A substrate transport empiric arm droop mapping apparatus for a substrate transport system of a processing tool, the mapping apparatus including a frame, an interface disposed on the frame forming datum features representative of a substrate transport space in the processing tool defined by the substrate transport system, a substrate transport arm, that is articulated and has a substrate holder, mounted to the frame in a predetermined relation to at least one of the datum features, and a registration system disposed with respect to the substrate transport arm and at least one datum feature so that the registration system registers, in an arm droop distance register, empiric arm droop distance, due to arm droop changes, between a first arm position and a second arm position different than the first arm position and in which the substrate holder is moved in the transport space along at least one axis of motion.

A control device is provided which is operable to change the position of a distal end side link hub by driving each of arms, which are proximal end side links of a plurality of link mechanisms by means of an actuator. When in a series of operations, the position change of the distal end side link hub is mad by an angle greater than a predetermined angle, a relay position setting unit is provided for setting a relay point between a starting point and a terminating point of each of the arms so that the interference of the three axis arms may be relieved. A position change control unit performs a position control so as to pass simultaneously through the relay point so set.

Methods for automated robotic movement for a robotic device using an electronic computing device are presented, the methods including: causing the electronic computing device to establish a working zone; measuring distances to all obstacles in the working zone thereby detecting all obstacles in the working zone; establishing a coverage path that accounts for all detected obstacles; executing the coverage path such the robotic device covers the working zone at least once; if a new obstacle is detected, establishing an adapted coverage path that accounts for the new obstacle; and executing the adapted coverage path. In some embodiments, methods further include: bypassing the new obstacle; and returning to the coverage path.

A method for controlling vibration of flexible mechanical arms based on cooperative tracking is disclosed, including: building a dynamic model of the flexible mechanical arm, according to a dynamic characteristic, constructing a flexible mechanical arm group made up of a plurality of flexible mechanical arms, assigning one of the plurality of flexible mechanical arms as a leader and the rest ones as followers which are required to track the leader's motion trajectory so as to realize cooperative work; designing cooperative control-based boundary controllers in combination with a Lyapunov method to realize cooperative work and suppress vibration of the flexible mechanical arms; and constructing a Lyapunov function using Lyapunov direct method to validate stability of the flexible mechanical arms under the control.

The present invention relates to a movement-dependent stabilisation support system (100) for stabilising a moving body (200), which comprises a plurality of sensors (110), a plurality of actuators (120) and a control unit (130). The plurality of sensors (110) continuously detects movement parameters of the body (200), on which basis the control unit (130) determines whether there is an instability of the body (200). If it is determined that there is an instability, the control unit (130) selects a stabilisation strategy, according to which the actuators (120) are controlled. When controlled, the actuators (120) attached to the body (200) stiffen and limit the freedom of movement of the body (200), such that a movement in the direction of the imminent unstable state is prevented or suppressed. In this way, the body (200) is supported in its stabilisation and an imminent fall is prevented.

A method and system for calibration of an augmented reality (AR) device's position and orientation based on a robot's positional configuration. A conventional visual calibration target is not required for AR device calibration. Instead, the robot itself, in any pose, is used as a three dimensional (3D) calibration target. The AR system is provided with a CAD model of the entire robot to use as a reference frame, and 3D models of the individual robot arms are combined into a single object model based on joint positions known from the robot controller. The 3D surface model of the entire robot in the current pose is then used for visual calibration of the AR system by analyzing images from the AR device camera in comparison to the surface model of the robot in the current pose. The technique is applicable to initial AR device calibration and to ongoing device tracking.

A transfer source action information acquisition unit acquires first action information of the transfer source robot. A transfer destination action information acquisition unit acquires second action information of the transfer destination robot. A correction unit corrects the action information of the transfer source robot by using the second action information and in accordance with a predetermined update formula and thereby generates third action information of the transfer destination robot. The pieces of the second action information of the transfer destination robot are less than the pieces of the first action information of the transfer source robot. The first to third action information includes a set of data indicative of one or more robot joint values and a set of data indicative of a coordinate value of a robot specific part.

Methods, apparatus, and computer-readable media for determining and utilizing corrections to robot actions. Some implementations are directed to updating a local features model of a robot in response to determining a human correction of an action performed by the robot. The local features model is used to determine, based on an embedding generated over a corresponding neural network model, one or more features that are most similar to the generated embedding. Updating the local features model in response to a human correction can include updating a feature embedding, of the local features model, that corresponds to the human correction. Adjustment(s) to the features model can immediately improve robot performance without necessitating retraining of the corresponding neural network model.

An anti-collision safety device/method for a modular robot is provided which automatically derives a new/updated geometric model for the kinematic chain when the robot has been reconfigured.

A modular robotic vehicle (MRV) having a modular chassis configured for a vehicle utilizing two-wheel steering, four-wheel steering, six-wheel steering, eight-wheel steering controlled by a semiautonomous system or an autonomous driving system, either system is associated with operating modes which may include a two-wheel steering mode, an all-wheel steering mode, a traverse steering mode, a park mode, or an omni-directional mode utilized for steering sideways, driving diagonally or move crab like. Accordingly, during semiautonomous control a driver of the modular robotic vehicle may utilize smart I/O devices including a smartphone, tablet like devices, or a control panel to select a preferred driving mode. The driver may communicate navigation instructions via smart I/O devices to control steering, speed and placement of the MRV in respect to the operating mode. Accordingly, GPS and a wireless network provides navigation instructions during an autonomous operation involving driving, parking, docking or connecting to another MRV.