In various examples, embodiments are directed to identifying map data (e.g., relevant to a route) using a quad-tree spatial index. In this regard, spatial map data that indicates various map features is represented in a quad-tree spatial index for use in identifying map data. To identify map data, bounding shapes may be generated in association with various segments of a route. An indication of an object-oriented bounding shape may be used to query the quad-tree spatial index to identify map data related to the object-oriented bounding shape. In embodiments, an object-oriented spatial index may be generated that indexes the object-oriented bounding shapes associated with the route. The object-oriented spatial index may be used to query the quad-tree spatial index to identify map data related to the corresponding object-oriented bounding shapes. Alternatively, the quad-tree spatial index may be used to query the object-oriented spatial index to identify map data.

A method is provided for causing one or more robots to execute a mission. The method includes determining a behavior tree in which the mission is modeled, and causing the one or more robots to execute the mission using the behavior tree and a leaf node library. The behavior tree is expressed as a directed tree of nodes including a switch node, a trigger node representing a selected task, and action nodes representing others of the tasks. The switch node is connected to the trigger node and the action nodes in a parent-child relationship in which the trigger node and the action nodes are children of the switch node. The trigger node is a first of the children that, when ticked by the switch node, returns an identifier of one of the action nodes to trigger the switch node to next tick the one of the action nodes.

A server to manage a plurality of robots, where each robot performs a specified set of tasks, includes a robot management table, an operational database, a preprocessor and an initializer. The robot management table lists per robot tasks and associated scripts according to a unique robot ID for each of the plurality of robots. The operational database stores operational data required to run the scripts. The preprocessor runs at least one algorithm to obtain and update the operational data. The initializer initializes a particular robot of the plurality of robots according to its unique robot ID with its associated the scripts when the server is in network communication with the particular robot.

Provided is a system and method for generating routes for use by a mobile robot. The mobile robot can comprise a navigation system in operative communication with a drive system; one or more sensors configured to collect sensor data, wherein the one or more sensors are configured to collect training data representative of a route or portions of a route as the mobile robot is navigated along the route; a user interface configured to receive user inputs providing route information; and a route generator configured to process the route information and the training data to generate a route network comprising a plurality of route segments. The training data can be generated while the mobile robot is navigated in a first direction and the mobile robot is configured to autonomously navigate in a second direction that is opposite the first direction.

A coverage path planning (CPP) method for multiple unmanned aerial vehicles (UAVs) in complex irregular areas includes: acquiring a plurality of regular sub-areas through a multi-strategy recursive optimal decomposition approach to address the problem of excessive decomposition of a concave vertex; and proposing, by considering the efficiency of solving for an access order among different areas, an adaptive large neighborhood search method to quickly acquire the access order among the areas, thereby acquiring a complete coverage planning path. Compared with existing methods, the CPP method can quickly and efficiently achieve coverage of complex irregular areas, improving the efficiency of UAV path planning. In addition, the CPP method has universality and is applicable to any unmanned system operating on a plane, with high practical value.

Disclosed are systems, methods and devices for centralized control of autonomous vehicles. In some embodiments, a system and method allow an autonomous control system on-board an autonomous vehicle to pass control of the autonomous vehicle to an offboard panel of experts upon encountering an anomaly. In some embodiments, a system and method allow a regulatory entity to proactively distribute rules and requirements to autonomous vehicles while operating within a regulated space.

Techniques are provided to manage routing of mobile objects in a given environment. For example, a method is performed by an object routing system. The object routing system receives a request for a given mobile object to traverse from a first location to a second location in a physical environment which is logically partitioned into a plurality of grid locations. The object routing system computes a route for the given mobile object to traverse from the first location to the second location. The computed route comprises a sequence of grid locations which are reserved at corresponding times, wherein each grid location of the computed route is reserved for the corresponding time that the given mobile object is expected to traverse through the grid location. The object routing system commands the given mobile object to traverse the computed route.

An automated vehicle (AV) is configured to perform automated travel, and includes vehicle sensors configured to detect a second vehicle in a surrounding environment of the AV. The AV includes at least one of a brake mechanism, an accelerator mechanism, a steering control, and a user interface configured to generate a user response to automated travel by the AV. The AV includes a computing device configured to identify an interaction between the AV and the second vehicle while executing an automated travel path, and receive user responses to automated travel by the AV. The computing device is configured to determine at least one aspect of wellbeing, trust, and satisfaction of the user riding the AV based on the user responses, and determine a learned optimal policy which increases the at least one aspect of wellbeing, trust, and satisfaction based on the user responses.

A robotic dog empowered by generative artificial intelligence (Gen-AI) is disclosed, capable of autonomously performing essential tasks such as guiding visually impaired individuals, detecting drugs and arms, and providing companionship. The robotic dog's lifelike design includes a head, eyes, ears, a nose, a mouth with teeth, a neck, a body, four legs with paws, and a tail, all meticulously crafted to mimic the appearance and behavior of a real dog. A trained AI model functions as the brain, processing environmental data captured by video cameras, audio microphones, and sensors to provide guidance commands to a control system that control the movements of the robotic dog. A well-trained live dog can serve as a teacher for one or multiple robotic dogs using a generative AI-based real-time training method, enabling efficient and effective training of robotic dogs.

This autonomous control system: calculates the position of an object to be controlled (first object); identifies an attribute of an object not to be controlled (second object); calculates the position of the second object; evaluates the degree of deviation of the movement trajectory of the second object from a predicted movement trajectory of the second object corresponding to the attribute of the second object; determines a safety standard related to the action of the first object, on the basis of the attribute of the second object and the degree of deviation from the predicted movement trajectory; and, on the basis of the position of the first object, the position of the second object, and the safety standard, corrects the action of the first object such that the first object becomes less likely to approach the second object as the safety standard becomes higher.