Kinematic and Dynamic Optimal Trajectory Planning of Industrial Robot Using Improved Multi-objective Ant Lion Optimizer

Rout, Amruta; Mahanta, Golak Bihari; Deepak, B. B. V. L.; Biswal, Bibhuti Bhusan

doi:10.1007/s40032-020-00557-8

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…Dynamic optimal trajectory planning using multiobjective ALO was introduced by Rout et al (2020) for IRMs. For such optimal trajectory planning, the kinematic and dynamic constraints and torque were considered as the essential parameters.…”

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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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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“…Recent related works are given below: An improved Multi-objective Ant Lion Optimizer based Kinematic and Dynamic Optimal Trajectory Planning for Industrial Robot was introduced by Amruta et al [16]. For such optimal trajectory planning, the kinematic and dynamic constraints (i.e.…”

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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“…The said algorithm is compared with Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) and validates the performance in terms of accuracy and speed. Amruta Rout et al [196] presented the kinematics and dynamics constraints of robotic trajectory. Parameters such as torque and jerks affect the trajectory of robots.…”

Section: Application To Ground Vehiclesmentioning

confidence: 99%

“…Moth Flame (a) Compared to other algorithms, it produces good solutions in complex scenarios [192] (a) Has premature convergence rate [191] Simulation T ≥ 0(n 2 ) WOA (a) Easy implementation with fast convergence rate [194] (a) Difficult to handle in a complex environment [141] Simulation T ≥ 0(n 2 ) AntLion (a) Produces good results in complex environment [195] (a) Involvement of a lot of variables makes it difficult to handle when integrated with different algorithms [74,196] Simulation T ≥ 0(n 2 )…”

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confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

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Kinematic and Dynamic Optimal Trajectory Planning of Industrial Robot Using Improved Multi-objective Ant Lion Optimizer

Cited by 14 publications

References 21 publications

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

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

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