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One of the most relevant problems related to Unmanned Aerial Vehicle's (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy.
RESUMO
What does transparency mean in a shared autonomy framework? Different ways of understanding system transparency in human-robot interaction can be found in the state of the art. In one of the most common interpretations of the term, transparency is the observability and predictability of the system behavior, the understanding of what the system is doing, why, and what it will do next. Since the main methods to improve this kind of transparency are based on interface design and training, transparency is usually considered a property of such interfaces, while natural language explanations are a popular way to achieve transparent interfaces. Mechanical transparency is the robot capacity to follow human movements without human-perceptible resistive forces. Transparency improves system performance, helping reduce human errors, and builds trust in the system. One of the principles of user-centered design is to keep the user aware of the state of the system: a transparent design is a user-centered design. This article presents a review of the definitions and methods to improve transparency for applications with different interaction requirements and autonomy degrees, in order to clarify the role of transparency in shared autonomy, as well as to identify research gaps and potential future developments.
RESUMO
OBJECTIVE: The aim of this study was to evaluate two models for adjusting autonomy in mobile robots to find out the best way for the operator to interact with the system with as many robots as possible. The first model is the most used in mobile robots; the second proposes a flexible autonomy management. BACKGROUND: There are different ways of adjusting the autonomy level in man-machine systems: adjustable autonomy, in which the operator has the initiative over the autonomy level; adaptive autonomy, in which the autonomy level is adjusted depending on the task and context; and mixed initiatives. One of the drawbacks of using adjustable autonomy is that it is claimed not to be flexible enough, resulting in a high operator workload. We propose and evaluate a flexible adjustable autonomy model for robot-team supervision. METHOD: Two experiments were designed to test the scalability and performance of the man-machine system with two alternative configurations for the autonomy management. The independent variable is the number of robots, and the measured variable is the man-machine system performance. The experiments are between subjects. We have used ANOVA and Bonferroni post hoc analysis for analyzing the results. RESULTS: On the basis of these analyses,we conclude that a flexible adjustable autonomy model results in better performance than the classic, rigid one, in which the operator directly chooses the autonomy level. CONCLUSION: Flexible autonomy adjustment permits one operator to control a team of robots with better results in terms of performance and robot use, as he or she can directly act at the error level, leaving the responsibility of readjusting and resuming the task to the system and hence reducing the operator's workload. APPLICATION: The results can be applied to exploration robotics, mainly, in which one operator controls a team of robots. In general, these principles can be extended to other single-man/multiple-machine systems.
