Purpose
– The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments.
Design/methodology/approach
– The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions; minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered.
Findings
– The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions.
Research limitations/implications
– The motion generated yield good results in both simulation and experimental environments.
Practical implications
– The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion.
Originality/value
– A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.
Multicriteria Optimal Humanoid Robot Motion Generation