A method and system for robotic motion planning which perform dynamic velocity attenuation to avoid robot collision with static or dynamic objects. The technique maintains the planned robot tool path even when speed reduction is necessary, by providing feedback of a computed slowdown ratio to a tracking controller so that the path computation is always synchronized with current robot speed. The technique uses both robot-obstacle distance and relative velocity to determine when to apply velocity attenuation, and computes a joint speed limit vector based on a robot-obstacle distance, a maximum obstacle speed, and a computed stopping time as a function of the joint speed. Two different control structure implementations are disclosed, both of which provide feedback of the slowdown ratio to the motion planner as needed for faithful path following. A method of establishing velocity attenuation priority in multi-robot systems is also provided.

A robotic system includes a robotic vehicle having a propulsion system, one or more sensors that image data representative of an external environment, and a controller that determines a waypoint for the robotic vehicle to move toward. The controller determines limitations on movement of the robotic vehicle toward a waypoint. The limitations are based on the image data. The controller controls the propulsion system to move the robotic vehicle to the waypoint subject to the limitations on the movement to avoid colliding with one or more objects. The controller determines one or more additional waypoints subsequent to the robotic vehicle reaching the waypoint, determines one or more additional limitations on the movement of the robotic vehicle toward each of the respective additional waypoints, and control the propulsion system of the robotic vehicle to sequentially move the robotic vehicle to the one or more additional waypoints.

A method for guiding in real time a robot arm for the processing of data of the surface of a body, includes generating a body model including a meshing of points; planning a treatment trajectory on the surface of the body model with a calculator; activating at least one transmission of a transmitter and/or acquisition of a sensor of an operator device, the operator device being arranged at the distal end of the robotised arm, the activation being carried out when the orientation of the axis of the sensor or the transmitter is merged with a predefined straight line passing through the target point, the target point being referenced on the generated body model.

A robot is configured to perform a task on an object using a method for generating a 3D model sufficient to determine a collision free path and identify the object in an industrial scene. The method includes determining a predefined collision free path and scanning an industrial scene around the robot. Stored images of the industrial scene are retrieved from a memory and analyzed to construct a new 3D model. After an object is detected in the new 3D model, the robot can further scan the image in the industrial scene while moving along a collision free path until the object is identified at a predefined certainty level. The robot can then perform a robot task on the object.

The invention relates to a method for controlling a plurality of mobile driverless manipulator systems (10, 20), in particular driverless transport vehicles in a logistics environment (40) for manipulating objects (30). In the method, ambient information is provided by a central control device (50), and in one step, an object to be moved (30) in the surroundings is detected. The position and the pose of the detected object are used for updating the ambient information and are taken into account in the path planning of the mobile driverless manipulator systems (10, 20) in that, prior to a movement of the detected object (30), a first mobile driverless manipulator system (10) is used to check whether the detected object (30) is needed for the orientation of a second mobile driverless manipulator system (20).

A method for a human-robot collaborative operation includes having a robot perform at least one automated task within a workspace and generating a dynamic model of a workspace based on a static nominal model of the workspace and data from a plurality of sensors disposed throughout the workspace. The method further includes controlling operation of the robot based on the dynamic model and the human operation, and verifying completion of the human operation based on a task completion parameter associated with the human operation and on based on at least one of the dynamic model, the data from the plurality of sensors, and the at least one automated task performed by the robot.

The system, devices and methods disclosed herein enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles. A method may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property.

A method to enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property. Embodiments further include systems and devices practicing the method.

The system, devices and methods disclosed herein enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles. A method may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property.

A system includes a robotic vehicle having a propulsion and a manipulator configured to perform designated tasks. The system also including a local controller disposed onboard the robotic vehicle and configured to receive input signals from an off-board controller. Responsive to receiving an input signal for moving in an autonomous mode, the local controller is configured to move the robotic vehicle toward one of the different final destinations by autonomously and iteratively determining a series of waypoints until the robotic vehicle has reached the one final destination. For each iteration, the local controller is configured to determine a next waypoint between a current location of the robotic vehicle and the final destination, determine movement limitations of the robotic vehicle, and generate control signals in accordance with the movement limitations.