Abstract: A robotics-assisted foundation installation system is provided in which data reporting the X, Y, and Z positions of foundation column tops are sent from a total surveying station to a grid control system. The grid control system receives the data and associates specific data with specific columns in an array—the “grid.” The grid control system compares the actual positions of the columns in the grid to target positions that were determined based on the requirements of the structure to be supported. After determining differences between the actual positions and the target positions, the grid control system sends instructions to column positioning tools associated with the individual columns. Actuators in a column positioning tool are directed by the grid control system to adjust the position of the associated column. Once the live streamed data confirms that each column is in the proper position, the columns are fixed in place.

Abstract: An example computer-implemented method includes receiving, from one or more vision components in an environment, vision data that captures features of the environment, including object features of an object that is located in the environment, and prior to a robot manipulating the object: (i) determining based on the vision data, at least one first adjustment to a programmed trajectory of movement of the robot operating in the environment to perform a task of transporting the object, and (ii) determining based on the object features of the object, at least one second adjustment to the programmed trajectory of movement of the robot operating in the environment to perform the task, and causing the robot to perform the task, in accordance with the at least one first adjustment and the at least one second adjustment to the programmed trajectory of movement of the robot.

Abstract: A robotics-assisted foundation installation system is provided in which data reporting the X, Y, and Z positions of foundation column tops are sent from a total surveying station to a grid control system. The grid control system receives the data and associates specific data with specific columns in an array—the “grid.” The grid control system compares the actual positions of the columns in the grid to target positions that were determined based on the requirements of the structure to be supported. After determining differences between the actual positions and the target positions, the grid control system sends instructions to column positioning tools associated with the individual columns. Actuators in a column positioning tool are directed by the grid control system to adjust the position of the associated column. Once the live streamed data confirms that each column is in the proper position, the columns are fixed in place.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: The present invention provides a compound having the structure: a processing of making the compound; and a process of using the compound as a reagent for the difluoromethoxylation and trifluoromethoxylation of arenes or heteroarenes.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: A robotics-assisted foundation installation system is provided in which data reporting the X, Y, and Z positions of foundation column tops are sent from a total surveying station to a grid control system. The grid control system receives the data and associates specific data with specific columns in an array - the “grid.” The grid control system compares the actual positions of the columns in the grid to target positions that were determined based on the requirements of the structure to be supported. After determining differences between the actual positions and the target positions, the grid control system sends instructions to column positioning tools associated with the individual columns. Actuators in a column positioning tool are directed by the grid control system to adjust the position of the associated column. Once the live streamed data confirms that each column is in the proper position, the columns are fixed in place.

Abstract: The present invention is directed to a wire comb. The wire comb includes a flat body which includes a first end and a second end. The ends are connected by an arcuate section which has an upper and lower surface. The upper surface of the arcuate section has a wire mount area that includes at least one opening formed in the upper surface with each opening including a shape selected to receive and hold a wire within. A mounting feature extends from at least one of the first end and the second end. The mounting feature is adapted to engage a printed circuit board.

Abstract: An example computer-implemented method includes receiving, from one or more vision components in an environment, vision data that captures features of the environment, including object features of an object that is located in the environment, and prior to a robot manipulating the object: (i) determining based on the vision data, at least one first adjustment to a programmed trajectory of movement of the robot operating in the environment to perform a task of transporting the object, and (ii) determining based on the object features of the object, at least one second adjustment to the programmed trajectory of movement of the robot operating in the environment to perform the task, and causing the robot to perform the task, in accordance with the at least one first adjustment and the at least one second adjustment to the programmed trajectory of movement of the robot.

Abstract: An example method for providing a graphical user interface (GUI) of a computing device includes receiving an input indicating a target pose of the robot, providing for display on the GUI of the computing device a transparent representation of the robot as a preview of the target pose in combination with the textured model of the robot indicating the current state of the robot, generating a boundary illustration on the GUI representative of a limit of a range of motion of the robot, based on the target pose extending the robot beyond the boundary illustration, modifying characteristics of the transparent representation of the robot and of the boundary illustration on the GUI to inform of an invalid pose, and based on the target pose being a valid pose, sending instructions to the robot causing the robot to perform the target pose.

Abstract: These drawings Illustrate certain aspects of some of the embodiments of the present disclosure, and should not be used to limit or define the claims. FIG. 1 is a schematic diagram of an acrolein injection system and acrolein leak detection and alert system that may be used in accordance with certain embodiments of the present disclosure. FIG. 2 is another schematic diagram of an acrolein injection system and acrolein leak detection and alert: system that may be used in accordance with certain embodiments of the present disclosure. FIG. 3 is another schematic diagram of an acrolein injection system and acrolein leak detection and alert system that may be used in accordance with certain embodiments of the present disclosure. FIG. 4 is a diagram illustrating an example of a wellbore drilling assembly that may be used in accordance with certain embodiments of the present disclosure.

Abstract: An improvement to a roadside sprayer fixates spray nozzles on a registration plate. The registration plate may include integral tabs depending on the application. Inclination of the tabs is adjustable to place streams and droplets in a desired swath coverage. With the nozzles rigidly mounted onto the plate, the entire plate is notated. The use of the registration plate in a spray unit creates a uniform nutation among the nozzles, thereby reducing variability in droplet placement, and providing a more predictable spray from the nozzles. In other embodiments, the spray unit is utilized in a spray system adaptable to service vehicles, and may be utilized in conjunction with a vegetation engagement device, such as a cutter to engage multiple zones of a roadway right-of-way. Additionally, an improved spray unit includes an electromagnetic field and an attractor to generate plate motion.

Abstract: The COMBINATION DEADBOLT LOCK COVER is a cover that goes over the deadbolt to keep lock-pickers from gaining access to the deadbolt lock of a dwelling. It consists of two components. The first component is the cover that you place the outside locking mechanism of your deadbolt inside and secure it to the hole on your dwelling door that the deadbolt originally goes. Once you screw your deadbolt lock with the COMBINATION DEADBOLT LOCK COVER securely covering your deadbolt lock then you slide the second part of the COMBINATION DEADBOLT LOCK COVER which is a DROPDOWN PLATE into a prefabricated slot to render the deadbolt lock of your dwelling completely un-accessible. The DROPDOWN PLATE has a hole in it in which you can slide a combination lock or a standard lock into making the deadbolt lock on your dwelling door un-seen and un-accessible.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: A method for managing the servicing of vehicles in a fleet includes using a mobile device associated with a vehicle to be serviced to contact a fleet call center. The contact is received and a service request is opened in the fleet call center. A local servicer is identified, and information in the service request is sent to a data device of a service technician of the local servicer. Upon completion of servicing of the vehicle, a notification of completion of the service request is sent to a service administrator. The notification is validated to generate a completed service request, and information in the completed service request is sent from the service administrator to an approver of a fleet operations unit. The completed service request is approved at the fleet operations unit, and a purchase order is generated and transmitted from the fleet operations unit to the service administrator.

Abstract: The present invention provides a compound having the structure: a processing of making the compound; and a process of using the compound as a reagent for the difluoromethoxylation and trifluoromethoxylation of arenes or heteroarenes.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: A method for controller tracking with multiple degrees of freedom includes generating depth data at an electronic device based on a local environment proximate the electronic device. A set of positional data is generated for at least one spatial feature associated with a controller based on a pose of the electronic device, as determined using the depth data, relative to the at least one spatial feature associated with the controller. A set of rotational data is received that represents three degrees-of-freedom (3DoF) orientation of the controller within the local environment, and a six degrees-of-freedom (6DoF) position of the controller within the local environment is tracked based on the set of positional data and the set of rotational data.

Abstract: A method for managing the servicing of vehicles in a fleet includes using a mobile device associated with a vehicle to be serviced to contact a fleet call center. The contact is received and a service request is opened in the fleet call center. A local servicer is identified, and information in the service request is sent to a data device of a service technician of the local servicer. Upon completion of servicing of the vehicle, a notification of completion of the service request is sent to a service administrator. The notification is validated to generate a completed service request, and information in the completed service request is sent from the service administrator to an approver of a fleet operations unit. The completed service request is approved at the fleet operations unit, and a purchase order is generated and transmitted from the fleet operations unit to the service administrator.