The invention relates to a method for programming a robot, in particular a robot comprising a robotic arm, in which method a movement to be performed by the robot is set up preferably in a robot programme by means of a predefined motion template, the motion template is selected from a database comprising a plurality of motion templates, the motion template comprises one or more execution modules that can be parameterized and at least one learning module, the one or more execution modules are used for planning and/or performing the robot movement or part of the robot movement, the leaning module records one or more configurations of the robot during an initialization process, in particular in the form of a teaching process, and the learning module calculates parameters for the one or more execution modules on the basis of the recorded configurations, preferably using an automatic learning process. Also disclosed is a corresponding system for programming a robot.

A method for operating a medical-robotic device is provided. The robotic device includes a number of components able to be moved autonomously in an environment of the robotic device. Planning data for an autonomous movement or constraint of at least one subset of the movable components is provided to the robotic device. A movement or constraint of the corresponding movable components to be carried out autonomously by the robotic device is planned based on the planning data provided. The planned movement or constraint is visually presented. A way for an operator to exert influence on the planned movement or constraint is provided, and the movement or constraint is autonomously carried out as a function of the influence exerted.

To control a motion of a device subject to constraints, a sequence of states and corresponding control inputs are transformed into a lifted space to determine a linear model of the dynamics of the device in the lifted space by minimizing fitting errors between the lifted states and approximation of the lifted states according to the linear control law. The fitting errors define an error model as a function bounding a data-driven envelope of a Lipschitz continuity on the fitting errors allowing to solve an optimal control problem in the lifted space according to the linear model subject to the constraints reformulated based on an evolution of the error model. The control input in the lifted space is transformed back to the original space for control.

A robot motion planning technique using an external computer communicating with a robot controller. A camera or sensor system provides input scene information including start and goal points and obstacle data to the computer. The computer plans a robot tool motion based on the start and goal points and the obstacle environment, where the robot motion is planned using either a serial or parallel combination of sampling-based and optimization-based planning algorithms. In the serial combination, the sampling method first finds a feasible path, and the optimization method then improves the path quality. In the parallel combination, both sampling and optimization methods are used, and a path is selected based on computation time, path quality and other factors. The computer converts dense planned waypoints to sparse command points for transfer to the robot controller, and the controller computes robot kinematics and interpolation points and controls the movement of the robot.

The invention relates to a method for programming a robot, in particular a robot comprising a robotic arm, in which method a movement to be performed by the robot is set up preferably in a robot programme by means of a predefined motion template, the motion template is selected from a database comprising a plurality of motion templates, the motion template comprises one or more execution modules that can be parameterized and at least one learning module, the one or more execution modules are used for planning and/or performing the robot movement or part of the robot movement, the leaning module records one or more configurations of the robot during an initialization process, in particular in the form of a teaching process, and the learning module calculates parameters for the one or more execution modules on the basis of the recorded configurations, preferably using an automatic learning process. Also disclosed is a corresponding system for programming a robot.

A simulation system to determine an optimal trajectory path for a robot with an attached implement includes a trajectory simulator which provides a simulated trajectory path for an implement, an implement model database which comprises motion data of the implement, and a logger that associates a time stamp of the implement's motion during the simulated trajectory path to generate logger data. A profile is determined by the logger data received from the logger which identifies implement motion that exceeds predetermined thresholds, and a tuner adjusts the simulated trajectory path so as to reduce the number of times predetermined thresholds are exceeded.

A robot program generation apparatus includes an allowable jerk value setting unit for setting allowable jerk values to joints of a robot, a joint information calculation unit for simulating execution of a robot program in a virtual space and calculating positions and jerks of the joints in association with time, a jerks determination unit for determining whether or not the calculated jerk is an excess jerk exceeding the allowable jerk values, a joint information identification unit for identifying the joints and positions of the joints in which the excess jerks are generated, and a robot program adjustment unit for adjusting the robot program by modifying a teaching position within the neighborhood of the positions of the joints in which the excess jerks are generated so that the jerks of the identified joints become equal to or smaller than the allowable jerk values.

A computer-implemented method executed by data processing hardware of a robot causes the data processing hardware to perform operations. The robot includes an articulated arm having an end effector configured to engage with an object. The operations include receiving a measured task parameter set for the end effector. The measured task parameter set representing positions of the end effector while manipulating the object. The operations also include generating a task space model for the object based on the measured task parameter set. The task space model modelling the at least one constrained axis of the object. The operations further include limiting movement of the end effector along the at least one constrained axis of the object based on the task space model.