Development of an oil and gas refinery inspection robot

Oil and gas refineries present challenging environments in which to work and operate, especially in places like the Middle East where temperatures can reach 50 °C and sand storms which can reduce visibility to a few meters. In addition, there can be gas or steam leaks which present health and safety hazards to the workers. At present, continuous operation of these plants requires that human workers venture out into these conditions in order to observe and report on the conditions within the plant. The goal of this work is to design, fabricate, assemble, and test an inspection robot in an effort to reduce the exposure and risks to human operators while increasing the flexibility and range of remote observations provided by a mobile robot. In this paper, we will report on the design approach taken, the subsystems identified and developed, the software environment chosen, and the application tasks envisioned. We will also report on the challenges of developing a robust localization algorithm for use in the challenging environment of a refinery as well as the needs for robust wireless communications in order to maintain command and control from the operators control room. An overview of the fivedegree-of-freedom arm designed and fabricated, and its realtime control will also be presented. Results from GPS navigation and localization experiments will be presented. While average errors during parked operations were often less than one meter for the WAAS enabled GPS system, locational errors during dynamic operations were often more than three meters. This is due to multi-path signals near building structures and piping infrastructure. Real-time arm control has been implemented using FPGAs and while tuning presented some challenge, the FPGA has provided smooth and repeatable operation. Sensors include gas detectors, acoustic sensors, thermal imaging, and video camera streaming. In addition, we will report on a multi-faceted approach to localization using three different sensor technologies and integrated using a Kalman filter.