Abstract: An example virtualized computing system includes a host cluster having a virtualization layer directly executing on hardware platforms of hosts, the virtualization layer supporting execution of virtual machines (VMs), the VMs including pod VMs, the pod VMs including container engines supporting execution of containers in the pod VMs; an orchestration control plane integrated with the virtualization layer, the orchestration control plane including a master server and pod VM controllers, the pod VM controllers executing in the virtualization layer external to the VMs, the pod VM controllers configured as agents of the master server to manage the pod VMs; pod VM agents, executing in the pod VMs, configured as agents of the pod VM controllers to manage the containers executing in the pod VMs.

Abstract: An example virtualized computing system includes: a host cluster having hosts and a virtualization layer executing on hardware platforms of the hosts, the virtualization layer supporting execution of virtual machines (VMs); an orchestration control plane integrated with the virtualization layer, the orchestration control plane including a master server executing in a first VM of the VMs; guest cluster infrastructure software (GCIS) executing in the master server, the GCIS configured to create a set of objects defining a container orchestration cluster, and manage lifecycles of second VMs of the VMs based on state of the set of objects; and guest software executing in the second VMs to implement the container orchestration cluster as a guest cluster of the host cluster, the guest software having components that interface with the GCIS.

Abstract: A simulation may be used to determine a difference between progress of a manually-driven vehicle and progress of a simulated autonomous vehicle. The method includes retrieving log data collected for the manually-driven vehicle driving along a route, generating a plurality of path segments for a portion of the route. The plurality of path segments corresponds to points in a lane that the manually-driven vehicle traveled through on the portion of the route. The method also includes running, using a software of the autonomous vehicle, a simulation of the autonomous vehicle driving along the plurality of path segments, extracting metrics from the log data and the simulation, and determining the difference between a first progress of the manually-driven vehicle and a second progress of the simulated autonomous vehicle based on the metrics.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

Abstract: A location awareness system including a communication network, and a network operating element coupled to the communication network. At least one anchor network gateway is coupled to the communication network, the at least one anchor network gateway configured to generate a wireless anchor network. A plurality of anchors are configured to couple to one of the at least one anchor network gateway via its respective wireless anchor network. A plurality of tags is each configured to communicate with at least one anchor to provide ranging information for determination of a position of the tag within an area covered by the system.

Abstract: A turbine engine having a compressor section, a combustor section, a turbine section, and a rotatable drive shaft that couples a portion of the turbine section and a portion of the compressor section. A bypass conduit couples the compressor section to the turbine section while bypassing at least the combustion section. At least one particle separator is located in the turbine engine having a separator inlet that receives a bypass stream, a separator outlet that receives a reduced-particle stream flows, and a particle outlet that receives a concentrated-particle stream comprising separated particles. A conduit, fluidly coupled to the particle outlet, extends through an interior of at least one stationary vane.

Abstract: A simulation may be used to determine a difference between progress of a manually-driven vehicle and progress of a simulated autonomous vehicle. The method includes retrieving log data collected for the manually-driven vehicle driving along a route, generating a plurality of path segments for a portion of the route. The plurality of path segments corresponds to points in a lane that the manually-driven vehicle traveled through on the portion of the route. The method also includes running, using a software of the autonomous vehicle, a simulation of the autonomous vehicle driving along the plurality of path segments, extracting metrics from the log data and the simulation, and determining the difference between a first progress of the manually-driven vehicle and a second progress of the simulated autonomous vehicle based on the metrics.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

Abstract: Aspects of the disclosure provide a of generating and following planned trajectories for an autonomous vehicle. For instance, a baseline for a planned trajectory that the autonomous vehicle can use to follow a route to a destination may be determined. A stopping point corresponding to a traffic control that will cause the autonomous vehicle to come to a stop using the baseline may be determined. Sensor data identifying objects and their locations may be received. A plurality of constraints may be generated based on the sensor data. A planned trajectory may be generated using the baseline, the stopping point, and the plurality of constraints, wherein constraints beyond the stopping point are ignored.

Abstract: A simulation may be used to determine a difference between progress of a manually-driven vehicle and progress of a simulated autonomous vehicle. The method includes retrieving log data collected for the manually-driven vehicle driving along a route, generating a plurality of path segments for a portion of the route. The plurality of path segments corresponds to points in a lane that the manually-driven vehicle traveled through on the portion of the route. The method also includes running, using a software of the autonomous vehicle, a simulation of the autonomous vehicle driving along the plurality of path segments, extracting metrics from the log data and the simulation, and determining the difference between a first progress of the manually-driven vehicle and a second progress of the simulated autonomous vehicle based on the metrics.

Abstract: Systems and methods related to roadmaps for mobile robotic devices are provided. A computing device can determine a roadmap that includes a first intersection associated with first and second edges. The computing device can determine an edge interaction region (EIR) surrounding the first intersection that includes portions of the first and second edges, where a traversal region on the first edge portion can overlap a traversal region on the second edge portion. The computing device can determine first and second sub-edges of the first edge; the first sub-edge within the EIR and the second sub-edge outside the EIR. The computing device can receive a request to determine a route, determine the route specifying travel along the first sub-edge with a first rule set and along the second sub-edge with a second rule set, and provide the route.

Abstract: An example system includes a vehicle and a sensor connected to the vehicle. The system may receive a predetermined path for the vehicle to follow. The system may also receive a plurality of objectives, associated with a corresponding set of sensor data, for which to collect sensor data. The system may determine, for each of the plurality of objectives, a portion of the environment for the sensor to scan to acquire the corresponding set of sensor data. The system may determine, based on the portion of the environment determined for each of the plurality of objectives, a sensor trajectory through which to move the sensor. The system may cause the sensor to move through the determined sensor trajectory and scan portions of the environment corresponding to the determined sensor trajectory as the vehicle moves along the predetermined path.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for spatiotemporal reservations for robots. In various implementations, a sequence of spatial regions of an environment, and a sequence of respective time intervals that are reserved for a robot to operate within the sequence of spatial regions, may be reserved for the robot. A default path through the sequence of spatial regions may be identified. During traversal of the default path, it may be determined that the default path will be unpassable by the robot through a given spatial region during a given time interval reserved for the robot to operate within the given spatial region. Thus, an alternative path through the given spatial region that is traversable by the robot during the given time interval may be identified. The robot may then be traversed along the alternative path through the given spatial region within the given time interval.

Abstract: Systems and methods related to roadmaps for mobile robotic devices are provided. A computing device can determine a roadmap that includes a first intersection associated with first and second edges. The computing device can determine an edge interaction region (EIR) surrounding the first intersection that includes portions of the first and second edges, where a traversal region on the first edge portion can overlap a traversal region on the second edge portion. The computing device can determine first and second sub-edges of the first edge; the first sub-edge within the EIR and the second sub-edge outside the EIR. The computing device can receive a request to determine a route, determine the route specifying travel along the first sub-edge with a first rule set and along the second sub-edge with a second rule set, and provide the route.

Abstract: An example system may include a vehicle, a sensor, and a control system that may determine a target location for an object carried by the vehicle. The control system may also determine a plurality of points defining a boundary of a volume to be occupied by the object at the target location. The plurality of points may be scannable in a sequence by the sensor to scan the volume. The control system may additionally determine a respective field of visibility to each respective point. Further, the control system may determine a path for the vehicle to follow to the target location. The respective field of visibility may intersect with at least a respective portion of the determined path such that each respective point is observable by the sensor along at least the respective portion of the determined path as the vehicle moves along the determined path to the target location.

Abstract: Systems and methods related to roadmaps for mobile robotic devices are provided. A computing device can determine a roadmap that includes a first intersection associated with first and second edges. The computing device can determine an edge interaction region (EIR) surrounding the first intersection that includes portions of the first and second edges, where a traversal region on the first edge portion can overlap a traversal region on the second edge portion. The computing device can determine first and second sub-edges of the first edge; the first sub-edge within the EIR and the second sub-edge outside the EIR. The computing device can receive a request to determine a route, determine the route specifying travel along the first sub-edge with a first rule set and along the second sub-edge with a second rule set, and provide the route.

Abstract: An example system includes a vehicle and a sensor connected to the vehicle. The system may receive a predetermined path for the vehicle to follow. The system may also receive a plurality of objectives, associated with a corresponding set of sensor data, for which to collect sensor data. The system may determine, for each of the plurality of objectives, a portion of the environment for the sensor to scan to acquire the corresponding set of sensor data. The system may determine, based on the portion of the environment determined for each of the plurality of objectives, a sensor trajectory through which to move the sensor. The system may cause the sensor to move through the determined sensor trajectory and scan portions of the environment corresponding to the determined sensor trajectory as the vehicle moves along the predetermined path.

Abstract: An example system includes a vehicle and a sensor connected to the vehicle. The system may receive a predetermined path for the vehicle to follow. The system may also receive a plurality of objectives, associated with a corresponding set of sensor data, for which to collect sensor data. The system may determine, for each of the plurality of objectives, a portion of the environment for the sensor to scan to acquire the corresponding set of sensor data. The system may determine, based on the portion of the environment determined for each of the plurality of objectives, a sensor trajectory through which to move the sensor. The system may cause the sensor to move through the determined sensor trajectory and scan portions of the environment corresponding to the determined sensor trajectory as the vehicle moves along the predetermined path.

Abstract: An example system may include a motor, a position-controlled motor controller configured to drive the motor to a commanded position with a characteristic acceleration profile, and a control system. The control system may be configured to determine a target velocity for the motor. The control system may be additionally configured to determine a target position that, when commanded to the motor controller, is predicted to cause the motor controller to drive the motor with the target velocity at a target time point by driving the motor with the characteristic acceleration profile. Further, the control system may be configured to provide an instruction for execution by the position-controlled motor controller, the instruction may be configured to cause the motor controller to drive the motor to the target position.