Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

The trajectory planning for robotic arms is a significant area of research, given its role in facilitating seamless trajectory execution and enhancing movement efficiency and accuracy. This paper focuses on the development of path planning algorithms for a robotic arm with six degrees of freedom. Specifically, three alternative approaches are explored: polynomial (cubic and quantic), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The comparison of outcomes between different methods revealed that polynomial methods were found to be more straightforward to implement, albeit constrained by the intricacy of the pathway. Upon examining the functioning of the WOA, it has been shown that it is well suited for all types of pathways, regardless of their level of complexity. In addition, when GA is applied, it has been shown less smoothness than WOA but also less complexity. In brief, WOA is deemed superior in the path planning process since it is more thorough in determining the optimal path due to the conical spiral path technique it employs in offering optimized path planning. in comparison to GA, WOA is better in implementation speed and accuracy. However, GA is smoother in start and finish path

Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

The trajectory planning for robotic arms is a significant area of research, given its role in facilitating seamless trajectory execution and enhancing movement efficiency and accuracy. This paper focuses on the development of path planning algorithms for a robotic arm with six degrees of freedom. Specifically, three alternative approaches are explored: polynomial (cubic and quantic), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The comparison of outcomes between different methods revealed that polynomial methods were found to be more straightforward to implement, albeit constrained by the intricacy of the pathway. Upon examining the functioning of the WOA, it has been shown that it is well suited for all types of pathways, regardless of their level of complexity. In addition, when GA is applied, it has been shown less smoothness than WOA but also less complexity. In brief, WOA is deemed superior in the path planning process since it is more thorough in determining the optimal path due to the conical spiral path technique it employs in offering optimized path planning. in comparison to GA, WOA is better in implementation speed and accuracy. However, GA is smoother in start and finish path