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.

Systems, devices, and methods including: at least one unmanned aerial vehicle (UAV); at least one battery pack comprising at least one battery; and at least one motor of the at least one UAV, where the at least one battery is configured to transfer energy to the at least one motor; where power from the at least one motor is configured to ascend the at least one UAV to a second altitude when the at least one battery is at or near capacity, and where the second altitude is higher than the first altitude; and where power from the at least one motor is configured to descend the at least one UAV to the first altitude after the Sun has set to conserve energy stored in the at least one battery.

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.

A robot based on origami principles, includes: linkage components, central panels, and a plurality of sector-shaped panels, wherein: two ends of the linkage components are connected to a same number of the sector-shaped panels, with every two adjacent sector-shaped panels connected by a respective one of connecting elements; the central panels are located in a middle of the linkage components, and the central panels are configured for placement of a control assembly, the control assembly comprising a folding/unfolding motor and a motion control system; the folding/unfolding motor is configured to drive the connecting elements to control the robot to switch to any one of a multirotor configuration, a wheel configuration, or a waterborne motion configuration.

A robot based on origami principles, includes: linkage components, central panels, and a plurality of sector-shaped panels, wherein: two ends of the linkage components are connected to a same number of the sector-shaped panels, with every two adjacent sector-shaped panels connected by a respective one of connecting elements; the central panels are located in a middle of the linkage components, and the central panels are configured for placement of a control assembly, the control assembly comprising a folding/unfolding motor and a motion control system; the folding/unfolding motor is configured to drive the connecting elements to control the robot to switch to any one of a multirotor configuration, a wheel configuration, or a waterborne motion configuration.

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.

An Offline Large Language Model for Drone Control and Monitoring is disclosed. The system incorporates a smaller large language model that is trained in a much similar way, but in an offline setting to simplify drone operation, making it accessible to users with minimal training. This is especially advantageous in military contexts where quick deployment and ease of use are critical. The offline nature of the model ensures functionality in environments without reliable internet access.