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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.

This paper aims to develop a robust person tracking method for human following robots. The tracking system adopts the multimodal fusion results of millimeter wave (MMW) radars and monocular cameras for perception. A prototype of human following robot is developed and evaluated by using the proposed tracking system.

Limited by angular resolution, point clouds from MMW radars are too sparse to form features for human detection. Monocular cameras can provide semantic information for objects in view, but cannot provide spatial locations. Considering the complementarity of the two sensors, a sensor fusion algorithm based on multimodal data combination is proposed to identify and localize the target person under challenging conditions. In addition, a closed-loop controller is designed for the robot to follow the target person with expected distance.

A series of experiments under different circumstances are carried out to validate the fusion-based tracking method. Experimental results show that the average tracking errors are around 0.1 m. It is also found that the robot can handle different situations and overcome short-term interference, continually track and follow the target person.

This paper proposed a robust tracking system with the fusion of MMW radars and cameras. Interference such as occlusion and overlapping are well handled with the help of the velocity information from the radars. Compared to other state-of-the-art plans, the sensor fusion method is cost-effective and requires no additional tags with people. Its stable performance shows good application prospects in human following robots.

This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.

Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.

A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios.

This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

This paper aims to present a system that is fully capable of addressing the issue of detection, tracking and following pedestrians, which is a very challenging task, especially when it is considered for using in large outdoors infrastructures. Three modules, detection, tracking and following, are integrated and tested over long distances in semi-structured scenarios, where static or dynamic obstacles, including other pedestrians, can be found.

The detection is based on the probabilistic fusion of a laser scanner and a camera. The tracking module pairs observations with previously detected targets by using Kalman Filters and a Mahalanobis-distance. The following module allows to safely pursue the target by using a well-defined navigation scheme.

The system can track pedestrians from static position to 3.46 m/s (running). It handles occlusions, crossings or miss-detections, keeping track of the position even if the pedestrian is only detected in 55/per cent of the observations. Moreover, it autonomously selects and follows a target at a maximum speed of 1.46 m/s.

The main novelty of this study is the integration of the three algorithms in a fully operational system, tested in real outdoor scenarios. Furthermore, the addition of labelling to the detection algorithm allows using the full range of a single sensor while preserving the high performance of a combined detection. False-positives’ rate is reduced by handling the uncertainty level when pairing observations. The inclusion of pedestrian speed in the model speeds up and simplifies tracking process. Finally, the most suitable target is automatically selected by a scoring system.

The aim of this paper is to develop autonomous mobile home healthcare robots, which are capable of observing patients’ motions, recognizing the patients’ behaviours based on observation data, and providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost‐effective, safe and easier at‐home rehabilitation to most motor‐function impaired patients (MIPs).

The paper has developed following programs/control algorithms: control algorithms for a mobile robot to track and follow human motions, to measure human joint trajectories, and to calculate angles of lower limb joints; and algorithms for recognizing human gait behaviours based on the calculated joints angle data.

A Hidden Markov Model (HMM) based human gait behaviour recognition taking lower limb joint angles and body angle as input was proposed. The proposed HMM based gait behaviour recognition is compared with the Nearest Neighbour (NN) classification methods. Experimental results showed that a human gait behaviour recognition using HMM can be achieved from the lower limb joint trajectory with higher accuracy than compared classification methods.

The research addresses human motion tracking and recognition by a mobile robot. Human gait behaviour recognition is HMM based lower limb joints and body angle data from extracted from kinect sensor at the mobile robot.

In corridor environments, human-following robot encounter difficulties when the target turning around at the corridor intersections, as walls may cause complete occlusion. This paper aims to propose a collision-free following system for robot to track humans in corridors without a prior map.

In addition to following a target and avoiding collisions robustly, the proposed system calculates the positions of walls in the environment in real-time. This allows the system to maintain a stable tracking of the target even if it is obscured after turning. The proposed solution is integrated into a four-wheeled differential drive mobile robot to follow a target in a corridor environment in real-world.

The experimental results demonstrate that the robot equipped with the proposed system is capable of avoiding obstacles and following a human target robustly in the corridors. Moreover, the robot achieves a 90% success rate in maintaining a stable tracking of the target after the target turns around a corner with high speed.

This paper proposes a human target following system incorporating three novel features: a path planning method based on wall positions is introduced to ensure stable tracking of the target even when it is obscured due to target turns; improvements are made to the random sample consensus (RANSAC) algorithm, enhancing its accuracy in calculating wall positions. The system is integrated into a four-wheeled differential drive mobile robot effectively demonstrates its remarkable robustness and real-time performance.

The purpose of this paper is to describe research to enable a robust navigation of guide robots in erratic environments with partial sensor information.

Two techniques were developed. One is a robust node discrimination method by using an adaptive sensor matching method. The other is a robot navigation technique with partial sensor information.

A successful navigation was implemented in erratic environments using partial sensor information.

First robot navigation is addressed along the generalized Voronoi graph (GVG) with partial sensor information. A solution is also provided for a phantom node detection problem, which is one of the main defects in GVG navigation.

This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).

The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.

It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.

In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

Drawing on person–environment fit theory, this study aims to investigate how the relationships between service task types (i.e. utilitarian and hedonic service tasks) and perceived authenticity (i.e. service and brand authenticity) differ under different conditions of service providers (human employee vs service robot). This study further examines whether customers’ stereotypes toward service robots (competence vs warmth) moderate the relationship between service types and perceived authenticity.

Using a 2 × 2 between-subjects experimental design, Study 1 examines a casual restaurant, whereas Study 2 assesses a theme park restaurant. Analysis of covariance and PROCESS are used to analyze the data.

Both studies reveal that human service providers in hedonic services positively affect service and brand authenticity more than robotic employees. Additionally, the robot competence stereotype moderates the relationship between hedonic services, service and brand authenticity, whereas the robot warmth stereotype moderates the relationship between hedonic services and brand authenticity in Study 2.

Restaurant managers need to understand which functions and types of service outlets are best suited for service robots in different service contexts. Robot–environment fit should be considered when developers design and managers select robots for their restaurants.

This study blazes a new theoretical trail of service robot research to systematically propose customer experiences with different service types by drawing upon person–environment fit theory and examining the moderating role of customers’ stereotypes toward service robots.

This study aims to examine humans’ reactions to service robots’ display of warmth in robot-to-robot interactions – a setting in which humans’ impressions of a service robot will not only be based on what this robot does in relation to humans, but also on what it does to other robots.

Service robot display of warmth was manipulated in an experimental setting in such a way that a service robot A expressed low versus high levels of warmth in relation to another service robot B.

The results indicate that a high level of warmth expressed by robot A vis-à-vis robot B boosted humans’ overall evaluations of A, and that this influence was mediated by the perceived humanness and the perceived happiness of A.

Numerous studies have examined humans’ reactions when they interact with a service robot or other synthetic agents that provide service. Future service encounters, however, will comprise also multi-robot systems, which means that there will be many opportunities for humans to be exposed to robot-to-robot interactions. Yet, this setting has hitherto rarely been examined in the service literature.