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.

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).

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.

An information processing apparatus includes an acquirer that acquires first information indicating a remaining charge of a first battery that is detachably mounted in an electric vehicle and supplies electric power for traveling of the electric vehicle and second information regarding a destination of the electric vehicle, a travel route predictor that predicts a travel route of the electric vehicle based on the second information acquired by the acquirer, and a determiner that refers to map information indicating, on a map, positions of a plurality of charging stations at which a second battery to be rented to a user is charged and determines a charging station at which the second battery is rented as a replacement for the first battery mounted in the electric vehicle based on the travel route predicted by the travel route predictor.

A localization method and system for mobile remote inspection and/or manipulation tools in confined spaces are provided. The system comprises a mobile remote inspection and/or manipulation device including a carrier movable within the confined space and an inspection and/or manipulation tool, such as an inspection camera, pose sensors arranged on the movable carrier for providing signals indicative of the position and orientation of the movable carrier, and distance sensors arranged on the movable carrier for providing signals indicative of the distance to interior surfaces of the confined space. The localization method makes use of probalistic sensor fusion of the measurement data provided by the pose sensors and the distance sensors in order to precisely determine the actual pose of the movable carrier and localize data generated by the inspection and/or manipulation tool.

A machine control system includes an agricultural work machine having an ECU coupled via a system bus to control engine functions, a GPS receiver, data collector, and specialized guidance system including a stored program. The data collector captures agricultural geospatial data including location data for the work machine and data from the ECU, and executes the stored program to: (a) capture geometries of the farm; (b) capture agricultural geospatial data; (c) automatically classify the agricultural geospatial data using the geometries of the farm, into activity/event categories including operational, travel, and ancillary events; (d) aggregate the classified data to create geospatial data events; (e) match the geospatial data events to a model to generate matched events; (f) use the matched events to generate actionable information for the working machine in real time or near real-time; and (g) send operational directives to the agricultural work machine based on the actionable information.

A method of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

Disclosed herein are a method of localization by synchronizing multi sensors and a robot implementing the same. The robot according to an embodiment includes a controller that, when a first sensor acquires first type information, generates first type odometry information using the first type information, that, at a time point when the first type odometry information is generated, acquires second type information by controlling a second sensor and then generates second type odometry information using the second type information, and that the robot by combining the first type odometry information and the second type odometry information.

Described are systems and methods to utilize images to determine the position and/or orientation of a vehicle (e.g., an autonomous ground vehicle) operating in an unstructured environment (e.g., environments such as sidewalks which are typically absent lane markings, road markings, etc.). The described systems and methods can determine the vehicle's position and orientation based on an alignment of annotated images captured during operation of the vehicle with a known annotated reference map. The translation and rotation applied to obtain alignment of the annotated images with the known annotated reference map can provide the position and the orientation of the vehicle.

The invention relates to a method for adjusting fully automatic vehicle guidance functions, which are realized by means of a vehicle system of a motor vehicle, during the operation of the motor vehicles in a predefined navigation environment. A stationary infrastructure device that communicates with the motor vehicles is associated with the navigation environment. Function limits of each vehicle guidance function are defined by means of limit operation parameters of the vehicle guidance function. Current traffic situation information describing dynamic objects in the navigation environment is determined by the infrastructure device by means of environment sensors of the navigation environment. The current traffic situation information is used, together with a digital map describing stationary objects and properties of the navigation environment, to determine at least one piece of risk information for each motor vehicle.