Forward and Inverse Kinematic Analysis and Validation of the ABB IRB 140 Industrial Robot

Almaged, Mohammed

doi:10.17932/iau.ijemme.21460604.2017.7/2.1383-1401

Cited by 17 publications

(11 citation statements)

References 1 publication

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“…In the numerous surroundings and applications, ABB IRB 140 (Almaged, 2017) robot is extremely flexible. Figure 3 illustrates the ABB IRB 140 (6DOF) IRM.…”

Section: Iks Of Robotic Manipulators Based On the Hybrid Optimization...mentioning

confidence: 99%

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Singh

Banga

2022

Journal of Field Robotics

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In this paper, an optimal collision-free trajectory is developed based on the hybrid optimization algorithms for industrial robotic manipulators (IRMs). Three IRMs such as PUMA 560 (six degrees of freedom-6DOF), KUKA LBR iiwa 14 R820 (7DOF), and ABB IRB 140 (6DOF) are considered. The key objective is to enhance the smoothness and efficiency of manufacturing robots by optimum joint trajectory design using the seventh-order polynomial function. The proposed approach is to solve both kinematics and trajectory planning problems by using the different combinations of the hybrid meta-heuristic algorithms. The kinematic parameters including jerk, acceleration, and velocity mostly impact the travel smoothness of the robot end-effector on the trajectory path. Therefore, these parameters are to be constrained for generating the collision-free path. The endurance of velocity and acceleration can be obtained by reducing the jerk which leads to smooth robotic motion. The proposed work is executed using a robotic toolbox in MATLAB with a graphical user interface. The values of acceleration, velocity, and jerk are computed for the robot joints. Each robot obtained the minimum traveling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. From the experimental outcomes, the proposed scheme of the hybrid optimization algorithms is more effective for the trajectory planning of IRMs than that of other works.

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“…In the numerous surroundings and applications, ABB IRB 140 (Almaged, 2017) robot is extremely flexible. Figure 3 illustrates the ABB IRB 140 (6DOF) IRM.…”

Section: Iks Of Robotic Manipulators Based On the Hybrid Optimization...mentioning

confidence: 99%

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Singh

Banga

2022

Journal of Field Robotics

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“…In the numerous surroundings and applications, ABB IRB 140 robot [23] is extremely flexible. In the product requirement file, this robot contains different possible characteristics for example, extreme temperature surroundings, clean room categorization for the high degree of decomposition safety, foundry safety and capability to wash, and several other characteristics for applications like arc welding and gluing.…”

Section: System Model Of Abb Irb 140 (6dof)mentioning

confidence: 99%

Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators

Singh

Banga²

2022

Soft Comput

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In industrial applications and automation, the robotic manipulators exhibit a significant role. Several complex robotic systems performed a number of industrial works named spray painting, welding, assembly, pick and place action etc. The end-effector's position and the joint angles are plays a vital role since any task is activated inside the pre-defined robotic manipulator's work space. Also, the problem of trajectory planning is a very challenging task in the robotic fields.To solve these problems, this paper proposes a kinematics analysis and trajectory planning of an Industrial robotic manipulators (IRMs) based on the hybrid optimization algorithms. Here, three IRMs such as PUMA 560 (6 DOF), KUKA LBR iiwa 14 R820 (7DOF) and ABB IRB 140 (6DOF) are considered. For each robot, the forward and the inverse kinematics (IK) are analyzed and also the trajectory planning of each robot is discussed using the hybrid optimization algorithms. In this work, 18 optimization algorithms such as PSO (particle swarm optimization), SSO (social spider optimization), DFO (dragonfly optimization), BOA (butterfly optimization), CSA (crow search algorithm), BSA (bird swarm algorithm), SHO (selfish herd optimization), KHO (krill herd optimization), ALO (antlion optimization), ACO (ant colony optimization), GWO (Grey wolf optimization), GOA (grasshopper optimization), SBO (satin bowerbird optimizer), WCO (world cup optimization), COA (cuckoo optimization algorithm), CFA (cuttlefish algorithm), SOA (seagull optimization algorithm), and TSA (tunicate swarm algorithm) are utilized for both forward and inverse kinematic analysis and the trajectory planning problem. For the PUMA 560, KUKA LBR iiwa 14 R820, and ABB IRB 140 IRMs, forward kinematics (FK) are solved by the hybrid combination of PSO-SSO, SHO-KHO, and SBO-WCO individually. Also the IK are solved by the DFO-BOA, ALO-ACO and COA-CFA for each IRMs. The trajectory planning problem is solved by the CSA-BSA, GWO-GOA and SOA-TSA for each robot individually. These optimization techniques gives number of solution for kinematics and trajectory problem but it converses the best solution for the minimum multi-objective function value. The advantages of these hybrid algorithm are shorter computation time, and fewer iterations. The kinematics and trajectory analysis of each IRM is simulated using robotic tool box in MATLAB with GUI interface. For each IRM, optimized position values of the end effector, joint angles and the best optimal path are computed with minimum objective function. Finally, the performance of each robot is compared to the existing robotic works.

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“…The ABB IRB 140 robot arm (ABB Inc., Auburn Hills, MI 48326, USA), an industrial manipulator with six rotational axes, was used in the real world (Suárez and Heredia, 2013;Almaged, 2017). The example referring to the dimension, mass, and workspace of the links of the ABB IRB 140 industrial manipulator is proposed.…”

Section: Illustrative Example: System Spring Efficiency Assessment Of...mentioning

confidence: 99%

Spring efficiency assessment and efficient use of spring methods of statically balanced planar serial manipulators with revolute joints only

Juang

Jhuang²,

Chen³

2022

Mech. Sci.

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Abstract. This paper proposes a spring efficiency assessment of a statically spring-balanced planar serial manipulator. The admissible spring configurations for the static balancing of planar serial manipulators without auxiliary links have been determined in the past. Gravity is balanced by the spring configuration systematically; however, the spring configuration also contains counter-effects between springs. Conceptually, with fewer counter-effects between springs, there is less burden on the spring system, which means that the springs are used more efficiently, and accordingly, the system would be safer, and its service life would be longer. In this study, the spring energy is represented in a quadratic form. The coefficients in a quadratic form represent the change in elastic energy with the relative position between links, which is named “elastic pseudo-stiffness”. Compared to the quadratic form of gravity energy, those elastic pseudo-stiffnesses for static balancing are regarded as positive contributions of a spring, while those that contain counter-effects are seen as negative ones. Spring efficiency is defined as the ratio of the elastic pseudo-stiffnesses, which has positive contributions for balancing to total elastic pseudo-stiffnesses. To use springs efficiently, the counter-effects, which are functions of spring parameters, need to be decreased, including spring stiffness and the attachment location of springs on links. A method to use spring efficiently by adjusting spring parameters is developed. Furthermore, it is found that, for a spring attached between adjacent links, the spring efficiency is 100 %, and the spring efficiency decreases while the number of joints over which the spring spans increases. In a spring manipulator system, the efficiency is negatively correlated to the payload. As an example, an efficiency assessment on a 3 degrees of freedom (DOF) manipulator is shown at the end.

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Forward and Inverse Kinematic Analysis and Validation of the ABB IRB 140 Industrial Robot

Cited by 17 publications

References 1 publication

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators

Spring efficiency assessment and efficient use of spring methods of statically balanced planar serial manipulators with revolute joints only

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