Methods and enhanced apparatus used in such methods are described that a dispatched logistics operation for a deliverable item from a hold-at-location (HAL) logistics facility having a secured storage and using a modular autonomous bot apparatus assembly and a dispatch server. The bot apparatus assembly picks up and delivers the item from the HAL facility in response to a delivery dispatch command from the dispatch server. In response, the MAM of the bot verifies compatibility of modular components for the operation, controls receiving of the deliverable item from the secured storage at the HAL facility, then autonomously causes movement to the delivery destination. The MAM notifies the customer before delivery of the approaching delivery, authenticates delivery is to the authorized customer, provides access to the item within the bot apparatus assembly, monitors unloading of the item, then autonomously moves back to the HAL facility.

The present disclosure relates to systems, methods, and computer readable media implemented in connection with an unmanned aerial vehicle (UAV) to navigate a UAV along a desired path. For example, systems disclosed herein identify an anticipatory flight path and identify a plurality of reference points on the flight path relative to a current position of the UAV. The systems described herein may further determine reference angles between a current trajectory of the UAV and the reference points to determine an updated trajectory that the UAV should take to stay close to the identified flight path. The systems described herein may further cause the UAV to accelerate in a lateral direction based on the updated trajectory. The features and functionality of systems disclosed herein enable the UAV to accurately follow a complex path having sharp turns with little or no advanced knowledge of the flight path prior to departure.

A transportation system, that optimizes at least one operating parameter of a vehicle based on a physiological state of an occupant of the vehicle, includes a sensor that senses a physiological condition of the occupant and that outputs data based on the sensed physiological condition. The transportation system further includes an artificial intelligence system that receives and processes the data to determine an emotional state of the occupant, and optimizes, for achieving a favorable emotional state of the occupant, the at least one operating parameter of the vehicle in response to detecting the emotional state of the occupant.

A computer includes a processor and a memory storing instructions executable by the processor to receive a time series of vectors from a sensor, determine a weighted moving mean of the vectors, determine an inverse covariance matrix of the vectors, receive a current vector from the sensor, determine a squared Mahalanobis distance between the current vector and the weighted moving mean, and output an indicator of an anomaly with the sensor in response to the squared Mahalanobis distance exceeding a threshold. The squared Mahalanobis distance is determined by using the inverse covariance matrix.

A threat response system including one or more UAVs for protecting a vehicle of an owner. The one or more UAVs may be located in a docking station within a trunk of the vehicle. The threat response system may launch the one or more UAVs in response to detecting an intruder in a vicinity of the vehicle. The threat response system may further classify an occurring event by analyzing data received from the one or more UAVs with a machine learning system trained on data in an event database, with the event database storing one or more predicted events each corresponding to an event where the vehicle is vandalized, broken into, and/or stolen by the intruder. The threat response system may further select one or more UAV response operations from a response plan database to address the occurring event based on the classification of the occurring event.

A threat response system including one or more UAVs for protecting a vehicle of an owner. The one or more UAVs may be located in a docking station within a trunk of the vehicle. The threat response system may launch the one or more UAVs in response to detecting an intruder in a vicinity of the vehicle. The threat response system may further classify an occurring event by analyzing data received from the one or more UAVs with a machine learning system trained on data in an event database, with the event database storing one or more predicted events each corresponding to an event where the vehicle is vandalized, broken into, and/or stolen by the intruder. The threat response system may further select one or more UAV response operations from a response plan database to address the occurring event based on the classification of the occurring event.

Disclosed is a system for controlling navigation of a robot in a dynamic environment based on heuristic learning. The system comprising: a heuristic learning unit configured to determine at least a preferred path, a preferred position, and a preferred orientation for the robot based on human robot interaction (HRI) during navigation of the robot and a path scaling factor and a navigation control unit configured to generate at least one of: an optimal path, an optimal position, and an optimal orientation, for navigation of the robot in the dynamic environment in real-time, during navigation of the robot, based on at least one of: the preferred path, the preferred position, the preferred orientation or a previous navigation data associated with the robot.

A computer-implemented method of using telematics data associated with an originating vehicle at a destination vehicle is provided. The method may include receiving telematics data associated with the originating vehicle by (1) a mobile device or (2) a smart vehicle controller associated with a driver or vehicle. The mobile device or smart vehicle controller may analyze the telematics data received to determine that (i) a travel event exists, or (ii) that a travel event message or warning is embedded within the telematics broadcast received. If the travel event exits, the method may include automatically taking a preventive or corrective action, at or via the mobile device or smart vehicle controller, which alleviates a negative impact of the travel event on the driver or vehicle to facilitate safer or more efficient vehicle travel. Insurance discounts may be provided to insureds based upon their usage of the risk mitigation or prevention functionality.

A computer-implemented method of using telematics data associated with an originating vehicle at a destination vehicle is provided. The method may include receiving telematics data associated with the originating vehicle by (1) a mobile device or (2) a smart vehicle controller associated with a driver or vehicle. The mobile device or smart vehicle controller may analyze the telematics data received to determine that (i) a travel event exists, or (ii) that a travel event message or warning is embedded within the telematics broadcast received. If the travel event exits, the method may include automatically taking a preventive or corrective action, at or via the mobile device or smart vehicle controller, which alleviates a negative impact of the travel event on the driver or vehicle to facilitate safer or more efficient vehicle travel. Insurance discounts may be provided to insureds based upon their usage of the risk mitigation or prevention functionality.

Systems and methods for managing an execution of an action strategy by an autonomous system are disclosed. The action strategy comprises a series of actions to be performed by the autonomous system to accomplish a corresponding active objective. The method comprises identifying, by a processor of the autonomous system, an active objective to be accomplished by the autonomous system, the active objective describing a hierarchy of actions to be performed to accomplish the corresponding active objective. The method comprises generating, by the processor, an action strategy from the hierarchy of actions of the active objective, the actions of the action strategy corresponding to the actions of the hierarchy of actions of the active objective and executing the action strategy. Upon completion of an execution of an action, the processor provides data comprising information relating to a state of completion of the action strategy.