Smooth and Time Optimal Trajectory Planning for Industrial Robot Using a Single Polynomial

Nguyen, Minh-Tuan; Huang, Jin-Huang

doi:10.1007/978-3-030-37497-6_76

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…A smooth and time based optimal trajectory planning using a single polynomial for industrial robots was presented by Nguyen and Huang in [18]. It performs two different function for trajectory planning they are smoothing and optimizing.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…A horizontal and time-based ideal trajectory planning for industrial robots was presented by Nguyen and Huang (2019) by means of a distinct polynomial. It performs two different functions for trajectory planning and they are smoothing and optimizing.…”

Section: Recent Related Work Are Given Belowmentioning

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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Time-optimal and Smooth Trajectory Planning for Multi-axis Motion Systems Based on ISC Similarity

Zhang,

Cheng,

Zou

2024

Int. J. Control Autom. Syst.

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Smooth and Time Optimal Trajectory Planning for Industrial Robot Using a Single Polynomial

Cited by 5 publications

References 9 publications

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

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

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

Time-optimal and Smooth Trajectory Planning for Multi-axis Motion Systems Based on ISC Similarity

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