Abstract: A computer-implemented method is disclosed for determining optimal operational parameters for a model predictive controller (MPC) for vehicle control. The method includes training a machine learning model by simulating vehicle operations across a range of operational parameters using an MPC framework. The simulation identifies ranges of parameters that recover the vehicle from unstable states, with the bounds corresponding to the vehicle's performance envelope. The method comprises determining an optimum value for a vehicle parameter based on the simulation output, updating training data accordingly, and revising the vehicle's control system using the optimum value to control the operation of an actuator for a particular maneuver. The system may update the parameter set in response to changing vehicle or environmental conditions and supports both autonomous and human-in-the-loop operation.

Abstract: Unmanned systems, and primarily vehicles, and in most embodiments unmanned aerial systems (UAS), as well as novel guided and unguided munitions are presented herein. to the two combine to present unmanned weaponized systems that not only carry weapons functionality, but allow for advanced observation and reconnaissance. These unmanned systems are largely directed toward military applications where the weaponized unmanned system may be forward deployed to allow human warfighters to remain at long range distances from the potential targets, and to provide surveillance, target identification and tracking, general reconnaissance information, and the like, while also providing weaponized attack capabilities. The unmanned systems may, in some embodiments, include advanced command and control capabilities for communication between the system and the remote, rear-positioned warfighter, and between separate elements of the unmanned system.

Abstract: Some embodiments described herein provide intelligent movable racks for a data center and a central system for monitoring and directing the positioning of such racks within the data center. For example, a rack may include computing equipment as well as a power system, a cooling system, and a cabling system (e.g., for data communication). The rack may include a controller in communication with the computing equipment, the power system, the cooling system, and the cabling system. The rack may also include a rack interface for physically supporting the rack and operatively connecting the systems of the rack to power, cooling, and cabling infrastructure of the data center. The rack interface may receive an autonomous robot for moving the rack within the data center. The controller may control the power system and the cooling system based in part on the autonomous movement of the rack.

Abstract: Disclosed are systems and methods for vehicle control. In one example, the system includes a memory with an instruction module that, when executed by a processor, directs the processor to manage vehicle operation using a control action sequence from a model predictive controller. This controller utilizes an enhanced predicted vehicle state derived from a predicted vehicle state and a residual generated by a last-layer Bayesian meta-learning vehicle model. The system enhances vehicle control by integrating advanced predictive modeling and adaptive learning techniques.

Abstract: A method for a replay driver skill improvement system is described. The method includes logging vehicle commands requested by a vehicle operator of an ego vehicle to perform a selected driving maneuver. The method also includes identifying one or more of the logged vehicle commands in which operation of the ego vehicle is outside of a predetermined threshold while performing the selected driving maneuver. The method further includes operating the ego vehicle according to the logged vehicle commands until the one or more of the logged vehicle commands in which operation of the ego vehicle is outside of the predetermined threshold are reached. The method also includes performing, through shared control with the vehicle operator, improved vehicle commands to complete the selected driving maneuver while operating the ego vehicle at or within the predetermined threshold.

Abstract: Systems and methods of autonomously controlling a vehicle across the grip driving and drift driving operating ranges, are provided. The contemplated autonomous control can be effectuated using a closed-loop control system. In some embodiments, closed-loop control may be accomplished by deriving control laws involving sideslip angle, yaw rate, wheel speed, and other vehicle states. These control laws may be used to control the vehicle in a stable drift condition.

Abstract: A mobile printing robot system for printing a construction layout. The method addresses a problem that occurs when there is a loss of a tracking lock to an absolute positioning device, such as a total station. In a construction site, a line of sight between a mobile robot and an absolute positioning device may be lost when the mobile robot moves into a shadowed region behind an obstacle, such as a column. The mobile printing robot may use its local sensor data to continue to print until a maximum estimated position is reached. The mobile robot may also provide position updates to the absolute positioning device to aid it to regain a track lock when the mobile robot emerged from the shadowed region.

Abstract: Systems and methods provide for corrective assistance to maintain a stable controllable drift, while permitting a drift driving experience for the driver. Examples of the systems and methods disclosed herein include receiving first data corresponding to a current operational state of a vehicle while performing a controllable drift; receiving a request to operate the vehicle in a requested operational state; determining that the requested operational state exceeds a threshold operational state associated with an uncontrollable drift; and, in response to the determination, providing corrective assistance to operate the vehicle in a corrective operational state to prevent the vehicle from entering an uncontrollable drift.

Abstract: A mobile printing robot prints a building layout on a construction surface. The building layout has lines that are printed. The mobile printing robot also prints additional line attribute information to aid construction crews. This may include a description of line type (e.g. control line, wall type, etc.). It may also include information on offset as another example. As still another example, directional information may be printed for a line. As still another example, mandatory or voluntary construction details, such materials to be used for implementing features associated with the layout lines, may be printed.

Abstract: A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle can receive from a data store or a graphical user interface, ranges for one or more operational parameters. The computer implemented method can determine optimum values for vehicle parameters of the vehicle of one or more other parameters by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of a parameter as determined by the computer implemented method.

Abstract: Systems and methods for determining a maximum phase recovery envelope are disclosed herein. In one example, a system includes a processor and a memory having a vehicle control module. The vehicle control module includes instructions that, when executed by the processor, cause the processor to determine a critical point on a phase plane indicating a maximum defined recovery point a vehicle can recover from, perform forward and reverse simulations from the critical point to define outermost contours of a maximum phase recovery envelope using parameters and state of the vehicle, and cause the vehicle to operate within the maximum phase recovery envelope.

Abstract: Some embodiments described herein provide intelligent movable racks for a data center and a central system for monitoring and directing the positioning of such racks within the data center. For example, a rack may include computing equipment as well as a power system, a cooling system, and a cabling system (e.g., for data communication). The rack may include a controller in communication with the computing equipment, the power system, the cooling system, and the cabling system. The rack may also include a rack interface for physically supporting the rack and operatively connecting the systems of the rack to power, cooling, and cabling infrastructure of the data center. The rack interface may receive an autonomous robot for moving the rack within the data center. The controller may control the power system and the cooling system based in part on the autonomous movement of the rack.

Abstract: A computer implemented method for determining optimal values for controls parameters for a model predictive controller for controlling a vehicle can receive from a data store or a graphical user interface, ranges for one or more operational parameters. The computer implemented method can determine optimum values for controls parameters by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the controls parameter as determined by the computer implemented method.

Abstract: Systems and methods of controlling a vehicle in a stable drift are provided. With the goal of enhancing the driver experience, the disclosed drift control systems provide an interactive drift driving experience for the driver of a vehicle. In some embodiments, a driver is allowed to take manual control of a vehicle after a stable drift is initiated. For safety reasons, an assisted driving system may provide corrective assistance to prevent the vehicle from entering an unstable/unsafe drift. In other embodiments, an autonomous driving system retains control of the vehicle throughout the drift. However, the driver may perform “simulated drift maneuvers” such as counter-steering, and clutch kicking in order to communicate their desire to drift more or less aggressively. Accordingly, the autonomous driving system will effectuate the driver's communicated desire in a manner that keeps the vehicle in a safe/stable drift.

Abstract: Systems and methods provide for corrective assistance to maintain a stable controllable drift, while permitting a drift driving experience for the driver. Examples of the systems and methods disclosed herein include receiving first data corresponding to a current operational state of a vehicle while performing a controllable drift; receiving a request to operate the vehicle in a requested operational state; determining that the requested operational state exceeds a threshold operational state associated with an uncontrollable drift; and, in response to the determination, providing corrective assistance to operate the vehicle in a corrective operational state to prevent the vehicle from entering an uncontrollable drift.

Abstract: Systems and methods include techniques associated with one or more machine learning systems analyze, compare, and process one or both of model outputs or model inputs. Content verification may include generating content using a first trained machine learning system and then verifying the initial generation using one or more second trained machine learning systems, such as by generating content associated with a prompt, comparing output labels, or comparing output responsive to changing parameters. Additionally, data preparation may include image scaling and batching methods to improve machine learning outputs.

Abstract: Systems and methods are provided for dynamic driver training, and may include: a communication interface to receive sensor data, the sensor data comprising driver biometric data and driver performance data for a driver operating a vehicle; a driver inference circuit to infer a skill level and emotional state of the driver operating the vehicle; and a driver training circuit to, based on the inferred skill level and emotional state of the driver operating the vehicle, dynamically adjust a driver training level for the driver while the driver is operating the vehicle.

Abstract: A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle, can receive from a data store or a graphical user interface, ranges for one or more external parameters. The computer implemented method can determine optimum values for external parameters of the vehicle by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the vehicle parameter as determined by the computer implemented method.

Abstract: A computer implemented method for determining optimal values for controls parameters for a model predictive controller for controlling a vehicle can receive from a data store or a graphical user interface, ranges for one or more operational parameters. The computer implemented method can determine optimum values for controls parameters by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the controls parameter as determined by the computer implemented method.

Abstract: Systems and methods include techniques associated with one or more machine learning systems analyze, compare, and process one or both of model outputs or model inputs. Content verification may include generating content using a first trained machine learning system and then verifying the initial generation using one or more second trained machine learning systems, such as by generating content associated with a prompt, comparing output labels, or comparing output responsive to changing parameters. Additionally, data preparation may include image scaling and batching methods to improve machine learning outputs.