A vehicle control framework enables improved attitude tracking and mode switching of a vehicle by modeling the vehicle as a switched system, where the vehicle is operable for changing a geometric configuration during flight. The vehicle control framework implements a control law that accommodates modeling uncertainties and unknown external disturbances. The vehicle also enforces a switching time constrained by a minimum dwell time which can be adaptively updated based on attitude errors.

A method for controlling a flight-capable drone in an elevator shaft of an elevator system uses an elevator system inspection arrangement configured for carrying out the method. The method comprises the following steps: receiving elevator shaft segment information provided by the elevator system that indicates which volume segment of the elevator shaft is currently designated to be off-limits for the drone; and controlling the drone along a flight path automatically determined by the drone, wherein the drone determines the flight path such that the drone travels exclusively outside of the volume segment designated as off-limits for the drone, wherein the drone determines the flight path taking into account the received elevator shaft segment information. By exchanging the elevator shaft segment information with the elevator system, the drone is able to initiate evasive maneuvers in good time in order to prevent collisions with fast-moving components of the elevator system.

A robot configured for inspection of a pipe is disclosed herein. The robot can include a housing, a sensing device coupled to the housing, a carbon-neutral power source positioned within the housing, a plurality of wheels rotatably coupled to the housing, and a computing device communicably coupled to the sensing device and the carbon-neutral power source. The computing device can include a processing unit and a memory to store a software stack that, when executed by the processing unit, causes the computing device to: receive a signal from the sensing device, detect a condition of the pipe based on the received signal, generate a situational alert based on the detected condition, and transmit the situational alert to a user of the robot.

A system for providing an interactive inspection map of an inspection surface inspected by an inspection robot includes an inspection circuit structured to interpret inspection data of the inspection surface from the inspection robot and a user interaction circuit structured to interpret a user focus value from a user device. The user focus value includes an activation state value. The system further includes an inspection visualization circuit structured to provide the interactive inspection map to the user device in response to the user focus value. The interactive inspection map is based on the inspection data. Also, the system includes an inspection data validation circuit that determines an inspection data validity description of the inspection data. The inspection data validity description is provided as a display layer on the interactive inspection map to indicate whether the inspection data is valid.

A motion control method for an adaptive self-reconfigurable pipeline robot based on environmental perception includes: acquiring internal images of the pipeline for scene recognition, segmenting planar surfaces and curved surfaces in the images according to recognition results, and extracting boundary lines of the pipeline; calculating a straight-pipe width, a bent-pipe curvature, a slope angle, and a step height to analyze passability of the robot; designing a path planner and a swing-arm planner to generate a reference trajectory and a swing-arm angle sequence of the robot, performing smoothing, and inputting the reference trajectory and the swing-arm angle sequence into a model predictive control (MPC) motion controller; estimating a position and state of the robot through an Error State Kalman Filter (ESKF) algorithm, and inputting estimation results and collision warning signals into the MPC motion controller; and finally outputting a signal for control of a motor and a swing-arm motor.

An in-oil autonomous operation detection robot of a storage tank bottom plate and an autonomous operation detection method are provided. The in-oil autonomous operation detection robot of the storage tank bottom plate includes a motion module, a positioning and attitude recognition module, an obstacle avoidance module, a detection module and a control module, wherein the motion module is configured to adjust and control a motion direction, a speed and an attitude of the robot under the control of the control module; the positioning and attitude recognition module is configured to recognize a position and an attitude of the robot under the control of the control module; the obstacle avoidance module is configured to avoid obstacles under the control of the control module; and the detection module is configured to detect corrosions of the bottom plate.

A localization system comprises: a device; a master unit which wirelessly transmits a first localization signal; a plurality of lateration units distributed about the area within which the device is being localized, wherein each lateration unit of the plurality independently starts its own timer upon its receipt of the first localization signal; and a localization unit. The device receives the first localization signal and responsively wirelessly transmits a second localization signal. Each of the lateration units: independently receives the second localization signal; stops its respective timer responsive to receipt of the second localization signal; and wirelessly transmits a timer count signal to a localization unit. The timer count signal identifies the transmitting lateration unit and a count of its respective timer. The localization unit utilizes the plurality of timer along with respective distances between the master unit and the lateration units to localize the first device via time-of-flight lateration.