Whale Optimization Algorithm for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm

Qazani, Mohammad Reza Chalak; Asadi, Houshyar; Arogbonlo, Adetokunbo; Rahimzadeh, Ghazal; Mohamed, Shady; Pedrammehr, Siamak; Lim, Chee Peng; Nahavandi, Saeid

doi:10.1109/smc52423.2021.9659159

Cited by 10 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although applying the basic GWO algorithm for path planning can improve the path planning ability of robotic arms to a certain extent, there are still problems such as low operating accuracy, slow convergence speed, and easy falling into local optima. Much of the literature has also proposed corresponding improvement methods for the above issues [33][34][35][36]. Although these methods can play a certain role in addressing specific problems, there are still problems such as poor robustness, poor universality, and susceptibility to local optima when applied to the path planning of robotic arms.…”

Section: Introductionmentioning

confidence: 99%

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Feng,

Dai,

Zhou

et al. 2024

Machines

View full text Add to dashboard Cite

Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Feng,

Dai,

Zhou

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…As a result, the motion cueing algorithm (MCA) [21,22] is devised to re-generate the motion sensations for the simulator user to experience the same sensation as if in a vehicle, within the limited working area. MCA is divided to classical [23], adaptive [24][25][26][27][28][29], optimal [30][31][32][33] and model predictive [34][35][36][37][38][39][40][41] methods. In this regard, false motion cues lead to motion sickness in simulator users, which is the main disadvantage of the MCA.…”

Section: Introductionmentioning

confidence: 99%

A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN

Qazani

Tabarsinezhad

Asadi

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Self Cite

View full text Add to dashboard Cite

The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-theart studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods.

show abstract

Enhancing the Whale Optimisation Algorithm with sub-population and hybrid techniques for single- and multi-objective optimisation

Cai,

Choo,

et al. 2023

Soft Comput

View full text Add to dashboard Cite

Whale Optimization Algorithm for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm

Cited by 10 publications

References 43 publications

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN

Enhancing the Whale Optimisation Algorithm with sub-population and hybrid techniques for single- and multi-objective optimisation

Contact Info

Product

Resources

About