A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.

The embodiments of present disclosure herein address unresolved problem of cognitive navigation strategies for a telepresence robotic system. This includes giving instruction remotely over network to go to a point in an indoor space, to go an area, to go to an object. Also, human robot interaction to give and understand interaction is not integrated in a common telepresence framework. The embodiments herein provide a telepresence robotic system empowered with a smart navigation which is based on in situ intelligent visual semantic mapping of the live scene captured by a robot. It further presents an edge-centric software architecture of a teledrive comprising a speech recognition based HRI, a navigation module and a real-time WebRTC based communication framework that holds the entire telepresence robotic system together. Additionally, the disclosure provides a robot independent API calls via device driver ROS, making the offering hardware independent and capable of running in any robot.

An autonomous moving apparatus control system including a range sensor, a reflection plate, and a control unit. The range sensor is installed in a cage of an elevator and detects a distance to an object by receiving reflected light of signal light applied to the object. The reflection plate is disposed in an elevator hall of a floor on which the elevator stops, and reflects the signal light. The control unit determines whether or not a mobile robot, which is an autonomous moving apparatus, can get on and off the elevator based on a detected distance, the detected distance being a distance to the reflection plate detected by the range sensor.

In an environment in which a plurality of second pedestrians moves along predetermined movement patterns, a plurality of movement routes Rw when a first pedestrian moves toward a destination point is recognized. Data, in which an environmental image indicating a visual environment in front of a virtual robot when the virtual robot moves along each of the movement routes and a moving direction command indicating a moving direction of the virtual robot are combined, is generated as learning data. In the environmental image, colors corresponding to time-series displacement behaviors of a moving object image region is applied to at least a portion of the moving object image region indicating a pedestrian (moving object) present around a robot. Model parameters of a CNN (action model) is learned using the learning data, and a moving velocity command for a robot is determined using a learned CNN.

An operating method for navigating a device in a dynamic environment is provided. The method includes building a map based on sensor data, localizing a position of the device on the map based on the sensor data, determining a first position of a moving object on the map based on the sensor data, determining a momentum of the moving object, determining a second position of the moving object on the map based on the determined momentum, and changing the position of the device based on the determined second position of the moving object and a position of at least one obstacle.

A method for controlling a robot is provided. The method includes the steps of: determining a target robot to travel to a first loading station among a plurality of robots, on the basis of information on a location of the first loading station and a task situation of each of the plurality of robots, when a first transport target object is placed at the first loading station; and determining a travel route of the target robot with reference to information on the location of the first loading station and a location of a first unloading station associated with the first transport target object.

Disclosed is a food moving stage. The food moving stage includes a first plate having a plurality of concavities formed on an upper surface; a second plate of which a lower surface is disposed to face the upper surface of the first plate; a first support member connected to the upper surface of the first plate and disposed between the first plate and the second plate; a second support member connected to the lower surface of the second plate and disposed between the first plate and the first support member; a plurality of rolling members disposed on each of the plurality of concavities and are in contact with the lower surface of the second plate; and an elastic friction member disposed on the upper surface of the second support member, and based on the second plate and the second support member rising, compressed and deformed by a lower surface of the first support member.

An elevator control method is implemented by a computer and comprises: receiving a board request from a board-standby robot; determining a priority of the board-standby robot based on information relating to the board-standby robot and operation information of the elevator; and controlling operation of an elevator car based on the priority of board-standby robots and the priority of on-board robots within the elevator car.

A robot control model learning device (10) performs, by using state information indicating the state of a robot which autonomously travels to a destination in a dynamic environment as an input, reinforcement learning to obtain a robot control model for selecting and outputting a behavior in accordance with the state of the robot from among a plurality of behaviors including an intervention behavior for intervening in the environment, while using the number of times the intervention behavior has been performed as a minus reward.

Provided are a mobile object control apparatus and a mobile object control method that provide an improved method of presenting a travel route during movement of the mobile object. A degree of relation with an individual in the vicinity of a mobile object is decided, and a travel mode that is associated with a process performed by a driving section that moves the mobile object and a process performed by an output section that outputs a representation for presenting a travel route of the mobile object is set on the basis of the degree of relation.