A method of broadcasting a signal to marshal a plurality of autonomously operated vehicles including the signal broadcasted to the plurality of autonomously operated vehicles, the establishment of a secure data connection with each of the plurality of autonomously operated vehicles based on the signal, the determination of a disruption in the secure data connection to one or more vehicles of the plurality of autonomously operated vehicles, and the causation of the one or more vehicles of the plurality of autonomously operated vehicles to initiate an action based on the disruption in the secure data connection.

A method of broadcasting a signal to marshal a plurality of autonomously operated vehicles including the signal broadcasted to the plurality of autonomously operated vehicles, the establishment of a secure data connection with each of the plurality of autonomously operated vehicles based on the signal, the determination of a disruption in the secure data connection to one or more vehicles of the plurality of autonomously operated vehicles, and the causation of the one or more vehicles of the plurality of autonomously operated vehicles to initiate an action based on the disruption in the secure data connection.

Disclosed is a smart snow removal method, applied to a snow removal robot. The method includes: obtaining, through global positioning system real-time kinematic (GPS-RTK) positioning technology, a latitude and longitude coordinates of a target snow throwing area and a target snow removal area, to generate a snow removal map; rasterizing the snow removal map; converting the snow removal map into a potential field: starting from a grid located in the target snow throwing area, and assigning, through a breadth-first search (BFS) algorithm, a potential energy value to the grid located in the target snow removal area in an outward diffusion manner; wherein the potential energy value increases with an increase in a number of diffusion layers; and controlling the snow removal robot to travel on an uncleaned grid with a highest current potential energy value one by one, and performing the snow removal operation on an arrived grid.

Disclosed is a smart snow removal method, applied to a snow removal robot. The method includes: obtaining, through global positioning system real-time kinematic (GPS-RTK) positioning technology, a latitude and longitude coordinates of a target snow throwing area and a target snow removal area, to generate a snow removal map; rasterizing the snow removal map; converting the snow removal map into a potential field: starting from a grid located in the target snow throwing area, and assigning, through a breadth-first search (BFS) algorithm, a potential energy value to the grid located in the target snow removal area in an outward diffusion manner; wherein the potential energy value increases with an increase in a number of diffusion layers; and controlling the snow removal robot to travel on an uncleaned grid with a highest current potential energy value one by one, and performing the snow removal operation on an arrived grid.

A method of operating a robot in a manner to avoid obstacles is disclosed. The robot is any type of following vehicle, configured to follow a leader. As the robot identifies a potential collision, specific actions are initiated to avoid such a collision while still following the leader, albeit not on a standard following path. Based on the following robot determining that the following robot is clear of the obstacle, the following robot will begin returning to the standard following position. As the robot returns to the standard following path, the specific action is completed and standard following continues. The robot may use various sensors to determine variables of the robot and obstacle, such as convergence velocity, collision distance, available space, and collision time.

A method of operating a robot in a manner to avoid obstacles is disclosed. The robot is any type of following vehicle, configured to follow a leader. As the robot identifies a potential collision, specific actions are initiated to avoid such a collision while still following the leader, albeit not on a standard following path. Based on the following robot determining that the following robot is clear of the obstacle, the following robot will begin returning to the standard following position. As the robot returns to the standard following path, the specific action is completed and standard following continues. The robot may use various sensors to determine variables of the robot and obstacle, such as convergence velocity, collision distance, available space, and collision time.

A drone which is an unmanned moving object according to an embodiment includes a flight controller that controls the driving of the drone, a first communication unit that communicates with an operation device that remotely controls the drone, and a second communication unit that receives unique information sent out from another moving object for identifying presence and/or a position of the other moving object.

A drone which is an unmanned moving object according to an embodiment includes a flight controller that controls the driving of the drone, a first communication unit that communicates with an operation device that remotely controls the drone, and a second communication unit that receives unique information sent out from another moving object for identifying presence and/or a position of the other moving object.

A method and system for navigating a robot 100 are provided herein. In an embodiment, the method comprises: detecting on-site reference features of the robot's location when the robot is traversing in an environment; identifying objects of interest 180 from the detected on-site reference features; deriving a semantic data for each object of interest 180 from the on-site reference features; generating a semantic cost map 166 based on the semantic data of the objects of interest 180, the semantic cost map 166 representing cost of traversing in the environment; and navigating the robot 100 based on the semantic cost map 166.

A method and system for navigating a robot 100 are provided herein. In an embodiment, the method comprises: detecting on-site reference features of the robot's location when the robot is traversing in an environment; identifying objects of interest 180 from the detected on-site reference features; deriving a semantic data for each object of interest 180 from the on-site reference features; generating a semantic cost map 166 based on the semantic data of the objects of interest 180, the semantic cost map 166 representing cost of traversing in the environment; and navigating the robot 100 based on the semantic cost map 166.