In context
Vertical metal tanks are critical metering instruments for trade settlement in the petrochemical industry, requiring high accuracy and regular calibration. Traditional manual measurement methods are hazardous and inefficient, driving the need for automated solutions that combine precision optics with robotic platforms.
What was reported
Researchers proposed a measurement method (LTWR) integrating a laser tracker and a wall-climbing robot for vertical tank volume measurement. The robot, equipped with a laser rangefinder, autonomously navigates the tank wall while the tracker records target positions to compute measurement points. The study focused on path planning based on measuring point distribution (MPD), comparing two calculation approaches: a fitting method suitable for circumferentially sparse points and an infinitesimal method for vertically sparse points.
Simulations on a virtual tank model and field experiments on a real tank were conducted. The results showed that the proposed path, a vertical reciprocating path (VRP), significantly reduced measurement time compared to horizontal winding paths (HWP). For a 50,000 m³ tank, VRP took 27.5 minutes versus over 106 minutes for HWP. Measurement errors in both simulation and physical experiments were less than 0.04%, and the overall loss was smaller than existing methods.
The study also analyzed the effect of the number of measuring point columns (n) on accuracy, finding that increasing n beyond 16 did not consistently improve precision due to random wall irregularities.
Why it mattered
This work demonstrated a practical automated approach for high-precision tank calibration, reducing human exposure to hazardous environments while improving efficiency. The path planning methodology, grounded in MPD analysis, offers a framework for optimizing robotic measurement tasks in large-scale industrial structures.
“The results show that the measurement based on the planned path can be completed quickly, and the measurement errors in both simulation and experiments are less than 0.04%.”
Source: 《机器人》期刊 (robot.sia.cn) · Published 2024-02-04 · “立式罐容量计量中爬壁机器人的路径规划”