This work shows a method of creating trajectories to achieve end-effector velocity control for high degree of freedom position controlled, high-gain, robots. The focus of this work is throwing an object. It is shown that the full reachable area of the end-effector does not need to be known to achieve the desired velocity when a good collision model of the robot is available. The end-effector velocity (magnitude and direction) is specified as well as a duration of this velocity. A sparse map of reachable end-effector positions in free space and the corresponding poses in joint space is created using random sampling in joint space and forward kinematics. The desired trajectory in free space is placed within the sparse map with the first point of the trajectory being a known pose from the original sparse map. The Jacobian Transpose Controller method of inverse kinematics is then used to find the subsequent points in the trajectory. Each pose in the trajectory is checked against the collision model to guarantee no self-collisions. This method was tested on the 130 cm tall full size humanoid Jaemi Hubo and its virtual representation.
Intelligent Robots and Systems
Jacobian matrices,collision avoidance,dexterous manipulators,end effectors,humanoid robots,manipulator kinematics,position control,sampling methods,sparse matrices,velocity control,Jacobian transpose controller method,collision-free trajectory design,degree-of-freedom position controlled-high-gain robots,end-effector velocity control,end-effector velocity direction,end-effector velocity magnitude,forward kinematics,humanoid Jaemi Hubo robot,inverse kinematics,joint space,object throwing,random sampling,reachable end-effector position sparse map,robot collision model,self-collision detection,sparse reachable maps
Computer vision,Kinematics,Inverse kinematics,Jacobian matrix and determinant,Computer science,Control engineering,Robot end effector,Forward kinematics,Artificial intelligence,HUBO,Trajectory,Humanoid robot