Preliminary Experimental Results of a Temporal Planning-Based System for Autonomous Inspection Using UGV

摘要

Abstract Inspection and maintenance (IM) operations in offshore oil & gas platforms involve significant challenges due to the harsh conditions in such remote environments. Therefore, there is a need for robotic solutions that can accomplish IM missions autonomously, a goal that has become more feasible in the last years due to advances in instrumentation, perception and artificial intelligence (AI). In this paper, we present initial experimental results of the Taurob unmanned ground vehicle (UGV) performing autonomous inspection at Equinor’s K-Lab Test Centre, located in Haugesund, Norway. The proposed solution employs the simultaneous task planning (STP) algorithm, introduced by Furelos-Blanco in [1], as a high-level action planner. STP can take into account durative high-level actions, such as “visit a specific location”, “inspect a sensor”, or “take a picture of a specified component” and, in addition, can replan online in case of events such as low battery status, or the need to revisit a location. Communication with the robot is achieved via Equinor’s Integration and Supervisory Control of Autonomous Robots (ISAR) framework, which guarantees a fast and secure wi-fi connection. Two types of experiments were carried out: 1) A basic inspection round, which involves visiting a sequence of predefined waypoints; 2) A replanning scenario, which also takes into account the battery status. Our preliminary tests, where fast and efficient plans were computed and executed in real time, including replanning, showed that connecting the guidance and control system of a UGV with a high-level planner like STP is a promising approach to increased autonomy in IM missions.