Inclusion of Bidirectional Angular Positioning Deviations in the Kinematic Model of a Six-DOF Articulated Robot for Static Volumetric Error Compensation

Alam, Md Moktadir; Ibaraki, Soichi; Fukuda, Koki; Morita, Soichiro; Usuki, Hiroshi; Otsuki, Naohiro; Yoshioka, Hirotaka

doi:10.1109/tmech.2022.3156056

Cited by 17 publications

(6 citation statements)

References 34 publications

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“…The experiment is set up so that one axis rotates in the robot's C-space while the other rotating axes remain fixed or at the zero angular position. In the literature, this type of experiment is referred to as the axis indexing test [9]. For example, when the A1 axis of the robot in Figure 1 rotates from t0 radians (rad) to 1 rad with an interval of supposing 0.1 rad while the A2-axis to A6-axis is kept at constant (0 rad), the experiment is defined as A1 axis indexing test.…”

Section: Methodology Of the Experimentsmentioning

confidence: 99%

“…Thus, a method is devised that combines the guidance of the flight cost function and flight constraints to discover an optimal path with the shortest length that satisfies both optimality and requirements. In the experiment, the power consumption for the rotary axis is examined by conducting the axis indexing test, as defined in the literature [9,40]. As demonstrated by the researchers, the axis indexing test, based on the "circle point method" has inherent effects on the end effector positions of the robot due to changes in joint axis angles [8].…”

Section: Related Work and Our Approach To Robot Motion Planningmentioning

confidence: 99%

“…An indexing test is used to identify the energy consumption for each rotary axis using different motion planning algorithms rather than not finding the optimal movement. Ibaraki et al [8] and Alam et al [9] demonstrated that axis indexing tests ensure that the robot can accurately reach and maintain positions, improving the precision of its operations as well as the robot's ability to consistently return to the same position when commanded, which is critical for tasks requiring repeatability. By performing an axis indexing test, it can identify and quantify any systematic errors or misalignments in the robot's rotary axes, allowing for corrective actions so that robots can operate more efficiently, reducing cycle times and improving productivity.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Alam,

Nishi,

Liu

et al. 2023

Energies

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Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

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Section: Methodology Of the Experimentsmentioning

confidence: 99%

Section: Related Work and Our Approach To Robot Motion Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Alam,

Nishi,

Liu

et al. 2023

Energies

Self Cite

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“…If the base segment is placed such that the robot frequently needs to extend its arm to the maximum reach or move through a larger section of its range of motion to accomplish tasks, it consumes more energy. Thus, the intelligent placement of the robot base segment can be modelled as an optimization problem [44][45][46] where: The idea is to link the decisional variables to the objective function through a set of decisional expressions represented by the robot inverse kinematic (IK) model [47][48][49][50]:…”

Section: Energy-efficient Design Of Robotized Manufacturing Cellsmentioning

confidence: 99%

Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design

Răileanu,

Borangiu,

Lențoiu

et al. 2024

Sustainability

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The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered in this research. An energy-monitoring framework set up by joint velocities provides input data that are fed to the optimization model. The physical placement of the robot base stands for the decisional variables, while the objective function is represented by the total distance covered by individual joints along established task routes transposed into energy consumption. The values of the decisional variables are restricted by trajectory constraints (waypoints on paths), joint operating values and link dimensions. Modelling technique and practical results using the Microsoft Solver optimization tool from Excel for Microsoft 365, Version 2312 are reported for SCARA-type robots. The performance of the optimization model is compared with actual measurements of consumed energy on an Adept Cobra S600 SCARA robot.

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“…Separately, the accuracy of different measurement tools is an essential factor impacting the effectiveness of kinematic calibration. Therefore, high-precision and high-resolution measurement tools such as laser trackers [22, 23] and monocular cameras to the systematic error caused by these approaches to data measurement, which can weaken the overall [24, 25], are typically selected for data measurement. In fact, few scholars have paid much attention to the systematic error caused by these approaches to data measurement, which can weaken the overall precision control of robot motion.…”

Section: Introductionmentioning

confidence: 99%

Kinematic calibration and feedforward control of a heavy-load manipulator using parameters optimization by an ant colony algorithm

Wang,

Xie,

Jiang

et al. 2024

Robotica

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Most of the currently available three-degree-of-freedom manipulators are light load and cannot achieve full continuous rotation; given this, we designed a heavy-load manipulator that achieves unrestricted and continuous rotation. Due to manufacturing and assembly errors, parameter deviations between the real manipulator and its underlying theoretical model were unavoidable. Because of the lack of high-precision, high-frequency, and real-time closed-loop detection methods, we proposed a type of kinematics calibration of parameterized ant colony optimization and feedforward control methods. This was done to achieve high-precision motion control. First, an error model combining structural parameters and joint output angles was established, and the global sensitivity of each error source was analyzed to distinguish both primary and secondary sources. Based on the measured data of a laser tracker, the ant colony optimization was then used to identify six error sources. This resulted in both link length and joint driving errors of the designed manipulator. As it is a type of systematic error, the rounding error of the theoretical trajectory was carefully analyzed, and feedforward control methods with different coefficients were designed to further improve positioning accuracy based on the kinematic calibration. Experimental results showed that the proposed kinematic calibration and feedforward control methods achieved relatively precise motion control for the designed manipulator.

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Inclusion of Bidirectional Angular Positioning Deviations in the Kinematic Model of a Six-DOF Articulated Robot for Static Volumetric Error Compensation

Abstract: His research interests include the machining of difficultto-cut materials, high speed machining, environmental friendly machining, damage mechanism of coated tool and development of newly tools, and generation mechanism of Belag film etc..

Cited by 17 publications

References 34 publications

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design

Kinematic calibration and feedforward control of a heavy-load manipulator using parameters optimization by an ant colony algorithm

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