The present disclosure relates to a system for tracking wildlife such as game animals. The tracking system includes a drone that has a camera and a dart dispenser that dispenses a dart with a location transmitter. The drone sends images from the camera to a remote controller, where a user pilots the drone and observes animals for disease or other behaviors, and sends instructions to dispense the dart. The real-time location of the dart is then provided to a mobile application for use in tracking or hunting the animal.

A computer system configured to use emergency response system (EMS) vehicle telematics data to reduce risk of accidents may be configured to (1) receive, when the EMS vehicle is en route to an emergency location, the EMS vehicle telematics data associated with the EMS vehicle and including GPS location, speed, route, heading, acceleration, and/or lane data; (2) determine that a current route of an autonomous vehicle will interfere with the route of the EMS vehicle; (3) determine an alternate route for the autonomous vehicle to avoid interfering with the route of the EMS vehicle; and (4) direct the autonomous vehicle to (i) travel along the alternate route or (ii) pull over to a side of a road on the current route to allow the EMS vehicle to pass unimpeded. Insurance discounts may be generated based upon the risk mitigation or prevention functionality.

A system for autonomous or semi-autonomous operation of a vehicle is disclosed. The system includes a machine automation portal (MAP) application configured to enable a computing device to (a) display a map of a work site and (b) provide a graphical user interface that enables a user to (i) define a boundary of an autonomous operating zone on the map and (ii) define a boundary of one or more exclusion zones. The system also includes a robotics processing unit configured to (a) receive the boundary of the autonomous operating zone and the boundary of each exclusion zone from the computing device, (b) generate a planned command path that the vehicle will travel to perform a task within the autonomous operating zone while avoiding each exclusion zone, and (c) control operation of the vehicle so that the vehicle travels the planned command path to perform the task.

Vehicles and methods described herein include a vehicle that operates with a rider according to an operating parameter. The vehicle includes: a physiological monitoring sensor configured to measure a physiological parameter of the rider; an experience hybrid neural network trained on outcomes related to a rider in-vehicle experience associated with the physiological parameter to determine an emotional state of the rider; an augmented reality system configured to present augmented reality content to the rider of the vehicle based, at least in part, on the operating parameter; and an optimization hybrid neural network that identifies a variation in the operating parameter to change the emotional state of the rider and that generates a command to vary the operating parameter and the augmented reality content according to the variation.

The systems and methods herein provide improved methodologies for visualization on a user's display of sensor data (e.g., 2D and 3D information obtained from or derived from sensors) for objects, components, or features of interest in a scene. The previously acquired sensor data is processable for concurrent display of objects/features/scene or location visualizations to a user during their real-time navigation of a scene camera during a variety of user visualization activities. Sensor data can be acquired via the operation of vehicles configured with one or more sensors, such as unmanned aerial vehicles, or from other methodologies, or from any other suitable sensor data acquisition activities. Objects etc. for which acquired sensor data can be visualized by a user on a display includes buildings, parts of buildings, and infrastructure elements, among other things. The improved display of information to a user for visualization and information generation therefrom provides significant benefits over prior art display methodologies and exhibits notable utility for user activities such as, inspection, condition assessment, performance assessment, insurance applications, construction, inventorying, building information modeling, asset management and the like. Information derivable from the methodologies herein can be used for machine learning libraries and digital twin processes.

A waste collection bag may be used by an autonomous cleaning robot (e.g., an autonomous vacuum) to store waste during a plurality of cleaning processes. The waste bag comprises a waste bag, a waste collection sachet, and an absorbent. The waste bag has a first side and a second side, where the first side has an opening for waste to enter, and is composed of filtering material. The waste collection enclosed sachet has a first side and a second side that connect to form a cavity. The waste collection enclosed sachet is tethered to the second side of the waste bag and is composed of dissolvable paper. The absorbent may absorb liquid waste and is located inside the cavity of the waste collection enclosed sachet.

A site connectivity system includes a first connectivity module configured to be coupled to a first machine, a second connectivity module configured to be coupled to a second machine, and a third connectivity module configured to be coupled to a third machine. The third machine is a different type of machine than at least one of the first machine or the second machine. The first connectivity module and the second connectivity module are configured to establish a site network at a site. The third connectivity module is configured to join the site network and transmit data regarding the third machine to the first connectivity module through the site network.

A site connectivity system includes a deployable connectivity hub configured to be selectively deployed at a site. The deployable connectivity hub including a wireless hub connectivity module configured to facilitate wireless communications and a processing circuit. The processing circuit is configured to establish a local site network with a plurality of wireless machine connectivity modules including at least a first wireless machine connectivity module associated with a first machine at the site and a second wireless machine connectivity module associated with a second machine at the site, establish a connection with a remote server, receive data from the first wireless machine connectivity module regarding the first machine over the local site network, transmit the data to the second wireless machine connectivity module over the local site network, and transmit the data to the remote server over the connection.

Techniques are disclosed for performing custom waypoint missions using an onboard computing device in communication with a movable object. The movable object can include a flight controller, and a communication system. The flight controller can be in communication with the onboard computing device which includes a processor and an onboard data manager. The onboard data manager can receive at least one input, determine one or more instructions to be performed by the at least one movable object based on the at least one input, generate movement commands to implement the one or more instructions, and send movement commands to the flight controller to be executed.

An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.