Abstract: An unmanned aerial vehicle can be configured to adjust a beam direction, provide path information, act as a base station, act as a cluster head, include an improved directional antenna or array of directional antennas, communicate in a collaboration using belief propagation, receive communications from a serving station aiding in navigation or improved signal performance, or the like.

Abstract: A method for calculating a time to contact of an autonomous vehicle, the method comprising: obtaining a plurality of event data an image, wherein the event data is associated with a pixel associated with a change in light intensity; determining a reference signal frequency associated with a transmitted light; identifying a select event data from the plurality of event data, wherein the light frequency associated with the select event data is substantially the same as the reference signal frequency; determining an object based on the select event data, wherein the object is fully enclosed by a bounding box comprising coordinates of a rectangular border; calculating a distance between a set of coordinates of the bounding box closest to the autonomous vehicle and the autonomous vehicle; and calculating the time to contact between the set of coordinates of the bounding box and the autonomous vehicle.

Abstract: A component of a robotic system, including: processor circuitry; and a non-transitory computer-readable storage medium including instructions that, when executed by the processor circuitry, cause the processor circuitry to: train a neuro-capability map plugin, which is a continuous or semi-continuous resolution neural network component encoded with kinematic capability attributes with respect to an action to be performed by a robot in a workspace; and publish the neuro-capability map plugin to a robotic skills repository where it is obtainable by robotic controller circuitry for embedding within a neural network usable perform one or more inferences to control the robot to perform the action.

Abstract: Example predictive wireless feedback control systems disclosed herein include a receiver to receive measurements of a controlled system via a first wireless link. Disclosed example systems also include an observer to output estimated values of states of the controlled system based on a state space model that is updated based on the measurements. Disclosed example system further include a predictor to predict future values of the states of the controlled system based on the estimated values of the states, a first latency of the first wireless link and an upper limit of a second latency associated with a second wireless link that is to communicate values of a control signal to an actuator associated with the controlled system. In disclosed examples, the predictor is to output the predicted future values of the states to a controller that is to determine the control signal.

Abstract: Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Abstract: Disclosed herein are systems, devices, and methods for real-time determinations of likelihoods for possible trajectories of a collaborator in a workspace with a robot. The system determines a current kinematic state of the collaborator and determines a goal of the collaborator based on occupancy information about objects in the workspace. The system also determines a possible trajectory for the collaborator based on the goal and the current kinematic state and determines a short-horizon trajectory for the collaborator based on previously observed kinematic states of the collaborator towards the goal. The system also determines a likelihood that the collaborator will follow the possible trajectory based on the short-horizon trajectory, the goal, and the current kinematic state. The system also generates a movement instruction to control movement of the robot based on the likelihood that the collaborator will follow the possible trajectory.

Abstract: Example wireless feedback control systems disclosed herein include a receiver to receive a first measurement of a target system via a first wireless link. Disclosed example systems also include a neural network to predict a value of a state of the target system at a future time relative to a prior time associated with the first measurement, the neural network to predict the value of the state of the target system based on the first measurement and a prior sequence of values of a control signal previously generated to control the target system during a time interval between the prior time and the future time, and the neural network to output the predicted value of the state of the target system to a controller. Disclosed example systems further include a transmitter to transmit a new value of the control signal to the target system via a second wireless link.

Abstract: An apparatus comprising a memory to store an observed trajectory of a pedestrian, the observed trajectory comprising a plurality of observed locations of the pedestrian over a first plurality of timesteps; and a processor to generate a predicted trajectory of the pedestrian, the predicted trajectory comprising a plurality of predicted locations of the pedestrian over the first plurality of timesteps and over a second plurality of timesteps occurring after the first plurality of timesteps; determine a likelihood of the predicted trajectory based on a comparison of the plurality of predicted locations of the pedestrian over the first plurality of timesteps and the plurality of observed locations of the pedestrian over the first plurality of timesteps; and responsive to the determined likelihood of the predicted trajectory, provide information associated with the predicted trajectory to a vehicle to warn the vehicle of a potential collision with the pedestrian.

Abstract: Techniques are disclosed to facilitate, in autonomous vehicles, the robust detection and classification of objects in a scene using a static sensors in conjunction with event-based sensors. A trained system architecture may be implemented, and the fusion of both sensors thus allows for the consideration of scenes with overexposure, scenes with underexposure, as well as scenes in which there is no movement. In doing so, the autonomous vehicle may detect and classify objects in conditions in which each sensor, if operating separately, would not otherwise be able to classify (or classify with high uncertainty) due to the sensing environment.

Abstract: A robot configured to be operable within a multi-robot system. The robot includes an input configured to receive global coordinate state information of the robot and of any neighboring robots or obstacles; and processing circuitry configured to: transform the global coordinate state information into a relative coordinate system that is with respect to the robot and is based on a type of desired formation of the robot and any neighboring robots or obstacles around a point; generate a reference formation algorithm which is based on the desired formation; and controlling, based on the reference formation algorithm and tracking errors between the desired formation and a current state of the robot, a trajectory of the robot to converge towards the desired formation while avoiding collisions with any neighboring robots or obstacles.

Abstract: Techniques are disclosed for a decentralized path and motion planning of autonomous agents within an environment. The planning may include determining if an active neighboring autonomous agent is present and selectively controlling the autonomous agent to operation in in an independent path planning operation mode and in a coordinating path planning operation mode, based on the detection of the neighboring agent(s).

Abstract: Example predictive wireless feedback control systems disclosed herein include a receiver to receive measurements of a controlled system via a first wireless link. Disclosed example systems also include an observer to output estimated values of states of the controlled system based on a state space model that is updated based on the measurements. Disclosed example system further include a predictor to predict future values of the states of the controlled system based on the estimated values of the states, a first latency of the first wireless link and an upper limit of a second latency associated with a second wireless link that is to communicate values of a control signal to an actuator associated with the controlled system. In disclosed examples, the predictor is to output the predicted future values of the states to a controller that is to determine the control signal.

Abstract: An apparatus comprising a memory to store an observed trajectory of a pedestrian, the observed trajectory comprising a plurality of observed locations of the pedestrian over a first plurality of timesteps; and a processor to generate a predicted trajectory of the pedestrian, the predicted trajectory comprising a plurality of predicted locations of the pedestrian over the first plurality of timesteps and over a second plurality of timesteps occurring after the first plurality of timesteps; determine a likelihood of the predicted trajectory based on a comparison of the plurality of predicted locations of the pedestrian over the first plurality of timesteps and the plurality of observed locations of the pedestrian over the first plurality of timesteps; and responsive to the determined likelihood of the predicted trajectory, provide information associated with the predicted trajectory to a vehicle to warn the vehicle of a potential collision with the pedestrian.

Abstract: A safety system for a vehicle may include one or more processors configured to determine uncertainty data indicating uncertainty in one or more predictions from a driving model during operation of a vehicle; change or update one or more of the driving model parameters to one or more changed or updated driving model parameters based on the determined uncertainty data; and provide the one or more changed or updated driving model parameters to a control system of the vehicle for controlling the vehicle to operate in accordance with the driving model including the one or more changed or updated driving model parameters.

Abstract: Methods and apparatus for dynamically routing robots based on exploratory on-board mapping are disclosed. A control system of a robot includes an image manager to command a depth camera to capture depth images of an environment. The depth camera has a field of view. The control system further includes a map generator to generate a map of the environment based on the depth images. The map includes a representation of unoccupied space within the environment, and a path extending through the unoccupied space from a reference location of the robot to a target location of the robot. The control system further includes a field of view evaluator to determine whether the field of view associated with the reference location satisfies a threshold. The control system further includes a route generator to generate, in response to the field of view associated with the reference location satisfying the threshold, a route to be followed by the robot within the environment.

Abstract: An unmanned aerial vehicle can be configured to adjust a beam direction, provide path information, act as a base station, act as a cluster head, include an improved directional antenna or array of directional antennas, communicate in a collaboration using belief propagation, receive communications from a serving station aiding in navigation or improved signal performance, or the like.

Abstract: Methods and apparatus to facilitate autonomous navigation of robotic devices. An example autonomous robot includes a region model analyzer to: analyze a first image of an environment based on a first neural network model, the first image captured by an image sensor of the robot when the robot is in a first region of the environment; and analyze a second image of the environment based on a second neural network model, the second image captured by the image sensor when the robot is in a second region of the environment, the second neural network associated with the second region. The example robot further includes a movement controller to: autonomously control movement of the robot within the first region toward the second region based on the analysis of the first image; and autonomously control movement of the robot within the second region based on the analysis of the second image.

Abstract: Systems and techniques for an architecture for contextual memories in map representation for 3D reconstruction and navigation are described herein. In an example, a system for contextual memory mapping is adapted to receive a data set of physical world sensor readings. The system may be further adapted to generate voxel data from the data set, the voxel data includes voxel coordinates and a physical world occupancy indicator. The system may be further adapted to select a block of addresses in the memory to store the voxel data. The system may be further adapted to generate a hash map to map voxel coordinates to memory locations in the block of addresses, the voxel coordinates having a contextual relationship that is maintained by the hash map. The system may be further adapted to store the voxel data at memory addresses based on the hash map.

Abstract: A human-robot collaboration system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: predict a human atomic action based on a probability density function of possible human atomic actions for performing a predefined task; and plan a motion of the robot based on the predicted human atomic action.

Abstract: Example predictive wireless feedback control systems disclosed herein include a receiver to receive measurements of a controlled system via a first wireless link. Disclosed example systems also include an observer to output estimated values of states of the controlled system based on a state space model that is updated based on the measurements. Disclosed example system further include a predictor to predict future values of the states of the controlled system based on the estimated values of the states, a first latency of the first wireless link and an upper limit of a second latency associated with a second wireless link that is to communicate values of a control signal to an actuator associated with the controlled system. In disclosed examples, the predictor is to output the predicted future values of the states to a controller that is to determine the control signal.