Abstract: The present invention relates to the reduction of multi-sensor data when used as input to machine-learning (ML) models. Typically, ML models use sensor data to learn characteristics of a problem domain. This data is usually input to the ML model in an end-to-end fashion: the data from sensor 1 is appended with the data from sensor 2, etc., until the entire concatenated data set forms a single input example from which the model learns. The more sensors, the more data, the larger the size of the data input to the ML model, and the longer it is likely to take to train and run the model. Disclosed is a method to combine data from multiple sensors, reducing it into a smaller input data space. The data from 2 or more sensors of the same type can be combined in the same input data space, to simplify the input data size, enabling smaller, faster machine-learning models.

Abstract: Described is a system and method through which (a) information related to detected moving objects can be received, processed and confirmed, (b) such information can be evaluated—with the information from differing receiving, processing and confirming sources being aggregated and integrated, thereby increasing the overall efficiency and accuracy of the system and method, and with the elements of the system and the practice of the method resulting in the sharing of such information with such elements controlling various aspects of their own operation—to assess the possible impacts of the presence and/or operation of such detected moving objects within a specific environment, and as a result of such evaluation (c) actions can be initiated based upon (x) the identification of the detected moving object and (y) the possible impacts thereof within such specific environment.

Abstract: Described is a system and method through which (a) information related to detected moving objects can be received, processed and confirmed, (b) such information can be evaluated—with the information from differing receiving, processing and confirming sources being aggregated and integrated, thereby increasing the overall efficiency and accuracy of the system and method, and with the elements of the system and the practice of the method resulting in the sharing of such information with such elements controlling various aspects of their own operation—to assess the possible impacts of the presence and/or operation of such detected moving objects within a specific environment, and as a result of such evaluation (c) actions can be initiated based upon (x) the identification of the detected moving object and (y) the possible impacts thereof within such specific environment.

Abstract: Embodiments of the invention provide systems and methods for obstacle avoidance. In some embodiments, a robotically controlled vehicle capable of operating in one or more modes may be provided. Examples of such modes include teleoperation, waypoint navigation, follow, and manual mode. The vehicle may include an obstacle detection and avoidance system capable of being implemented with one or more of the vehicle modes. A control system may be provided to operate and control the vehicle in the one or more modes. The control system may include a robotic control unit and a vehicle control unit.

Abstract: An autonomous vehicle and systems having an interface for payloads that allows integration of various payloads with relative ease. There is a vehicle control system for controlling an autonomous vehicle, receiving data, and transmitting a control signal on at least one network. A payload is adapted to detachably connect to the autonomous vehicle, the payload comprising a network interface configured to receive the control signal from the vehicle control system over the at least one network. The vehicle control system may encapsulate payload data and transmit the payload data over the at least one network, including Ethernet or CAN networks. The payload may be a laser scanner, a radio, a chemical detection system, or a Global Positioning System unit.

Abstract: Embodiments of the invention provide systems and methods for obstacle avoidance. In some embodiments, a robotically controlled vehicle capable of operating in one or more modes may be provided. Examples of such modes include teleoperation, waypoint navigation, follow, and manual mode. The vehicle may include an obstacle detection and avoidance system capable of being implemented with one or more of the vehicle modes. A control system may be provided to operate and control the vehicle in the one or more modes. The control system may include a robotic control unit and a vehicle control unit.

Abstract: Embodiments of the invention provide systems and methods for obstacle avoidance. In some embodiments, a robotically controlled vehicle capable of operating in one or more modes may be provided. Examples of such modes include teleoperation, waypoint navigation, follow, and manual mode. The vehicle may include an obstacle detection and avoidance system capable of being implemented with one or more of the vehicle modes. A control system may be provided to operate and control the vehicle in the one or more modes. The control system may include a robotic control unit and a vehicle control unit.

Abstract: An autonomous vehicle and systems having an interface for payloads that allows integration of various payloads with relative ease. There is a vehicle control system for controlling an autonomous vehicle, receiving data, and transmitting a control signal on at least one network. A payload is adapted to detachably connect to the autonomous vehicle, the payload comprising a network interface configured to receive the control signal from the vehicle control system over the at least one network. The vehicle control system may encapsulate payload data and transmit the payload data over the at least one network, including Ethernet or CAN networks. The payload may be a laser scanner, a radio, a chemical detection system, or a Global Positioning System unit. In certain embodiments, the payload is a camera mast unit, where the camera communicates with the autonomous vehicle control system to detect and avoid obstacles.

Abstract: Embodiments of the invention provide systems and methods for obstacle avoidance. In some embodiments, a robotically controlled vehicle capable of operating in one or more modes may be provided. Examples of such modes include teleoperation, waypoint navigation, follow, and manual mode. The vehicle may include an obstacle detection and avoidance system capable of being implemented with one or more of the vehicle modes. A control system may be provided to operate and control the vehicle in the one or more modes. The control system may include a robotic control unit and a vehicle control unit.