Technology is disclosed herein for operating a tasking service for UAVs. In an implementation, a tasking service receives task parameters which includes a desired state of the UAVs for performing a task and service information associated with performing the task. The tasking service continuously receives state information from the UAVs which identifies a present state of the UAVs and continuously evaluates the present state of the UAVs with respect to the desired state. When the present state of an UAV matches the desired state, the tasking service assigns the task to the UAV and provides the service information to the UAV. In an implementation, the tasking service receives task parameters via an application programming interface from a client application in communication with the tasking service.

Technology is disclosed herein for operating a tasking service for UAVs. In an implementation, a tasking service receives task parameters which includes a desired state of the UAVs for performing a task and service information associated with performing the task. The tasking service continuously receives state information from the UAVs which identifies a present state of the UAVs and continuously evaluates the present state of the UAVs with respect to the desired state. When the present state of an UAV matches the desired state, the tasking service assigns the task to the UAV and provides the service information to the UAV. In an implementation, the tasking service receives task parameters via an application programming interface from a client application in communication with the tasking service.

A method and a system for rhythmic motion control of a robot based on a neural oscillator, including: acquiring a current state of the robot, and a phase and a frequency generated by the neural oscillator; and obtaining a control instruction according to the acquired current state, phase and frequency and a preset reinforcement learning network so as to control the robot. The preset reinforcement learning network includes an action space, a pattern formation network and the neural oscillator. A control structure designed by the present disclosure, which is composed of the neural oscillator and the pattern formation network, can ensure formation of an expected rhythmic motion behavior; and meanwhile, a designed action space for joint position increment can effectively accelerate the training process of rhythmic motion reinforcement learning, and solve a problem that design of the reward function is time-consuming and difficult in learning with existing model-free reinforcement learning.

A method and a system for rhythmic motion control of a robot based on a neural oscillator, including: acquiring a current state of the robot, and a phase and a frequency generated by the neural oscillator; and obtaining a control instruction according to the acquired current state, phase and frequency and a preset reinforcement learning network so as to control the robot. The preset reinforcement learning network includes an action space, a pattern formation network and the neural oscillator. A control structure designed by the present disclosure, which is composed of the neural oscillator and the pattern formation network, can ensure formation of an expected rhythmic motion behavior; and meanwhile, a designed action space for joint position increment can effectively accelerate the training process of rhythmic motion reinforcement learning, and solve a problem that design of the reward function is time-consuming and difficult in learning with existing model-free reinforcement learning.

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 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 corrosion positioning system, a corrosion inspection vehicle and a corrosion positioning method using the same are provided. The corrosion positioning method includes the following steps. An image information is read by a corrosion hotspot recognition module to recognize a corrosion hotspot. A location information of the corrosion hotspot is obtained by a corrosion hotspot positioning module. The location information of the corrosion hotspot is corrected by an inspection optimization module to obtain a corrected location information. The step of correcting the location information of the corrosion hotspot includes the following steps. The correlation information of a plurality of feature points in a plurality of frames in the image information is analyzed by the inspection optimization module. The position information of a point to be corrected is corrected by the inspection optimization module according to the correlation information. The location information is normalized by the inspection optimization module.

A corrosion positioning system, a corrosion inspection vehicle and a corrosion positioning method using the same are provided. The corrosion positioning method includes the following steps. An image information is read by a corrosion hotspot recognition module to recognize a corrosion hotspot. A location information of the corrosion hotspot is obtained by a corrosion hotspot positioning module. The location information of the corrosion hotspot is corrected by an inspection optimization module to obtain a corrected location information. The step of correcting the location information of the corrosion hotspot includes the following steps. The correlation information of a plurality of feature points in a plurality of frames in the image information is analyzed by the inspection optimization module. The position information of a point to be corrected is corrected by the inspection optimization module according to the correlation information. The location information is normalized by the inspection optimization module.

An autonomous system for detecting, localizing, and potentially deactivating chemical threats or emissions using multiple sensing modalities and reinforcement learning techniques. The system includes visual sensors (e.g., RGB, RGBD, LIDAR), non-visual sensors (e.g., gas concentration, airflow, GPS, RADAR), a neural network architecture and processor to fuse information from different sensors, a module based on deep reinforcement learning for decision making, and a robotic interface for executing actions. The neural network extracts relevant information from sensor streams and encodes them into a joint embedding space. The module considers the current observations, historical data, and previous actions to determine the optimal action for threat localization under partially observable conditions. The system is trained in simulated environments to minimize source localization time while accounting for various constraints. The autonomous system enables effective chemical threat detection and source localization in complex, dynamic environments without endangering human operators.

An autonomous system for detecting, localizing, and potentially deactivating chemical threats or emissions using multiple sensing modalities and reinforcement learning techniques. The system includes visual sensors (e.g., RGB, RGBD, LIDAR), non-visual sensors (e.g., gas concentration, airflow, GPS, RADAR), a neural network architecture and processor to fuse information from different sensors, a module based on deep reinforcement learning for decision making, and a robotic interface for executing actions. The neural network extracts relevant information from sensor streams and encodes them into a joint embedding space. The module considers the current observations, historical data, and previous actions to determine the optimal action for threat localization under partially observable conditions. The system is trained in simulated environments to minimize source localization time while accounting for various constraints. The autonomous system enables effective chemical threat detection and source localization in complex, dynamic environments without endangering human operators.