Analyte survey systems (100) and related techniques are provided to improve the operation of handheld or unmanned mobile sensor or survey platforms. An analyte survey system includes a logic device (112) configured to communicate with a communication module (164) and a sensor assembly (166) of a modular sensor core (160), where the communication module is configured to establish a wireless communication link with a base station (130) associated with the modular sensor core and/or a mobile sensor platform (110) and the sensor assembly is configured to provide analyte sensor data as the modular sensor core is maneuvered within a survey area.

A surveillance performance (information expressing uncertainty of information relating to a potential risk area in a region that requires monitoring) and a tracking performance (information expressing the product of an importance level of a potential risk area and a coverage ratio (an overlap rate between potential risk areas and moving bodies)) are mutually analyzed, and plural moving bodies are allotted to either surveillance or tracking. Potential risk areas that require monitoring are determined such that the surveillance performance and the tracking performance respectively satisfy predetermined levels. The plural moving bodies are allocated to potential risk areas determined to be potential risk areas that require monitoring (simultaneous optimization). The respective moving bodies subject to simultaneous optimization start moving toward their allocated potential risk areas to monitor the potential risk areas.

Unmanned vehicles can be terrestrial, aerial, nautical, or multi-mode. Unmanned vehicles may be used to survey a property in response to or in anticipation of a threat. For example, an unmanned vehicle may analyze information about the property and based on the information deter theft of items on the property.

Techniques are described for an autonomous asset management system that integrates autonomous devices, such as drone devices and other robotic devices, with a home security system of a property to enable management, monitoring, and/or tracking of various assets located within the property. In some implementations, an indication of an asset associated with a property is obtained by an autonomous device. Sensor data collected by one or more sensors of the property is obtained by the autonomous device based on the indication of the asset. A present status of the asset is determined by the autonomous device based on the sensor data. A determination that the present status of the asset does not correspond to an expected status of the asset is made by the autonomous device. In response, the autonomous device navigates to the particular location of the property.

Systems, methods, and apparatuses for performing an opening or closing security procedure at a provider location using an unmanned aerial vehicle (UAV) are described herein. An autonomous security system includes a UAV, a user device, and a UAV security system. The UAV security system includes a processing circuit structured to guide the UAV along a predetermined route within or near the provider location. The processing circuit is further structured to receive monitoring data associated with the provider location and its surroundings from the UAV, the monitoring data comprising ultra-wideband (UWB) data and radio-frequency identification (RFID) data. The processing circuit is further configured to identify a foreign object based on the monitoring data, determine that the foreign object is one of a security threat or a defect, and provide a notification to the user device regarding the one of the security threat or the defect.

A method for real-time crowd management includes receiving LiDAR point cloud data from area of interest, forming a 3D static surface model of area of interest from LiDAR point cloud data, obtaining real-time CCTV camera images of area of interest, adding real-time CCTV camera images to 3D static surface model to generate real-time dynamic 3D model, identifying dynamic objects in the 3D model, generating a density map of an area of interest, adding density map to the 3D model, identifying which dynamic objects are people, replacing each person with animated character, displaying real-time dynamic 3D model, monitoring the 3D model in the area of interest for dangerous situations, simulating evacuation of area of interest by manipulating animated characters onto pathways leading away from area of interest, forming an evacuation strategy for crowd, and transmitting a notice of the dangerous situation and the evacuation strategy to an emergency authority.

Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

An aircraft includes an airframe with first and second wings having a fuselage extending therebetween. A propulsion assembly is coupled to the fuselage and includes a counter-rotating coaxial rotor system that is tiltable relative to the fuselage to generate a thrust vector. First and second yaw vanes extend aftwardly from the fuselage. A flight control system is configured to direct the thrust vector of the coaxial rotor system and control movements of the yaw vanes. In a VTOL orientation of the aircraft, differential operation of the yaw vanes and/or differential operations of first and second rotor assemblies of the coaxial rotor system provide yaw authority for the aircraft. In a biplane orientation of the aircraft, collective operation of the yaw vanes provides yaw authority for the aircraft.

Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

Various methods for monitoring a target user by a robotic vehicle include tracking the target user by the robotic vehicle, detecting an object in the presence of the target user based on one or more detection criteria, determining whether the object is a potential threat to the target user based on one or more threat criteria, determining whether to notify the third party of the potential threat to the target user based on one or more notification criteria in response to determining that the object is a potential threat, and notifying the third party of the potential threat to the target user in response to determining that the third party should be notified.