteristics of test items were analyzed by expert judgment using the classical test theory approach. The average of content analysis was 0.81, there were three items without key answers so it can not be analyzed. The reliability average was 0.84 which has very good quality and difficulty index indicated the difficulty level of questions was on three categories (easy, moderate, and difficult). The difficulty is in the cartesian position, the relation of two sets, determining gradient, determining the solution of linear equation systems of two variables.

Velocity-independent Marchenko focusing in time- and depth-imaging domains for media with mild lateral heterogeneity

The Marchenko method retrieves Green’s functions between the acquisition surface and any arbitrary point in the medium. The process generally involves solving an inversion starting with an initial focusing function, e.g., a direct-wave Green’s function from the desired subsurface position, typically obtained using an approximate velocity model. We have formulated the Marchenko method in the time-imaging domain. In that domain, we recognize that the traveltime of the direct-wave Green’s function is related to the Cheop’s traveltime pyramid commonly used in time-domain processing, which in turn can be readily obtained from the local slopes of the common-midpoint gathers. This observation allows us to substitute the velocity-model-based initial focusing operator with that from a data-driven slope estimation process. Moreover, we found that working in the time-imaging domain allows for the specification of the desired subsurface position in terms of vertical time, which is connected to the Cartesian depth position via the time-to-depth conversion. Our results suggest that the prior velocity model is only required when specifying the position in depth, but this requirement can be circumvented by making use of the time-imaging domain within its usual assumptions (e.g., mild lateral heterogeneity). Provided that those assumptions are satisfied, the estimated Green’s functions from the proposed method have comparable quality to those obtained with the knowledge of a prior velocity model.

Application of MCNP for determining the distribution of absorbed dose in lung brachytherapy by using radiation γ 131cs

One of the MCNP applications is to determine the radiation dose distribution in the brachytherapy process. In this research performed the calculation of the distribution of radiation doses in the lung organ and some organs at risk, so it can be known whether the doses received by the surrounding organs are still within normal limits or not. The American Association of Physicists in Medicine (AAPM) recommends that the doses administered in patient radiotherapy have inaccuracies that are allowed to be within the range of ± 5 %. This simulation is used to determine the energy of γ-radiation absorbed per particle transformation in the lung organ and other organs around it. The radioactive source used is 131Cs in the form of points with activity varying from 1.7 mCi to 2.3 mCi, and the number of seeds grown varies from 10 to 60. The simulation of the geometry of the human body is made up of phantom ORNL-MIRD. This research made left lung geometry as target organ with density 0,296 g / cm3 and located at a Cartesian coordinate position that is (8,5; 0; 43,5). From the absorbed dose can be calculated dose distribution on each organ including the target organ and the surrounding organ. If the target organ is considered to receive a dose of 100%, then the surrounding organs are left an atrial wall of 59.58%, left atrium content of 14.32%, left a ventricular wall of 11.62%, left the ventricular content of 15.5 % and right lung by 0.17%. From the results of the dose distribution can be said that the organs that exist around the target organs are still safe because receiving a radiation dose is still below 60%.

Stable Torque Optimization for Redundant Robots Using a Short Preview

We consider the known phenomenon of torque oscillations and motion instabilities that occur in redundant robots during the execution of sufficiently long Cartesian trajectories when the joint torque is instantaneously minimized. In the framework of on-line local redundancy resolution methods, we propose basic variations of the minimum torque scheme to address this issue. Either the joint torque norm is minimized over two successive discrete-time samples using a short preview window, or we minimize the norm of the difference with respect to a desired momentum-damping joint torque, or the two schemes are combined together. The resulting local control methods are all formulated as well-posed linear-quadratic problems, and their closed-form solutions also generate low joint velocities while addressing the primary torque optimization objectives. Stable and consistent behaviors are obtained along short or long Cartesian position trajectories, as illustrated with simulations on a 3R planar arm and with experiments on a 7R KUKA LWR robot.

Consensus of Multi-Agent Systems With Nonholonomic Restrictions via Lyapunov’s Direct Method

This letter presents a smooth time-varying $\delta $ -persistently exciting controller for full consensus of autonomous nonholonomic vehicles modeled as unicycles. This consists in the robots assuming a common prescribed Cartesian position relative to an unknown barycentre and an unknown common orientation. More significantly, for the first time in the literature, a strict Lyapunov function is provided and uniform global asymptotic stability for the closed-loop system is established. This is well beyond weaker convergence properties that are more commonly guaranteed in the literature.

Dynamic Control of Parallel Robots Driven by Flexible Cables and Actuated by Position-Controlled Winches

An alternative approach to standard computed torque with feedback linearization is proposed in this paper to control cable-driven parallel robots (CDPRs) with highly flexible cables. Exteroceptive feedback is used to measure the end-effector Cartesian position at a high sampling rate. Stability is demonstrated using singular perturbation theory. The proposed control scheme is experimentally validated on a planar 3-degree-of-freedom CDPR and its efficiency is assessed by comparison with a simple kinematic control law.

Deep 3D perception of people and their mobility aids

Robots operating in populated environments, such as hospitals, office environments or airports, encounter a large variety of people with some of them having an advanced need for cautious interaction because of their advanced age or motion impairments. To provide appropriate assistance and support robot helpers require the ability to recognize people and their potential requirements. In this article, we present a people detection framework that distinguishes people according to the mobility aids they use. Our framework uses a deep convolutional neural network for detecting people in image data. For human-aware robots it is necessary to know where people are in a 3D world reference frame instead of only locating them in a 2D image, therefore we add a 3D centroid regression output to the network to predict the Cartesian position of people. We further use a probabilistic class, position and velocity tracker to account for false detections and occlusions. Our framework comes in two variants: The depth only variant targets high privacy demands, while the RGB only framework provides improved detection performance for non-critical applications. Both variants do not require additional geometric information about the environment. We demonstrate our approach using a dedicated dataset acquired with the support of a mobile robotic platform. The dataset contains five classes: pedestrian, person in wheelchair, pedestrian pushing a person in a wheelchair, person using crutches and person using a walking frame. Our framework achieves an mAP of 0.87 for RGB and 0.79 for depth images at a detection distance threshold of 0.5m on our dataset, with a runtime of 53ms per image. The annotated dataset is publicly available and our framework is made open source as a ROS people detector.

Perturbation solutions to the nonlinear motion of displaced orbits

This paper analytically describes the nonlinear motion of displaced orbits obtained by low-thrust propulsion, and focuses on the first-order analytical derivation of perturbation solutions of osculating Keplerian element (OKE) time histories. A virtual Earth (VE) model is developed to transform non-Keplerian orbits above the Earth into Keplerian orbits around the VE by treating the thrust and coordinate transformation effects as perturbations. The numerical OKEs computed from the Cartesian position and velocity exhibit secular and periodic variations. The short-period perturbations of the orbital elements are derived by the conventional quasi-mean element method. The differential equations of the secular and long-period perturbations are linearized for an analytical solution. A comparison between the analytical OKEs and numerical OKEs of a specific displaced orbit validates the correctness and good accuracy of the analytical derivation. One contribution of this paper is that the analytical OKEs can be applied for a fast calculation of a displaced orbit in the preliminary mission design phase, which has more significance than accurate but complicated time-consuming calculations by complete variational equations. Another contribution is revealing the connections between the amplitude as well as frequency of perturbation solutions and displaced orbits from a physical perspective, from which the topology and geometrical size of a displaced orbit can be easily inferred from the time history of OKEs.

Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion

In this paper, a neural network (NN) based adaptive controller has been successfully developed for the teleoperation of a DLR-HIT II robot hand using Leap Motion sensor. To achieve this, the coordinate positions of the human hand fingers are captured by a Leap Motion sensor. Moreover, inverse kinematics is used to transform the Cartesian position data of the fingertips into corresponding joint angles of all the five fingers, which are then directly sent to teleoperate the DLR-HIT II hand via User Datagram Protocol (UDP). In addition, a NN-based control system programmed by the MATLAB Simulink function has been investigated to enhance the overall control performance by compensating the dynamic uncertainties existing in the teleoperation system. The stability of the control system has been proved mathematically using Lyapunov stability equations. A series of experiments have been conducted to test the performance of the proposed control technique, which has been proved to be an effective teleoperation strategy for the DLR-HIT II robot hand.

Force feasible set prediction with artificial neural network and musculoskeletal model

Developing tools to predict the force capabilities of the human limbs through the Force Feasible Set (FFS) may be of great interest for robotic assisted rehabilitation and digital human modelling for ergonomics. Indeed, it could help to refine rehabilitation programs for active participation during exercise therapy and to prevent musculoskeletal disorders. In this framework, the purpose of this study is to use artificial neural networks (ANN) to predict the FFS of the upper-limb based on joint centre Cartesian positions and anthropometric data. Seventeen right upper-limb musculoskeletal models based on individual anthropometric data are created. For each musculoskeletal model, the FFS is computed for 8428 different postures. For any combination of force direction and joint positions, ANNs can predict the FFS with high values of coefficient of determination (R2 > 0.89) between the true and predicted data.

Perceptual proxies for extracting averages in data visualizations.

Across science, education, and business, we process and communicate data visually. One bedrock finding in data visualization research is a hierarchy of precision for perceptual encodings of data (e.g., that encoding data with Cartesian positions allows more precise comparisons than encoding with sizes). But this hierarchy has only been tested for single-value comparisons, under the assumption that those lessons would extrapolate to multivalue comparisons. We show that when comparing averages across multiple data points, even for pairs of data points, these differences vanish. Viewers instead compare values using surprisingly primitive perceptual cues (e.g., the summed area of bars in a bar graph). These results highlight a critical need to study a broader constellation of visual cues that mediate the patterns that we can see in data, across visualization types and tasks.

Implementation of Two-Axis Position-Based Impedance Control with Inverse Kinematics Solution for A 2-DOF Robotic Finger

Position-based impedance control is a force control approach which consists of a single control law that accommodates the external force to achieve the desired dynamics of the body. A previously developed three-fingered robot hand was very rigid in its motion due to the application of position control alone. The position control scheme was inadequate for the tasks that involves the interaction of a robot end-effector with its environment which could damage fragile objects or be prone to slippage when provided with incorrect object's position. This paper introduces the application of two-axis position-based impedance control to one of the 2 degree-of-freedom (DOF) robotic finger of the robot hand. The goal of the control is to produce a mass-spring-dashpot system for the robot hand which considers the external force exerted by the object or environment onto the finger to modify the targeted position of the robot's tip-end. The position-based impedance control which was successfully performed however could not directly drive the DC-micromotors at the finger joints since it was expressed in the Cartesian position (X,Y,Z) form. Therefore, inverse kinematics was derived using geometrical approach to convert the Cartesian position (X,Y,Z) to angle position of motor which is controlled by PID. The proposed control and the developed kinematics were programmed using Matlab Simulink and tested in real-time experiments. The validation result has proven that the proposed position-based impedance control could modify the initial fingertip position according to the amount and direction of the applied external force, thus produced softness to the robotic finger.