Abstract: A system includes a navigation apparatus and controller. The navigation apparatus includes a body assembly that includes a plurality of pneumatic or artificial muscles that are configured to interact with a sidewall of the conduit and move the body assembly through the conduit. The plurality of pneumatic or artificial muscles are independently actuated to steer the body assembly. The navigation apparatus also includes a sensor array coupled to the body assembly. The sensor array is positioned to interact with the sidewall of the conduit and provide signals as the body assembly moves through the interior cavity. The controller is communicatively coupled to the sensor array and configured to determine a characteristic of the conduit based on the signals provided by the sensor array.

Abstract: A system includes a navigation apparatus and controller. The navigation apparatus includes a body assembly that includes a plurality of pneumatic or artificial muscles that are configured to interact with a sidewall of the conduit and move the body assembly through the conduit. The plurality of pneumatic or artificial muscles are independently actuated to steer the body assembly. The navigation apparatus also includes a sensor array coupled to the body assembly. The sensor array is positioned to interact with the sidewall of the conduit and provide signals as the body assembly moves through the interior cavity. The controller is communicatively coupled to the sensor array and configured to determine a characteristic of the conduit based on the signals provided by the sensor array.

Abstract: A system for performing industrial tasks includes a robot and a computing device. The robot includes one or more sensors that collect data corresponding to the robot and an environment surrounding the robot. The computing device includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the collected data from the robot, generate a virtual recreation of the robot and the environment surrounding the robot, receive inputs from a human operator controlling the robot to demonstrate an industrial task. The system is configured to learn how to perform the industrial task based on the human operator's demonstration of the task, and perform, via the robot, the industrial task autonomously or semi-autonomously.

Abstract: A robotic body scanning system includes a robotic manipulator, a force sensor, a probe, a surface sensing system, and a computing device. The probe is attached to the robotic manipulator and configured to scan the portion of the human body. The surface sensing system is configured to detect a surface of the portion of the human body and generate data representing the portion of the human body. The computing device is configured to receive data representing the portion of the human body from said surface sensing system and generate two or three-dimensional representations of the portion of the human body. The computing device includes a trajectory generation module configured to generate an adapted trajectory for the probe to follow based on the two or three-dimensional representations. The robotic manipulator is configured to move the probe along the adapted trajectory along the portion of the human body.

Abstract: A system includes at least one unmanned aerial vehicle and at least one unmanned vehicle communicatively coupled to the unmanned aerial vehicle. The unmanned aerial vehicle includes a propulsion system and an onboard pilot system configured to determine a flight path for the unmanned aerial vehicle. The unmanned vehicle includes a propulsion system and a localization system configured to determine a location of the unmanned aerial vehicle relative to the unmanned vehicle. The unmanned vehicle further includes a communication component configured to transmit location information to the unmanned aerial vehicle. The onboard pilot system is configured to determine the flight path based on the location information provided by the unmanned vehicle.

Abstract: The present approach employs a context-aware simulation platform to facilitate control of a robot remote from an operator. Such a platform may use the prior domain/task knowledge along with the sensory feedback from the remote robot to infer context and may use inferred context to dynamically change one or both of simulation parameters and a robot-environment-task state being simulated. In some implementations, the simulator instances make forward predictions of their state based on task and robot constraints. In accordance with this approach, an operator may therefore issue a general command or instruction to a robot and based on this generalized guidance, the actions taken by the robot may be simulated, and the corresponding results visually presented to the operator prior to evaluate prior to the action being taken.

Abstract: A method for generating a three-dimensional model of an asset includes receiving input parameters corresponding to constraints of a mission plan for operating an unmanned vehicle around an asset, generating the mission plan based on the input parameters including information of a representative asset type, wherein the mission plan includes waypoints identifying locations and orientations of one or more image sensors of the unmanned vehicle, generating a flight path for the unmanned vehicle connecting the waypoints that satisfy one or more predefined criteria, monitoring a vehicle state of the unmanned vehicle during execution of the flight path from one waypoint to the next waypoint, determining, at each waypoint, a local geometry of the asset sensed by the one or more image sensors, changing the mission plan on-the-fly based on the local geometry, and capturing images of the asset along waypoints of the changed mission plan.

Abstract: A method for generating a three-dimensional model of an asset includes receiving input parameters corresponding to constraints of a mission plan for operating an unmanned vehicle around an asset, generating the mission plan based on the input parameters including information of a representative asset type, wherein the mission plan includes waypoints identifying locations and orientations of one or more image sensors of the unmanned vehicle, generating a flight path for the unmanned vehicle connecting the waypoints that satisfy one or more predefined criteria, monitoring a vehicle state of the unmanned vehicle during execution of the flight path from one waypoint to the next waypoint, determining, at each waypoint, a local geometry of the asset sensed by the one or more image sensors, changing the mission plan on-the-fly based on the local geometry, and capturing images of the asset along waypoints of the changed mission plan.

Abstract: The present approach employs a context-aware simulation platform to facilitate control of a robot remote from an operator. Such a platform may use the prior domain/task knowledge along with the sensory feedback from the remote robot to infer context and may use inferred context to dynamically change one or both of simulation parameters and a robot-environment-task state being simulated. In some implementations, the simulator instances make forward predictions of their state based on task and robot constraints. In accordance with this approach, an operator may therefore issue a general command or instruction to a robot and based on this generalized guidance, the actions taken by the robot may be simulated, and the corresponding results visually presented to the operator prior to evaluate prior to the action being taken.

Abstract: A system for performing industrial tasks includes a robot and a computing device. The robot includes one or more sensors that collect data corresponding to the robot and an environment surrounding the robot. The computing device includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the collected data from the robot, generate a virtual recreation of the robot and the environment surrounding the robot, receive inputs from a human operator controlling the robot to demonstrate an industrial task. The system is configured to learn how to perform the industrial task based on the human operator's demonstration of the task, and perform, via the robot, the industrial task autonomously or semi-autonomously.