An embodiment provides unmanned aerial vehicles (UAVs) for infrastructure surveillance and monitoring. One example includes monitoring power grid components such as high voltage power lines. The UAVs may coordinate, for example using swarm behavior, and be controlled via a platform system. Other embodiments are described and claimed.

A method for exchanging data within a robotic system including at least three robotic units, each robotic unit having a first communication device with a short range communication reach for exchanging data with each other and a data storage. The method includes the acts of recognizing a necessity of a data transfer for a first robotic unit of the at least three robotic units, determining an area of interference of the short range communication reach of the first robotic unit and the short range communication reach of a second robotic unit of the at least three robotic units, the second robotic unit being out of the short range communication reach of the first robotic unit, determining a waypoint within the area of interference for a third robotic unit of the at least three robotic units, and commanding the third robotic unit to move to the waypoint.

A control device includes a communication unit, a restriction condition calculation unit, and a control unit. The communication unit receives, from a second robot, a current position of the second robot, and restriction-related information used for controlling the second robot based on positions of a first robot and the second robot at past times. The restriction condition calculation unit calculates restriction condition candidates indicating conditions of a range in which movement is possible, based on the current position and the restriction-related information received from the second robot. The restriction condition calculation unit also identifies a restriction condition having the most recent time among the calculated restriction condition candidates. The control unit controls the position of the first robot so as to move in a manner satisfying the restriction condition identified by the restriction condition calculation unit.

A system, including: a communication interface operable to receive sensor data related to a state of an object; object state estimation processor circuitry operable to estimate, based on the sensor data, a prospective probability of a degradation of the state of the object; and cobot fleet control processor circuitry operable to generate a command for either a transport cobot operable to transport the object, or another actor, to take proactive action to mitigate the prospective probability of the degradation of the state of the object.

A system, including: a communication interface operable to receive sensor data related to a state of an object; object state estimation processor circuitry operable to estimate, based on the sensor data, a prospective probability of a degradation of the state of the object; and cobot fleet control processor circuitry operable to generate a command for either a transport cobot operable to transport the object, or another actor, to take proactive action to mitigate the prospective probability of the degradation of the state of the object.

This application provides a fleet control method and apparatus, an electronic device, and a storage medium. The fleet control method is used for controlling a robot fleet and includes: determining a planned path of each robot in the robot fleet, where the planned path of each robot is used to indicate a movement path for the robot to move to a corresponding target storage location within a shelving unit region to execute a task; determining a following road segment in the planned path of each following robot based on the planned path of each robot, where the following road segment includes a road segment located on the ground and/or a road segment extending in a vertical direction; and sending the following road segment to a corresponding following robot.

An information generation method is an information output method executed by a mobile retail vehicle control device (information output device) and includes obtaining first information about a first time at which a first mobile object arrives at a first arrival point (first point); determining, based on the first information, a second point from which a second mobile object can arrive at the first arrival point at a second time within a predetermined period from the first time; and outputting instruction information for moving the second mobile object to the second point.

A center includes a vehicle related device, which is communicably connected with multiple in-vehicle devices, and a service related device communicably connected with a service providing unit. The vehicle related device generates, for each vehicle, a shadow using multiple pieces of vehicle data repeatedly acquired from the in-vehicle device of corresponding vehicle, and stores the generated shadow in a shadow storage unit. The service related device generates indices corresponding to respective shadows, each of which is assigned with the timing identification information, and stores the generated indices in an index storage unit provided in the service related device. The service related device acquires, from the index storage unit, one of the indices corresponding to a designated parameter, and the vehicle related device acquires one of the multiple pieces of vehicle data from the shadow storage unit using the one of the indices, which is acquired corresponding to the designated parameter.

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.

A method and system are provided for task allocation for a material handling system with autonomous mobile robots (AMR) operating within a material handling facility. The AMRs are configured to self-select tasks to perform utilizing a computer based warehouse execution system (WES) utilizing a pending workflow list of tasks to be performed within the material handling facility. The AMRs include onboard computers for communicating with the WES and to self-select tasks from the pending workflow list. The AMR may be directed to a prioritized task or task queue, and the WES may lock an AMR to a task or task queue until the task or tasks are performed, or until the AMR determines that the AMR should be reassigned or otherwise relieved of the task or task queue. The AMRs are thus adapted to independently self-select tasks, where the AMR and/or WES may enable the AMR to self-select tasks.