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To follow and maintain an appropriate distance to the selected target person (STP), the mobile robot is required to have capabilities: the human detection and tracking and an efficient following strategy with a smooth manner that does not appear threatening to the STP and surroundings. The efficient following strategy must integrate the STP position and the obstacle information to achieve smooth and safe human-following behaviors, especially in unknown environments where robot does not have understandings in advance. The purpose of this study is to propose a robust-adaptive-behavior strategy for mobile robots.

This paper presents a robust-adaptive-behavior strategy (RABS) based on the fuzzy inference mechanism to help the robot follow the STP effectively in various unknown environments with the real-time obstacle avoidance, both indoor and outdoor and on different robot platforms. In which, the traversability of robots’ unknown surrounding environments is analyzed by using the STP position and the obstacle information obtained from the two dimensional laser scan, whose purpose is to choose the highest-traversability-score direction (HTSD) and an adaptive-safe-following distance (ASFD). Then, the HTSD, the ASFD and the current velocity of the robot are considered as inputs of the fuzzy system to adjust its velocity smoothly.

The proposed RABS is verified by a set of experiments using a real big-heavy autonomous mobile robot (BH-AMR), with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg), aiding smart factories. The obtained results have shown that the proposed RABS equips the BH-AMR with the ability to follow the STP smoothly and safely even when the robot is moving at the maximum speed 1.5 (m/s).

In this paper, the autonomous mobile robot considers all environments as unknown even when it is working in mapped environments. This limitation is presented clearly in the future works section.

This proposed method can be used to help the autonomous mobile robot support persons in factories, hospitals, restaurants, supermarkets or at the airports.

This paper presents a RABS, including three new features: a fuzzy-based solution to help human-following robots maintain an appropriate distance to the STP safely and smoothly with the maximum velocity 1.5 (m/s); the proposed fuzzy-based solution, an adaptive vector field histogram and a new approach for the STP tracking is combined to follow the STP and avoid the collision simultaneously in unknown indoor and outdoor environments; the proposed RABS is considered for BH-AMRs (with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg)) to serve real tasks in smart factories.

Because mobile manipulators are unable to climb stairs, the elevator operation is a crucial capacity to help those kinds of robot systems work in modern multifloor buildings. Here, the elevator button manipulation is considered as an efficient approach to fulfill that requirement. Previously, some studies presented elevator button recognition algorithms while some others designed schemes for the button manipulation work. However, the mobile robot, the manipulator and the camera in their robot systems are asynchronous. Besides, the time-consuming calibration for the camera is inevitable, especially in changeable environments. This paper aims to present an alternative method for the elevator button manipulation to overcome mentioned shortcomings.

In this paper, the elevator button manipulation is conducted by using the visual-based self-driving mobile manipulator in which the autonomous mobile robot, the manipulator and the camera cooperate more efficiently. Namely, the mobile robot does not need to be located exactly in front of the elevator panel as the manipulator has the ability to adjust the initial frame of the camera based on the system kinematic synchronization. In addition, the proposed method does not require the real world coordinates of elevator buttons, but uniquely using their pixel positions. By doing this, not only is the projection from two-dimensional pixel coordinates to three-dimensional (3D) real world coordinates unnecessary, but also the calibration of the camera is not required.

The proposed method is experimentally verified by using a visual-based self-driving mobile manipulator. This robotic system is the integration of an autonomous mobile robot, a manipulator and a camera mounted on the end-effector of the manipulator.

Because the surface of the elevator button panel is usually mirror-like, the elevator button detection is easily affected by the glare and the brightness of the environmental light condition.

This robot system can be used for the goods delivery or the patrol in modern multifloor buildings.

This paper includes three new features: simultaneously detecting and manipulating elevator buttons without the projection from pixel coordinates to 3D real world coordinates, a kinematic synchronization to help the robot system eliminate accumulated errors and a safe human-like elevator button manipulation.

Collision avoidance is considered as a crucial issue in mobile robotic navigation to guarantee the safety of robots as well as working surroundings, especially for humans. Therefore, the position and velocity of obstacles appearing in the working space of the self-driving mobile robot should be observed to help the robot predict the collision and choose traversable directions. This paper aims to propose a new approach for obstacle tracking, dubbed MoDeT.

First, all long lines, such as walls, are extracted from the 2D-laser scan and considered as static obstacles (or mapped obstacles). Second, a density-based procedure is implemented to cluster nonwall obstacles. These clusters are then geometrically fitted as ellipses. Finally, the combination of Kalman filter and global nearest-neighbor (GNN) method is used to track obstacles’ position and velocity.

The proposed method (MoDeT) is experimentally verified by using an autonomous mobile robot (AMR) named AMR SR300. The MoDeT is found to provide better performance in comparison with previous methods for self-driving mobile robots.

The robot can only see a part of the object, depending on the light detection and ranging scan view. As a consequence, geometrical features of the obstacle are sometimes changed, especially when the robot is moving fast.

This proposed method is to serve the navigation and path planning for the AMR.

(a) Proposing an extended weighted line extractor, (b) proposing a density-based obstacle detection and (c) implementing a combination of methods [in (a) and (b) constant acceleration Kalman and GNN] to obtain obstacles’ properties.

To examine the mechanisms through which downsizing affects employees' affective commitment to the organization in two Korean banks.

A total of 910 questionnaires were completed by bank employees. Data was analysed using hierarchical regression and LISREL path analysis techniques. Twenty semi‐structured interviews were also conducted.

The results show that the more severe the extent of downsizing, the lower employees' affective commitment to the organization. Moreover, downsizing has an impact on employees' affective commitment to the organization through several of the daily work experiences of employees. Thus, downsizing affects employees' affective commitment to the organization both directly and indirectly. However, its indirect impact is much stronger.

The research was conducted in Korea and may have limited generalizability to Western cultures.

Sympathetic management of downsizing can minimize the negative impact on the affective commitment of surviving employees.

This paper offers insights into organizational commitment in a (relatively under‐researched) Korean context.

This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.