The present technology relates to a control device, a control method, and a program that make it possible for objects that are capable of moving autonomously to move in corporation with each other. The control device according to one aspect of the present technology moves, in response to an action made by a user on a predetermined object among multiple objects that are capable of moving autonomously, an object corresponding with the predetermined object. The present technology can be applied to a control device that controls mobile robots.

A vehicle includes an on-board controller. A position of the vehicle is detected by a positioning sensor and a CPU of the on-board controller Based on the position of the vehicle and area information stored in a memory, the CPU determines whether the vehicle is in a free driving zone, an alternative driving zone, or a remote driving zone. The CPU sets a driving mode to either one of a free driving mode, an alternative driving mode, and a remote driving mode based on a signal of the driving mode of the vehicle inputted by a main key of a vehicle main body and information indicating which driving zone the vehicle is in. A CPU of the vehicle main body controls an operation of the vehicle in accordance with the set driving mode. The on-board controller may be provided by a mobile terminal.

A movable body control system (SYS) includes first and second movable bodies (1#1, 1#2) that are movable in a predetermined area (TA) in which a wireless communication network (NW) is built; and a control apparatus (3) for controlling the first and second movable bodies through the wireless communication network, the control apparatus includes: a storage unit (32) for storing a first communication quality information that indicates a communication quality in the predetermined area when the first movable body exists in each of a plurality of different locations in the predetermined area; a generation unit (311) for generating, based on the first communication quality information, a target moving route (TGT#2) that allows the second movable body to move while avoiding a first low quality location (DA_low1) at which the communication quality does not reach a desired quality due to the first movable body; and a control unit (312) for controlling the second movable body so that the second movable body moves along the target moving route.

Transporters and methods for transporting an object. The transporter includes a carrier comprising a plurality of coupling members; a support assembly adapted to support the carrier; and a plurality of automatic guided vehicles configured to obtain kinematic information from a leading automatic guided vehicle of the plurality of automatic guided vehicles. Each of the plurality of automatic guided vehicles includes a carrier connecting member coupled to the respective coupling member of the carrier to enable the carrier to move with the plurality of automatic guided vehicles; and a patrol assembly adapted to enable the respective automatic guided vehicle to move along the predetermined path.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

An autonomous vehicle includes an array of sensors, a processor, and a switch. The array of sensors generate sensor data related to one or more objects in an external environment of the autonomous vehicle and the processor determines an environmental context. The switch transfers the sensor data from the array of sensors to the processor, where the switch is configured to: (a) receive first sensor data from a first sensor group of the array of sensors; (b) receive second sensor data from a second sensor group of the array of sensors; (c) determine an order of transmission of the first sensor data over the second sensor data in response to the environmental context; and (d) transmit the first sensor data to the processor prior to transmitting the second sensor data based on the order of transmission.

A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.

Methods, systems, and computer programs are presented for executing a mission by an autonomous device to inspect an asset. One method includes an operation for obtaining a workflow. The workflow includes operations to be executed during a mission to be performed by a robot and a destination for sending data resulting from the mission. The method further includes an operation for generating a package after completion of the mission associated with the workflow. The package is self-contained and comprises information obtained during the mission that enables generation of results. The package comprises sensor information collected by one or more sensors, telemetry information obtained by the robot, information about assets associated with the mission, software version identifier for the package generation, and routing information for transmitting the package to the destination. The method further includes an operation for analyzing the information of the package to determine results for the mission.

Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.

A robotic cleaning system may include a robotic cleaner configured to generate a map of an environment and a mobile device configured to communicatively couple to the robotic cleaner, the robotic cleaner configured to communicate the map to the mobile device. The mobile device may include a camera configured to generate an image of the environment, the image comprising a plurality of pixels, a display configured to display the image and to receive a user input while displaying the image, the user input being associated with one or more of the plurality of pixels, a depth sensor configured to generate depth data that is associated with each pixel of the image, an orientation sensor configured to generate orientation data that is associated with each pixel of the image, and a mobile controller configured to localize the mobile device within the map using the depth data and the orientation data.