Abstract: A robot learning system for trajectory learning of a robot (RB) having a robot arm between a base and a tool center point (TCP). A user interface allows the user to control the robot arm in order to follow a desired trajectory during a real-time. A probe sensor (PS) is mounted on the TCP during the learning session. The probe sensor (PS) measures a distance parameter (Z) indicative of distance from the TCP and a surface forming the trajectory to be followed, and an orientation parameter (X, Y) indicative of orientation of the TCP and the surface forming the trajectory to be followed. These distance and orientation data are provided as a feedback to the controller of the robot (CTL) during the real-time learning session, thereby allowing the robot controller software to assist the user in following a desired trajectory in a continuous manner.

Abstract: There is provided a computer-implemented method for determining feasible joint trajectories for an n-dof (n>5) robot in a rotation invariant processing of an object, such as milling, painting, and welding. The method includes the steps of receiving geometric data representing the object; receiving geometric data representing the processing tool; receiving a tool path X(t), where t is time; searching for feasible paths qRobot (t) using IK (t) and qTool (t) as set of possible solutions at time t, wherein qRobot (t) defines positions of all the joints in the robot as function of time, qTool (t) defines the rotation of a tool flange around the tool axis at time t, and IK (t) defines the inverse kinematics solutions for a given X(t) and qTool (t); and determining, from the geometric data and X(t), how the joint path qRobot (t) should be chosen so as to comply with one or more optimisation criteria.

Abstract: There is provided a computer-implemented method for determining feasible joint trajectories for an n-dof (n>5) robot in a rotation invariant processing of an object, such as milling, painting, and welding. The method includes the steps of receiving geometric data representing the object; receiving geometric data representing the processing tool; receiving a tool path X(t), where t is time; searching for feasible paths qRobot (t) using IK (t) and qTool (t) as set of possible solutions at time t, wherein qRobot (t) defines positions of all the joints in the robot as function of time, qTool (t) defines the rotation of a tool flange around the tool axis at time t, and IK (t) defines the inverse kinematics solutions for a given X(t) and qTool (t); and determining, from the geometric data and X(t), how the joint path qRobot (t) should be chosen so as to comply with one or more optimisation criteria.