Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Abstract: The present invention is directed to an impact protector (1), in particular for protection against external forces, comprising a base body (2) with an inner side (5) and an outer side (6). The base body (2) is preferably convexly curved outwards. The base body (2) is made of a cork material, in particular a pressed cork material. Furthermore, the invention relates to a textile holding device with an impact protector according to the invention.

Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Abstract: A method determines iteratively a motion of the vehicle from an initial location and a target location. An iteration of the method determines a location between the initial location and the target location that satisfies spatial constraints on locations of the vehicle and determines state transitions of the vehicle moved to the location from a set of neighboring locations determined during previous iterations. The method selects a neighboring location resulting in an optimal state transition of the vehicle and updates a graph of state transitions of the vehicle determined during previous iterations with the optimal state transition. The motion of the vehicle is determined a sequence of state transitions connecting the initial location with the target location and the vehicle is controlled according to the determined motion.

Abstract: Designing intrinsically safe robotic inspection systems used for unmanned navigation and inspection of large tanks with hazardous and explosive atmosphere is challenging. The disclosed methods and devices provide solutions to overcome such challenge. Intrinsically safe devices and methods using a combination of a lighter-than-air blimp with various intrinsically safe subsystems attached to the blimp are presented.

Abstract: A method determines iteratively a motion of the vehicle from an initial location and a target location. An iteration of the method determines a location between the initial location and the target location that satisfies spatial constraints on locations of the vehicle and determines state transitions of the vehicle moved to the location from a set of neighboring locations determined during previous iterations. The method selects a neighboring location resulting in an optimal state transition of the vehicle and updates a graph of state transitions of the vehicle determined during previous iterations with the optimal state transition. The motion of the vehicle is determined a sequence of state transitions connecting the initial location with the target location and the vehicle is controlled according to the determined motion.