A vehicle adapted to be remotely driven via a wireless communication network comprises a capturing unit for capturing live video data of the vehicle's environment, a video encoding unit for video encoding the captured live video data, a transmission unit for transmitting the encoded live video data via the wireless communication network, and a control unit for controlling the video encoding unit and/or the transmission unit. The control unit controls the video encoding unit to optimize the video encoding of the captured live video data and/or to control the transmission unit to optimize the transmission of the encoded live video data. The controlling is based on one, two or all of: (i) pre-determined location information associated with a current location of the vehicle; (ii) real-time driving information associated with current driving parameters of the vehicle, and; (iii) real-time environment information associated with a current environment of the vehicle.

The present invention provides a self-location estimation method including: a first step of estimating the self-location of a moving body (1) from the detection information of a plurality of sensors (5) to (8) by using a plurality of algorithms (11) to (13); a second step of determining a weighting factor for each algorithm from one or more state quantities A, B and C, which are obtained by estimation processing for each of a plurality of algorithms, by using a trained neural network (14); and a third step of identifying, as the self-location of the moving body (1), a location obtained by synthesizing the self-locations, which have been estimated by the algorithms, by using weighting factors.

The present disclosure relates generally to autonomous machines (AMs) and more particularly to techniques for intelligently planning, managing and performing various tasks using AMs. A control system (referred to as a fleet management system or FMS) is disclosed for managing a set of resources at a site, which may include AMs. The FMS is configured to control and manage the AMs at the site such that tasks are performed autonomously by the AMs. An AM may directly communicate with another AM located on the site to complete a task without requiring to be in constant communication with the FMS during the performance of the task. The FMS is configured to use various optimization techniques to allocate resources (e.g., AMs) for performing tasks at the site. The resource allocation is performed so as to maximize the use of available AMs while ensuring that the tasks get performed in a timely manner.

The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.

A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

A robot is provided. The robot includes a camera, a driving unit, and a processor. The robot is configured to, if a plurality of users included in one group are identified in an image captured via the camera, acquire profile information of each of the plurality of users, based on the profile information, acquire group feature information including group type information of the group, priority information of the plurality of users, and preferred waypoint information of the one group, and control the driving unit to perform a route guidance function based on the group feature information and destination information.

A robot is provided. The robot includes a camera, a driving unit, and a processor. The robot is configured to, if a plurality of users included in one group are identified in an image captured via the camera, acquire profile information of each of the plurality of users, based on the profile information, acquire group feature information including group type information of the group, priority information of the plurality of users, and preferred waypoint information of the one group, and control the driving unit to perform a route guidance function based on the group feature information and destination information.

A cleaning system and a cleaning method configured for cleaning task of solar panels are provided. The cleaning system includes an operation region, cleaning robots, shuttle robots, and a data processing system. The cleaning method includes a first carrying step, a cleaning step, and a second carrying step.

A system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.

Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.