Abstract: A control system and method locates a partially or fully occluded target object in an area of interest. The location of the occluded object may be determined using visual information from a vision sensor and RF-based location information. Determining the location of the target object in this manner may effectively allow the control system to “see through” obstructions that are occluding the object. Model-based and/or deep-learning techniques may then be employed to move a robot into range relative to the target object to perform a predetermined (e.g., grasping) operation. This operation may be performed while the object is still in the occluded state or after a decluttering operation has been performed to remove one or more obstructions that are occluding light-of-sight vision to the object.

Abstract: A system for facilitating pose detection using multi-chirp FMCW radar is configurable to emit a multi-chirp frequency modulated continuous wave (FMCW) radar signal comprising a high-bandwidth chirp and a low-bandwidth chirp, detect a reflected multi-chirp FMCW radar signal comprising a reflected high-bandwidth chirp and a reflected low-bandwidth chirp, determine a first object pose data using the reflected high-bandwidth chirp, and determine a second object pose data using the reflected low-bandwidth chirp.

Abstract: According to one aspect of the disclosure, a mobile augmented reality (AR) system can include: a receiver configured to receive radio frequency (RF) signals from one or more items located within an environment; a tracking module configured to generate tracking data responsive to a location of the system within the environment over time; a display device; and one or more processors configured to determine a location of at least one of the one or more items within the environment using the received RF signals and the tracking data, and generate a visual representation of the location of the at least one item for display on the display device.

Abstract: Described is the design, implementation, and evaluation of a robotic system configured to search for and retrieve RFID-tagged items in line-of-sight, non-line-of-sight, and fully-occluded settings. The robotic system comprises a robotic arm having a camera and antenna strapped around a portion thereof (e.g. a gripper) and a controller configured to receive information from the camera and (radio frequency) RF information via the antenna and configured to use the information provided thereto to implement a method that geometrically fuses at least RF and visual information. This technique reduces uncertainty about the location of a target object even when the object is fully occluded. Also described is a reinforcement-learning network that uses fused RF-visual information to efficiently localize, maneuver toward, and grasp a target object.

Abstract: A control system and method locates a partially or fully occluded target object in an area of interest. The location of the occluded object may be determined using visual information from a vision sensor and RF-based location information. Determining the location of the target object in this manner may effectively allow the control system to “see through” obstructions that are occluding the object. Model-based and/or deep-learning techniques may then be employed to move a robot into range relative to the target object to perform a predetermined (e.g., grasping) operation. This operation may be performed while the object is still in the occluded state or after a decluttering operation has been performed to remove one or more obstructions that are occluding light-of-sight vision to the object.