A control device includes a master storage part that stores map data including obstacle information and environmental parameter information on an environmental parameter that may influence an object to be transported, and a transport path determination part that, based on a transport instruction and the map data, determines a transport path according to the object to be transported, which is indicated by transported object information included in the transport instruction, in consideration of the environmental parameter that may influence the object to be transported.

A ship monitoring system includes: a shipboard information processing apparatus and including a shipboard communication unit and an information acquisition unit; and a support information processing apparatus and including: a support side communication unit capable of communicating with the shipboard communication unit; a time lag identification unit; and a state prediction unit. The time lag identification unit identifies a time from transmission of the ship motion information from the shipboard communication unit to reception of the ship motion information by the support side communication unit as a reception time lag. The state prediction unit inputs the ship motion information and the reception time lag to a ship motion model related to the ship motion of the ship to predict a motion state of the ship ahead of a motion state of the ship indicated by the ship motion information by a period of time based on the reception time lag.

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.

In order to provide a method for controlling conveyor vehicles of a conveying system which permits an energy-efficient and cost-efficient operation of said conveying system, it is proposed that the method comprises: Making available a plurality of conveyor vehicles of the conveying system; Specifying a conveying job for conveying one or more conveyed objects; Determining a conveying job data set for controlling a conveyor vehicle when performing the conveying job; Carrying out the conveying job.

Aspects of the subject disclosure may include, for example, a method in which a processing system determines that performance of a service is required at a first location in a smart community; identifies a robot to be transported, and determines whether physical transport or virtual transport is to be performed for the robot. If physical transport is to be performed, the system initiates communication with a robot transport controller to schedule the physical transport to the first location from a different second location. If virtual transport is to be performed, the processing system initiates communication with a radio access network (RAN) to schedule the virtual transport; the virtual transport includes configuring the robot at the first location. Other embodiments are disclosed.

A ship monitoring system includes: a shipboard information processing apparatus and including a shipboard communication unit and an information acquisition unit; and a support information processing apparatus and including: a support side communication unit capable of communicating with the shipboard communication unit; a time lag identification unit; and a state prediction unit. The time lag identification unit identifies a time from transmission of the ship motion information from the shipboard communication unit to reception of the ship motion information by the support side communication unit as a reception time lag. The state prediction unit inputs the ship motion information and the reception time lag to a ship motion model related to the ship motion of the ship to predict a motion state of the ship ahead of a motion state of the ship indicated by the ship motion information by a period of time based on the reception time lag.

One example method includes receiving datasets based on one or more instances of sensor data that are received from one or more sensors. The sensor data is associated with an aggregate fragility level that indicates how fragile one or more packages being transported by a movable edge node in an edge environment are. Features that are based on the datasets are extracted. Based on the extracted features, events that indicate anomalous driving patterns for the movable edge node are determined. In response to determining the events, an alarm based on a predetermined threshold that is based on the aggregate fragility level is generated.

Aspects of the subject disclosure may include, for example, a method in which a processing system configures one or more robots to perform tasks in an establishment, and assigns to the robots privileges and/or priorities in accordance with a policy. The method also includes detecting a situation in the establishment requiring performance of a task; facilitating the performance of the task by at least one of the robots; and dynamically reprogramming at least one of the robots in response to the situation to perform a specialized task to address the situation. Other embodiments are disclosed.

The automated pre-flight inspection of an unmanned aerial vehicle (UAV) uses a UAV and a dock. The UAV includes one or more cameras, one or more sub-systems, and a frame. The dock includes one or more processors, one or more memories, and one or more sensors configured for use with an automated pre-flight inspection of the UAV while the UAV is located at the dock. The one or more processors are configured to execute instructions stored in the one or more memories to perform the automated pre-flight inspection using the one or more sensors to produce output representing operational states of the one or more cameras, the one or more sub-systems, and one or more portions of the frame. The output is transmitted for display at a user device associated with the UAV.

Disclosed is an autonomous mobile service robot system for recognizing an automatic door and, in more detail, an autonomous mobile service robot system for recognizing an automatic door, the autonomous mobile service robot system enabling an autonomous mobile service robot to recognize an automatic door during moving, pass through the automatic door without an error in determination, and autonomously drive safely and stably for services by recognizing and defining information about an automatic door in simultaneously localization map-building (SLAM) of an autonomous mobile service robot that is operated for multiple purposes so that an accurate map considering location information of the automatic door is built.