Unconventional grasping in multi-limb humanoid robot using fuzzy logic intelligence

Abstract

This research article outlines grasping using the whole body of a human and explores the similar grasping potential of a humanoid robot. The unconventional grasping for a humanoid robot is addressed, in which the robot can perform autonomous grasping by the potential body limbs based on the object’s location and size. A comprehensive grasping possibility with human body limbs is studied using a stick diagram. These limb-assisted grasps are reported to utilize the theory of enumerative combinatorics, and an upper-body humanoid robot is used to implement some feasible intelligent, unconventional grasping. Single-Input Multi-Output and Multi-Input Multi-Output fuzzy logic control algorithms are developed to acquire the intelligent grasp objectives. These algorithms assign grasp weightage based on the size and position of the object to instruct the individual robotic arms to be involved in the grasping. Further, a Humanoid Robot Grasp Control algorithm is developed for head and arm motion control that assists in grasp execution. Finally, unconventional grasps are implemented using an upper-body dual-arm humanoid robot consisting of a 7 DoF robot with left and right arms, torso, and head in the virtual environment. The proposed strategy offers a benefit in intelligent grasping and improves the adaptability of a humanoid robot’s limbs for handling a variety of primitive-shaped objects.