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.

Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

A utility vehicle includes: a travel structure including a front wheel, a rear wheel, a steering structure mounted to the front wheel, and a drive source that drives the front wheel and/or the rear wheel; circuitry that controls the travel structure to effect autonomous travel without manned operation in a given travel area; a route setter that sets a travel route for the autonomous travel; a vehicle location detector that detects a location of the utility vehicle; and a target detector that detects a monitoring target in the travel area. In case that the monitoring target is detected at a location during the autonomous travel, the circuitry stores the location of the monitoring target as history information. The route setter sets a reference point at the location where the monitoring target was detected and sets the travel route based on the reference point.

The present disclosure provides a method for determining a garbage sweeping point in a smart city and an Internet of Things system. The method is implemented by the Internet of Things system. The method includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The method is performed through the management platform and includes: obtaining monitoring information on at least one road within a road network area, identifying a target object on the at least one road, the target object including at least garbage to be processed; determining, based on an identification result, information related to the target object, the information related to the target object including at least a garbage volume of the garbage to be processed; and determining at least one target garbage sweeping point based on the information related to the target object.

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.

An evacuation running assistance system includes a road shoulder evacuation possibility determiner to determine if an own vehicle can be evacuated to a road shoulder; an own vehicle situation determiner to determine a current situation of an own vehicle in accordance with a time limit and the road shoulder evacuation possibility, a controller to control an own vehicle in accordance with the situation of the own vehicle; and a road shoulder evacuation possibility road determiner to acquire evacuation space information from a past running history of the own vehicle. The own vehicle situation determiner determines that the own vehicle is in the situation to be controlled to perform the on-lane stopping when the road shoulder evacuation possibility road determiner does not determine within the provisional time that the evacuation of the own vehicle to the road shoulder is possible.

The present disclosure is directed to mobile correctional facility robots and systems and methods for coordinating mobile correctional facility robots to perform various tasks in a correctional facility. The mobile correctional facility robots can be used to perform many of the tasks traditionally assigned to correctional facility guards to help reduce the number of guards needed in any given correctional facility. When cooperation is employed among multiple mobile correctional facility robots to execute tasks, a central controller can be used to coordinate the efforts of the multiple robots to improve the performance of the overall system of robots as compared to the performance of the robots when working in uncoordinated effort to execute the tasks.

A display control device includes an image receiving unit that receives an image captured by an imaging device included in a work machine and a display control unit that processes the image based on a movement amount of the work machine in receipt delay time of the image, and generates a display signal.

A system, a method and a computer program product are provided for evaluating quality of data, such as sensor data and map data, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain first sensor features of the first sensor data associated with a road object in a first geographic region, first map features of the first map data associated with the road object and ground truth data associated with the road object. The processor may be configured to generate the machine learning model by configuring the ground truth data and calculating first information scores for each of the first sensor features and the first map features by recursively splitting each of the first sensor features and the first map features.

The present disclosure provides a system, a method and an apparatus for position-based parking of a vehicle, capable of solving the problem in the related art that an unmanned vehicle cannot be parked at a specified position accurately in an environment with a weak GPS signal. The method includes, during position-based parking of a vehicle at a predetermined position: receiving, by a communication device of the vehicle, posture data from a roadside device located within a predetermined range from the predetermined position; deciding, by a vehicle-mounted processing device of the vehicle, whether received posture data satisfies a predetermined positioning rule; determining posture adjustment data for the vehicle when the received posture data does not satisfy the predetermined positioning rule; and controlling the vehicle to perform a posture adjustment operation based on the posture adjustment data.