A path finding system using a series of networked receiver beacons is disclosed. The system includes receiver beacons placed on a path. Each of the receiver beacons include a transceiver receiving and sending signals and a location indicator such as a LED, that when activated indicates the location of the receiver beacon. Each of the receiver beacons include a controller coupled to the indicator and the transceiver. The controller is operable to receive an activation signal to activate the indicator. A transmitter is paired with each of the receiver beacons. The transmitter includes a transceiver to send an activation signal to at least one of the receiver beacons. The receiver beacon receives the activation signal and activates the indicator. The receiver beacon also relays the activation signal to at least another receiver beacon.

Head-mounted augmented reality (AR) devices can track pose of a wearer's head to provide a three-dimensional virtual representation of objects in the wearer's environment. An electromagnetic (EM) tracking system can track head or body pose. A handheld user input device can include an EM emitter that generates an EM field, and the head-mounted AR device can include an EM sensor that senses the EM field. EM information from the sensor can be analyzed to determine location and/or orientation of the sensor and thereby the wearer's pose. The EM emitter and sensor may utilize time division multiplexing (TDM) or dynamic frequency tuning to operate at multiple frequencies. Voltage gain control may be implemented in the transmitter, rather than the sensor, allowing smaller and lighter weight sensor designs. The EM sensor can implement noise cancellation to reduce the level of EM interference generated by nearby audio speakers.

Head-mounted augmented reality (AR) devices can track pose of a wearer's head to provide a three-dimensional virtual representation of objects in the wearer's environment. An electromagnetic (EM) tracking system can track head or body pose. A handheld user input device can include an EM emitter that generates an EM field, and the head-mounted AR device can include an EM sensor that senses the EM field. EM information from the sensor can be analyzed to determine location and/or orientation of the sensor and thereby the wearer's pose. The EM emitter and sensor may utilize time division multiplexing (TDM) or dynamic frequency tuning to operate at multiple frequencies. Voltage gain control may be implemented in the transmitter, rather than the sensor, allowing smaller and lighter weight sensor designs. The EM sensor can implement noise cancellation to reduce the level of EM interference generated by nearby audio speakers.

Autonomous vehicles, equipped with 5G/6G technology, can cooperate to avoid collisions only after they determine which wireless address belongs to which other vehicle in traffic. Systems and methods provided herein can enable autonomous vehicles to identify other autonomous vehicles in view, thereby associating each vehicle with a particular wireless address. A first vehicle emits an infrared light pulse while simultaneously broadcasting a wireless message with its wireless address, while a second vehicle receives the wireless address and the simultaneous infrared pulse. The second vehicle can thereby identify the first vehicle spatially and by wireless communication. The second vehicle responds by transmitting a second wireless message and simultaneously emitting a second infrared pulse. The first vehicle receives the second infrared pulse and the second wireless message, thereby identifying the second vehicle. After such localization and identification, the vehicles can then cooperate effectively.

A robot and a method for localizing a robot are disclosed. The method for localizing a robot may include acquiring communication environment information including identifiers of access points and received signal strengths from the access points, generating an environmental profile for a current position of the robot based on the acquired communication environment information, comparing the generated environmental profile with a plurality of learning profiles associated with a plurality of regions, respectively, determining a learning profile corresponding to the environmental profile, based on the comparison, and determining a region associated with the determined learning profile as a current position of the robot. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm.

A robot and a method for localizing a robot are disclosed. The method for localizing a robot may include acquiring communication environment information including identifiers of access points and received signal strengths from the access points, generating an environmental profile for a current position of the robot based on the acquired communication environment information, comparing the generated environmental profile with a plurality of learning profiles associated with a plurality of regions, respectively, determining a learning profile corresponding to the environmental profile, based on the comparison, and determining a region associated with the determined learning profile as a current position of the robot. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm.

Orientation and position sensing methods and devices are disclosed. The described methods and devices are based on implementing magneto-electric-quasi-static fields for position and orientation sensing in lossy-dielectric, conducting, or metallic non-line-of-sight environments, where obstructions or occlusions or nearby objects exists that are lossy in nature and that typically perturb radio or electromagnetic wave signaling. Detailed experimental results highlighting the performance of the disclosed methods are also presented.

Orientation and position sensing methods and devices are disclosed. The described methods and devices are based on implementing magneto-electric-quasi-static fields for position and orientation sensing in lossy-dielectric, conducting, or metallic non-line-of-sight environments, where obstructions or occlusions or nearby objects exists that are lossy in nature and that typically perturb radio or electromagnetic wave signaling. Detailed experimental results highlighting the performance of the disclosed methods are also presented.

A system can be used with a drone delivery service that facilitates a service delivery via at least one drone delivery device. The system includes a code generator configured to generate beacon data that identifies a subscriber. A beacon generator is configured to generate a wireless homing beacon that indicates the beacon data, wherein the wireless homing beacon is detectable by the at least one drone delivery device to facilitate the service delivery to the subscriber by the drone delivery device at a location selected by the subscriber and a network interface is configured to communicate via a network. The system receives delivery image data captured after the service delivery by the drone delivery device.

Electronic device comprising: a lighting device, capable of illuminating a room or the like; a photodetector; an ultra-wideband pulse transmitter; an ultra-wideband pulse receiver; a controller, connectable to the internet;
wherein the controller is adapted for coding a signal and transmitting it to the lighting device and/or to the pulse transmitter, and for decoding a signal received by the photodetector or the pulse receiver; wherein the lighting device is adapted to transmit the signal as a LiFi signal and the pulse transmitter is adapted to transmit the signal as a UWB signal.