Systems and methods for autonomous vehicle path planning are described herein. An example vehicle includes an image sensor to obtain an image of a scene of an area surrounding the vehicle. The vehicle also includes navigation system circuitry to: analyze the image and generate a semantically segmented image that identifies one or more types of features in the image; project the semantically segmented image to a two-dimensional (2D) map projection; convert the 2D map projection into a cost map; and determine a path for the vehicle based on the cost map.

A method and system for exploring a real-world environment using a mobile robot platform comprising mapping the environment at a first time, to generate a first representation of the environment and identifying an initial location of an object in the first representation of the environment. The environment is mapped at a second time, to generate a second representation of the environment. The mappings are generated based on data obtained from a sensor associated with the mobile robot platform. Based on data obtained from the sensor, a new location of the object is identified in the second representation of the environment, and a difference between the initial location of the object and the new location of the object is determined. Using a manipulator the object is moved to the initial location when it is determined that the initial location of the object differs from the new location.

A golf vehicle following system includes a following golf vehicle. The following golf vehicle includes a driveline, a communications interface, and a sensor system configured to acquire second data. The golf vehicle system includes at least one processing circuit having at least one processor and at least one memory, the at least one memory storing instructions thereon that, when executed by the at least one processor, cause the at least one processor to: receive a request for the following golf vehicle to follow a leading golf vehicle; and control the driveline such that the following golf vehicle follows the leading golf vehicle within a specified distance based on at least one of (a) first data acquired from at least one of the communications interface or from a global positioning system or (b) the second data acquired by the sensor system.

A method and an apparatus for determining a position of a shelf are provided. The method may include: obtaining a number of automated guided vehicles with shelf scanning devices; determining, based on the number of the automated guided vehicles, a scanning area of a place to which each automated guided vehicle belongs; determining, based on the scanning area, a scanning route of the scanning area to which each automated guided vehicle belongs; transmitting the scanning route of the scanning area to which the automated guided vehicle belongs, to the automated guided vehicle; and determining a position of a shelf in the scanning area to which the automated guided vehicle belongs based on scanning information of a shelf scanning device on the automated guided vehicle and position information of the automated guided vehicle.

The present disclosure relates to an autonomous mobile device and its control method and apparatus and a storage medium, the control method includes: an obtaining step configured to obtain point cloud data of the autonomous mobile device in a current work environment; a determination step configured to determine whether there exists an obstacle in the work environment based on the point cloud data; a processing step configured to, when it is determined that there exists an obstacle, recognize a type of the obstacle based on the point cloud data, and execute an obstacle avoidance action corresponding to the type of the obstacle. As such, the robustness of the obstacle avoidance action can be increased.

An unmanned device for a marine environment comprises a location sensor configured to gather location data corresponding to the unmanned device; at least one propulsion system; a transmitter and memory including computer program code. The computer program code is configured to, when executed, cause the processor to cause the propulsion system to propel the unmanned device in a pattern along the body of water, cause the sonar transducer to emit the one or more sonar beams into the body of water, receive sonar return data corresponding to sonar returns, and generate a sonar image corresponding to the sonar return data. Further, the computer program code is configured to cause the processor to detect an object within the sonar image, assign a score to the object indicating the likelihood that the object is a desired object type, and send an alert to the remote electronics device upon assignment of the score.

A mobile outdoor power equipment machine for performing a controlled task within a work area includes a drive system for providing movement of the machine, a working apparatus for performing the task, and a scanning system for scanning an area surrounding the machine. The scanning system is configured to provide detection of physical elements in the environment to aid in navigation of the machine. In an embodiment, the scanning system and a control system are configured to scan the area, determine the presence of a physical element in the area, determine that the physical element is located within the work area, determine the proximity of the physical element to the machine, and direct a behavior of the machine.

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.

Systems and methods for configuring, planning, and executing autonomous mowing operations in a turf environment using an autonomous mowing machine are described. The system determines external and internal boundaries of a property through position data (e.g., GPS, odometry) and visual information from onboard sensors. The collected information is used to create digital fences and zone boundaries. Objects within the mowing area are classified as transient or non-transient, with corresponding autonomous actions assigned. Boundaries and object data inform path planning, dynamically updating routes in response to environmental changes, object reclassification, or operator input via a client device. The machine executes the planned actions in real time, adjusting various operational parameters as it traverses the turf environment.

An autonomous mowing vehicle leverages a plurality of virtual safety bubbles and a virtual buffer around an object to avoid collisions between the object and the vehicle. The vehicle has a camera system comprising a plurality of cameras positioned around the vehicle, a mowing deck comprising one or more motorized blades for mowing plants in the environment, and a control system. The control system is configured to: capture image data from a camera system of an autonomous mowing vehicle; detect at least one object in an environment surrounding the autonomous mowing vehicle based on the image data; generate a virtual buffer for the object, the virtual buffer positioned around the object; generate a plurality of virtual safety bubbles around the autonomous mowing vehicle based on a configuration of the autonomous mowing vehicle; and perform autonomous operation of the mowing vehicle to perform, via at least the mowing deck, one or more landscaping actions in the environment while evading breach of the plurality of virtual safety bubbles by the virtual buffer of the object.