In some embodiments, a computer-implemented method for managing resources of a fleet of unmanned aerial vehicles (UAVs) is provided. A computing system creates a mission record and one or more candidate records. Each candidate record of the one or more candidate records represents one or more resources for accomplishing a mission represented by the mission record. The computing system adds a mission node representing the mission record to a resource competition network graph (RCN graph). The computing system adds one or more candidate nodes representing the one or more candidate records to the RCN graph. The computing system determines an optimized allocation of candidate records to mission records using at least a subgraph of the RCN graph. A candidate record is determined to commit to a mission record, and the computing system updates the RCN graph to commit the candidate record to the mission record.

A control method and a control system based on a mobile robot are provided, which correspondingly obtain first feature information and second feature information according to a specific target object and a face region of the user present in region environment, and a delivery request message is sent to the mobile robot; the obstacle presence information is obtained during moving process of mobile robot along the moving path so as to adjust moving state of mobile robot; the corresponding third feature information is obtained according to the image of current region of mobile robot; whether the mobile robot performs an article unloading operation is controlled according to a comparison result between the third feature information and the second feature information; the efficient transportation paths are chose according to the moving path of the mobile robot, and the real-time situation of the destinations may be verified.

A method includes navigating, by a UAV, to a delivery location in an environment; capturing, by at least one sensor on the UAV, sensor data representative of the delivery location; determining, based on the sensor data, a segmented point cloud of the delivery location, wherein the segmented point cloud defines a plurality of point cloud areas with corresponding semantic classifications; determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location; and based on determining that the pre-selected delivery point satisfies the condition, initiating, by the UAV, a payload delivery operation towards the pre-selected delivery point.

A centrally managed system for tuning a plurality of enhanced node-based logistics receptacles includes a backend server, a first enhanced node-based logistics receptacles, and a second node-based logistics receptacles. The backend server transmits a first setup message to a first bridge node, transmits a second setup message to a second bridge node, receives retrieved first event information from the first bridge node and retrieved second event information from the second bridge node, compares the retrieved first event information with a management profile and compares the retrieved second event information with the management profile, revises the management profile based upon the comparison of the retrieved first event information with the management profile and the comparison of the retrieved second event information with the management profile, and transmits an adjustment message that is based upon the revised management profile to the first bridge node.

A technique for avoiding a dynamic obstacle in a vicinity of UAV while the UAV flies a mission along a planned route includes: receiving a wireless radio signal containing information related to the dynamic obstacle in an audible speech format; translating the audible speech format to a text format; analyzing the information to determine an identity of the dynamic obstacle and determine whether the information relates to a flightpath, speed, or heading of the dynamic obstacle; in response to determining that the information relates to the flightpath, speed, or heading of the dynamic obstacle, determining an updated flightpath, speed or heading for the dynamic obstacle; comparing the updated flightpath, speed, or heading to the planned route; and in response to detecting a conflict between the dynamic obstacle and the UAV based on the comparing, performing an action by the UAV to avoid the conflict with the dynamic obstacle.

A method includes determining, by an unmanned aerial vehicle (UAV), a position of an autoloader device for the UAV; based on the determined position of the autoloader device, causing the UAV to follow a descent trajectory in which the UAV moves from a starting position to a first nudged position in order to deploy a tethered pickup component of the UAV to a payout position on an approach side of the autoloader device; deploying the tethered pickup component of the UAV to the payout position; causing the UAV to follow a side-step trajectory in which the UAV moves laterally to a second nudged position in order to cause the tethered pickup component of the UAV to engage the autoloader device; and retracting the tethered pickup component of the UAV to pick up a payload from the autoloader device.

A system for self-executing enhanced interaction with a node-based logistics receptacle. The system includes a wireless accessory sensor node disposed on the node-based logistics receptacle to monitor storage receptacle components of the node-based logistics receptacle to generate sensor data. The system includes a bridge node disposed on the node-based logistics receptacle that uploads information related the sensor data, detects an external device separate from the node-based logistics receptacle, communicates with the external device to establish a smart contract based connection that provides an interaction privilege, interfaces with the external device according to the interaction privilege, and transmits an update message to the backend server that corresponds to at least a portion of the uploaded information related to the sensor data and information related to interfacing with the external device.

A dynamic learning server-based logistics system includes a node-based logistics receptacle operative to receive a delivery item as part of a logistics transaction and also includes a backend server maintaining a management profile related to operation of the node-based logistics receptacle. The node-based logistics receptacle includes a plurality of monitored receptacle components, a wireless accessory sensor node having a plurality of sensors, and a bridge node operative to retrieve event information from the wireless accessory sensor node. The backend server receives the retrieved event information, compares the retrieved event information with the management profile, identifies a threshold change condition from the comparison, dynamically revises the management profile when the threshold change condition is identified, and transmits an adjustment message to the bridge node. The adjustment message is based upon the revised management profile, and the adjustment message initiates a timing change to operation of the bridge node.

Method, apparatuses and computer program products for training machine learning models for logistics robots and routing the robots are disclosed. A method of training an ML model involves providing a route to a logistics robot, obtaining route issue indications from the robot when it fails to traverse the route as expected, and associating map objects with the issue locations. The trained model can then be used to determine the likelihood of route issues for a specific logistics robot type based on the presence of certain map objects along the route. The disclosure further involves calculating route penalty value(s) based on the likelihood and updating the route accordingly, including selection of a logistics robot type for the route.

In a delivery system S including an UAV 1 and a management server 2, the UAV 1 controls at least a position and/or an orientation of the UAV 1 so that a package placed in a release location and a peripheral region of the package fall within an angle of view of a camera. And then, the UAV 1 saves, as an image that proves delivery completion of the package, an image of the peripheral region including the package captured by the camera in a storage unit 15 of the UAV 1 or a storage unit 22 of the management server 2.