A management device includes a memory configured to store map information for each area where a robot, which autonomously travels outdoors and indoors, travels, and processing circuitry configured to collect external information, receive a current position of the robot or a travel route of the robot from a control device controlling the robot, detect an occurrence of an event and an event occurrence area where the event occurs, based on the external information, identify the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot, determine information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information, and recommend information.

A management device includes a memory configured to store map information for each area where a robot, which autonomously travels outdoors and indoors, travels, and processing circuitry configured to collect external information, receive a current position of the robot or a travel route of the robot from a control device controlling the robot, detect an occurrence of an event and an event occurrence area where the event occurs, based on the external information, identify the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot, determine information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information, and recommend information.

A method comprising receiving, via a server, a distress signal from a first drone determining among a plurality of drones performing mission tasks, is experiencing distress and has entered a mitigation procedure responsive to the mitigation procedure to include the content received from the first drone; and assigning, via the server, a new drone to replace the first drone as a follower drone or a leader drone.

A method comprising receiving, via a server, a distress signal from a first drone determining among a plurality of drones performing mission tasks, is experiencing distress and has entered a mitigation procedure responsive to the mitigation procedure to include the content received from the first drone; and assigning, via the server, a new drone to replace the first drone as a follower drone or a leader drone.

A distributed control system with supplemental attitude adjustment including an aircraft control having an engaged state and a disengaged state. The system also including a plurality of flight components and a plurality of aircraft components communicatively connected to the plurality of flight components, wherein each aircraft component is configured to receive an aircraft command and generate a response command directing the flight components as a function of supplemental attitude. The supplemental attitude based at least in part on the engagement datum and generating a supplemental attitude includes choosing a position supplemental attitude if the aircraft control is disengaged and choosing a velocity supplemental attitude if the aircraft control is engaged. In generating the response command, the aircraft attitude is combined with the supplemental attitude to obtain an aggregate attitude, and the aircraft component is configured to generate the response command based on the aggregate attitude.

Systems, methods, and devices are provided for controlling an unmanned aerial vehicle (UAV) associated with flight response measures. The flight response measure may be generated by assessing one or more flight-restriction strips, assessing at least one of a location or a movement characteristic of the UAV relative to the one or more flight-restriction strips, and directing, with aid of one or more processors, the UAV to take one or more flight response measures based on at least one of the location or movement characteristic of the UAV relative to the one or more flight-restriction strips.

An apparatus of controlling flight of an aerial vehicle includes a memory storing a computer program code, and one or more processors, individually or collectively, configured to execute the computer program code to: collect, while the aerial vehicle traverses a flight path, a set of images corresponding to different fields of view of an environment around the aerial vehicle, construct a map of the environment using the set of images, and control the aerial vehicle to return along a return path using the map of the environment.

A system and/or a method for controlling the movement of a vehicle in a constrained environment subject to a disturbed vehicle model including uncertainty on the dynamics governing the movement of the vehicle, collects a feedback signal indicative of a state of the vehicle and a setpoint for controlling the vehicle according to a task and determine a robust invariant set centered on the setpoint for the operation of the vehicle in an unconstrained environment using the disturbed vehicle model. The robust invariant set is inflated equally in all directions until a termination condition defined by the constraint environment is met to produce a safe invariant set enabling control of the operation of the vehicle according to the task while maintaining the state of the vehicle within the safe invariant set.

A method of training a machine learning, ML, algorithm to control a watercraft is described. The watercraft is a submarine or a submersible submerged in water. The method is implemented, at least in part, by a computer, comprising a processor and a memory, aboard the watercraft. The method comprises: obtaining training data including respective sets of environmental parameters and corresponding actions of a set of communicatively isolated watercraft, including a first watercraft; and training the ML algorithm comprising determining relationships between the respective sets of environmental parameters and the corresponding actions of the watercraft of the set thereof. A method of controlling a watercraft by a trained ML algorithm is also described.

A method of training a machine learning, ML, algorithm to control a watercraft is described. The watercraft is a submarine or a submersible submerged in water. The method is implemented, at least in part, by a computer, comprising a processor and a memory, aboard the watercraft. The method comprises: obtaining training data including respective sets of environmental parameters and corresponding actions of a set of communicatively isolated watercraft, including a first watercraft; and training the ML algorithm comprising determining relationships between the respective sets of environmental parameters and the corresponding actions of the watercraft of the set thereof. A method of controlling a watercraft by a trained ML algorithm is also described.