An assembly is configured for connection to an unmanned aerial vehicle (UAV) and comprises a plurality of emulator devices each configured for attachment to the UAV and a plurality of first connection tethers each configured to operably couple a respective one of the plurality of emulator devices to the UAV at a respective spacing from the UAV. The emulator devices each comprise an emulation component configured to provide, to a target detection system, a characteristic associated with a respective type of airborne object. The plurality of respective first connection tethers each comprises material that does not substantially reflect RF energy. During flight of the UAV, when the assembly is connected, each respective emulator device maintains the respective spacing from the UAV and emulates the characteristic to the target detection system, such that the assembly emulates, to the target detection system, a plurality of airborne objects.

An AI based system and method for managing heterogeneous network-agnostic swarm of robots is disclosed. The method includes receiving a set of commands from a human machine interface associated with one or more electronic devices, determining one or more robotic capabilities associated with autonomous robot and capturing one or more positional parameters by using one or more sensors. The method includes broadcasting the one or more robotic capabilities and the one or more positional parameters to each of the one or more autonomous robots and determining one or more situational parameters associated with the one or more autonomous robots. Furthermore, the method includes detecting one or more targets and allocating the one or more tasks and the detected one or more targets among the one or more autonomous robots.

An AI based system and method for managing heterogeneous network-agnostic swarm of robots is disclosed. The method includes receiving a set of commands from a human machine interface associated with one or more electronic devices, determining one or more robotic capabilities associated with autonomous robot and capturing one or more positional parameters by using one or more sensors. The method includes broadcasting the one or more robotic capabilities and the one or more positional parameters to each of the one or more autonomous robots and determining one or more situational parameters associated with the one or more autonomous robots. Furthermore, the method includes detecting one or more targets and allocating the one or more tasks and the detected one or more targets among the one or more autonomous robots.

The disclosure generally relates to method and system for autonomous vehicles management in an operating environment. The method may include sending a route plan in response to a request received by an autonomous vehicle from amongst a plurality of autonomous vehicles in an operating environment including one or more devices, wherein the one or more device comprises device or device space. The method may further include categorizing the route plan into path segments, the path segments including a first path segment including one or more nodes from the plurality of nodes being inside a device zone of the one or more devices and a second path segment including one or more nodes from the plurality of nodes being outside the device zone of the one or more devices. The method may further include determining a type of path segment from the categorized path segments for each of the node in the navigation segment and annotating the first node of the first path segment with a second precondition based on determination of a destination node from the one or more nodes and the second precondition includes performing a hard lock action of the first device for the autonomous vehicle traversing the first path segment to the destination node.

The operation of container handling vehicles and remotely operated specialized container handling vehicles are controlled by a method such that total elapsed time or waiting time cost for transferring specified storage containers between them is minimal or optimal. The specialized container handling vehicles are operating at a level below an automated storage and retrieval system having a framework structure defining a storage grid for storing storage containers in grid cells. The storage containers are stored and retrieved by container handling vehicles running on top of the storage grid. At least one operational controller, which is in communication with a first type of controller in each container handling vehicle and a second type of controller in each specialized container handling vehicle, performs the followings steps: when a specified storage container is to be transferred from a storage column and its corresponding grid cell to a specialized container handling vehicle: assigning and instructing a container handling vehicle to pick up the storage container from the grid cell and lower it to a selected port, located at the lower end of an identified free delivery column by transmitting instructions to the first type of controller of the assigned container handling vehicle, where the port selected is included as a variable in a weighting function together with time and waiting cost to derive a trade-off of the container handling vehicle and the specialized container handling vehicle having the best matching travel time to the port; moving the container handling vehicle to the grid cell, picking up the storage container and transporting it to a grid cell of the delivery column where the port is; transmitting a signal to the specialized container handling vehicles comprising information of the selected port at a first location and when the storage container will be available at the port; based on responses from the specialized container handling vehicles, assigning and instructing a specialized container handling vehicle to retrieve the specified storage container at the selected port; moving the assigned specialized container handling vehicle to the selected port at the first location, picking up the storage container and bringing it to a second location; and/or, when a specified storage container is to be transferred to a grid cell, for storage in its corresponding storage column, by a specialized container handling vehicle: determining which port at a first location that is to be used, where the port selected is included as a variable in a weighting function together with time and waiting cost to derive a trade-off of the container handling vehicle

Embodiments of the present disclosure provides a method and a system for managing UAV data transmission in smart city based on IoT. The method includes obtaining requirement information of a user by a general platform of the management platform from the user platform through the service platform, and assigning the requirement information to a corresponding management sub-platform; determining different time domains and different airspaces of at least two UAVs performing missions by the management sub-platform based on the requirement information, wherein the different time domains may have an overlapping interval, and the different airspaces may have an overlapping interval; and controlling the at least two UAVs to perform the different missions in the different time domains and the different airspaces by the management platform, and collecting mission data corresponding to different missions.

Operating a network node to control a first Unmanned Aerial Vehicle (UAV) in a wireless telecommunications network can include determining a collision risk for the first UAV; determining a reporting rate for the first UAV based on the collision risk; and sending a reporting rate update message to the first UAV so as to cause the first UAV to report its position at the determined reporting rate. A method of operating an Unmanned Aerial Vehicle (UAV) in a wireless telecommunications network is also disclosed.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

Methods and systems for communicating between autonomous vehicles are described herein. Such communication may be performed for signaling, collision avoidance, path coordination, and/or autonomous control. An autonomous vehicle may determine an upcoming maneuver for the autonomous vehicle and identify a vehicle signal which is indicative of the upcoming maneuver. Then the autonomous vehicle may present the vehicle signal. After presenting the vehicle signal, the autonomous vehicle may perform the maneuver.

A first robot performs navigation to a predetermined first static or dynamic target location. A first set of velocity candidates is generated for the first robot based on the detection of a first set of one or more velocity obstacles. A first new velocity is selected from the first set of velocity candidates. The first robot is moved at a first velocity corresponding to the first new velocity. The first new velocity is a first desired velocity or a velocity closest to the first desired velocity when at least one velocity candidate of the first set of velocity candidates corresponds to a safe and reachable velocity for the first robot. The first new velocity is a minimum velocity possible by the first robot when each one of the first set of velocity candidates corresponds to a respective unsafe velocity for the first robot.