An exemplary motion capture system, device, and method are disclosed herein to facilitate the implementation of active marker technology onto objects and systems whose design considerations require low weight, low power usage, and/or modular implementation. The exemplary motion capture system, device, and method connect light sources with power driving components through modular, flexible strips and optimized driver circuit configuration and structure for minute unmanned or remote vehicles or wearables having very stringent design constraints.

An exemplary motion capture system, device, and method are disclosed herein to facilitate the implementation of active marker technology onto objects and systems whose design considerations require low weight, low power usage, and/or modular implementation. The exemplary motion capture system, device, and method connect light sources with power driving components through modular, flexible strips and optimized driver circuit configuration and structure for minute unmanned or remote vehicles or wearables having very stringent design constraints.

Robotic wings for an aerial drone include a plurality of armwing structures, each comprising a plurality of rigid members connected together by flexible living hinges in a single monolithic structure. Wing membranes are supported by the armwing structures. A drive mechanism is connected to the armwing structures for articulating the armwing structures. A motor is connected to the drive mechanism for actuating the drive mechanism to move the armwing structures through a series of wingbeats wherein the armwing structures expand in a downstroke and retract in an upstroke to move the wing membranes in a flapping motion.

Robotic wings for an aerial drone include a plurality of armwing structures, each comprising a plurality of rigid members connected together by flexible living hinges in a single monolithic structure. Wing membranes are supported by the armwing structures. A drive mechanism is connected to the armwing structures for articulating the armwing structures. A motor is connected to the drive mechanism for actuating the drive mechanism to move the armwing structures through a series of wingbeats wherein the armwing structures expand in a downstroke and retract in an upstroke to move the wing membranes in a flapping motion.

A method of performing pollination of a flower of a plant, and a system for performing pollination. The method comprises the steps of providing a device for generating an airflow; positioning the device relative to the plant such that the flower is subjected to the airflow; and dislodging pollen from the flower as a result of vibrations caused by airflow-induced instabilities in the flower.

A method of performing pollination of a flower of a plant, and a system for performing pollination. The method comprises the steps of providing a device for generating an airflow; positioning the device relative to the plant such that the flower is subjected to the airflow; and dislodging pollen from the flower as a result of vibrations caused by airflow-induced instabilities in the flower.

A system for providing medical situational awareness in a hazardous area includes an unmanned aerial system (UAS) and a UAS controller for operating the UAS. The UAS includes at least one electro-optical (EO) sensor for viewing the hazardous area to provide EO sensor data, and a processor for executing a vision algorithm package based on the EO sensor data to locate and analyze casualty victims within the hazardous area. The vision algorithm package includes casualty detection algorithms for casualty detection and identification; casualty assessment algorithms for casualty respiration rate detection, pulse rate detection and motion detection; and gross injury detection algorithms for casualty wound and hemorrhage detection, and kinematic irregularity detection. Casualty information as determined by the vision algorithm package is transmitted to the UAS controller. The UAS controller includes a display to display the casualty information to provide the medical situation for the hazardous area.

A system for providing medical situational awareness in a hazardous area includes an unmanned aerial system (UAS) and a UAS controller for operating the UAS. The UAS includes at least one electro-optical (EO) sensor for viewing the hazardous area to provide EO sensor data, and a processor for executing a vision algorithm package based on the EO sensor data to locate and analyze casualty victims within the hazardous area. The vision algorithm package includes casualty detection algorithms for casualty detection and identification; casualty assessment algorithms for casualty respiration rate detection, pulse rate detection and motion detection; and gross injury detection algorithms for casualty wound and hemorrhage detection, and kinematic irregularity detection. Casualty information as determined by the vision algorithm package is transmitted to the UAS controller. The UAS controller includes a display to display the casualty information to provide the medical situation for the hazardous area.

This invention presents a bio-neural navigation and intelligent intrusion detection system for UAVs, inspired by the head direction (HD) system in fruit flies. The navigation system uses a neural network-based architecture to process visual inputs and maintain orientation with a ring attractor network, enabling stable, autonomous flight in complex, GPS-denied environments. The multi-modal intrusion detection module integrates visual, radar, and acoustic sensor data to detect, classify, and respond to intrusions or obstacles in real-time. Combining supervised and unsupervised machine learning, it performs threat assessments and initiates adaptive responses like evasive maneuvers and dynamic re-routing. The integration of bio-neural navigation and intrusion detection ensures secure, autonomous UAV operations with enhanced situational awareness and threat management. This system is ideal for autonomous surveillance, urban air traffic management, and military reconnaissance, where adaptive navigation is critical.

This invention presents a bio-neural navigation and intelligent intrusion detection system for UAVs, inspired by the head direction (HD) system in fruit flies. The navigation system uses a neural network-based architecture to process visual inputs and maintain orientation with a ring attractor network, enabling stable, autonomous flight in complex, GPS-denied environments. The multi-modal intrusion detection module integrates visual, radar, and acoustic sensor data to detect, classify, and respond to intrusions or obstacles in real-time. Combining supervised and unsupervised machine learning, it performs threat assessments and initiates adaptive responses like evasive maneuvers and dynamic re-routing. The integration of bio-neural navigation and intrusion detection ensures secure, autonomous UAV operations with enhanced situational awareness and threat management. This system is ideal for autonomous surveillance, urban air traffic management, and military reconnaissance, where adaptive navigation is critical.