Abstract
The performance of robot arm motion generated via neural network are presented in this paper. The robot arm motion for obstacle avoidance simultaneously optimizing three functions; minimum distance, minimum time and minimum energy are generated. Four different initial and goal position had been chosen to test and analyze the performance of generated neural controller. The same neural controllers can be employed to a different range of initial and goal position. The motion generated yield good results in the simulator. In this research a new approach for intelligent robot arm path and motion generation are successfully implemented.
Highlights
Human being is said to have unique traits compared to other living creatures
As for this research, the evolved neural controller for generating the robot motion of the robot arm, three different optimization functions had been introduced based on the needs or requirements of this study
multi objective genetic algorithm (MOGA) had been chosen for solving complex optimization problem and provide better solution compare to deterministic method such as Jacobian [12]
Summary
Human being is said to have unique traits compared to other living creatures. Human being has the ability to think constructively, and fulfill daily needs by performing various kind of task, which may be complicated and need further scrutiny. If the humanoid robot is programmed to perform simple movement such as to move an object, it needs to identify at least seven different elements namely shape, size, position, color (for object’s identification purposes), as well as optimum distance, speed and energy (for the robot’s hand movement). As for this research, the evolved neural controller for generating the robot motion of the robot arm, three different optimization functions had been introduced based on the needs or requirements of this study. The three different functions are optimum time, distance and energy Those three criteria are chosen based on human arm motion criteria and it covers range of the required robot’s motion for the execution of task. In laymen’s term, the humanoid robot itself intelligently choose the best neural controller depending on the task which is required to be executed
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