A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Alhmiedat, Tareq; Marei, Ashraf M.; Messoudi, Wassim; Albelwi, Saleh; Bushnag, Anas; Bassfar, Zaid; Alnajjar, Fady; Elfaki, Abdelrahman Osman

doi:10.3390/machines11020158

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…In this case, the three attempts failed regarding the expected route and the location of the final position. This is supported by Alhmiedat et al [20], who reported an average localization error of 0.51 m and 86% user acceptability.…”

Section: Map and Navigation Functionality Of Peppersupporting

confidence: 63%

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An Exploration of the Pepper Robot’s Capabilities: Unveiling Its Potential

Mishra,

Romero,

Pande

et al. 2023

Applied Sciences

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The application of social robots is increasing daily due to their various capabilities. In real settings, social robots have been successfully deployed in multiple domains, such as health, education, and the service industry. However, it is crucial to identify the strengths and limitations of a social robot before it can be employed in a real-life scenario. In this study, we explore and examine the capabilities of a humanoid robot, ‘Pepper’, which can be programmed to interact with humans. The present paper investigates five capabilities of Pepper: mapping and navigation, speech, hearing, object detection, and face detection. We attempt to study each of these capabilities in-depth with the help of experiments conducted in the laboratory. It has been identified that Pepper’s sound and speech recognition capabilities yielded satisfactory results, even with various accents. On the other hand, Pepper’s built-in SLAM navigation is unreliable, making it difficult to reach destinations accurately due to generated maps. Moreover, its object and face detection capabilities delivered inconsistent outcomes. This shows that Pepper has potential for improvement in its current capabilities. However, previous studies showed that with the integration of artificial intelligence techniques, a social robot’s capabilities can be enhanced significantly. In the future, we will focus on such integration in the Pepper robot, and the present study’s exploration will help to establish a baseline comprehension of the in-built artificial intelligence of Pepper. The findings of the present paper provide insights to researchers and practitioners planning to use the Pepper robot in their future work.

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Section: Map and Navigation Functionality Of Peppersupporting

confidence: 63%

“…It facilitates the mapping of the environment and the location of objects to produce a safety map [14]. Alhmiedat et al [20] highlighted the problems with Pepper's navigation system. Gomez et al [21] mentioned that due to the small-range LIDARs and RGB, Pepper is not able to self-localize in larger surroundings.…”

Section: Related Workmentioning

confidence: 99%

An Exploration of the Pepper Robot’s Capabilities: Unveiling Its Potential

Mishra,

Romero,

Pande

et al. 2023

Applied Sciences

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“…The process by which a robot selects its own position, direction, and path to reach a destination is referred to as robot navigation. Mobile robots utilize a variety of sensors to scan the environment and gather geometric information about the area of interest [5]. The authors of [6] revealed that the creation or representation of a map is achieved through the simultaneous localization and mapping (SLAM) approach.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Alqobali,

Alshmrani,

Alnasser

et al. 2023

Applied Sciences

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Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave radars to construct an environment map. However, in robot navigation, scene understanding has become essential for comprehending the area of interest and achieving improved navigation results. The semantic model of the indoor environment provides the robot with a representation that is closer to human perception, thereby enhancing the navigation task and human–robot interaction. However, semantic navigation systems require the utilization of multiple components, including geometry-based and vision-based systems. This paper presents a comprehensive review and critical analysis of recently developed robot semantic navigation systems in the context of their applications for semantic robot navigation in indoor environments. Additionally, we propose a set of evaluation metrics that can be considered to assess the efficiency of any robot semantic navigation system.

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“…synchronous positioning and mapping (SLAM) technology is also a commonly technology in advanced sweeping robots. SLAM combines data from multiple sensors, such as cameras and laser rangefinders, and allows robots to navigate autonomously by continuously updating maps and positions [3]. Sweeping robots use various positioning technologies to navigate effectively and clean different types of spaces, and adapt to the layout and obstacles in the environment.…”

Section: Introductionmentioning

confidence: 99%

Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell

Chen,

Li,

2023

Meas. Sci. Technol.

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The existing source localization based on acoustic emission technology often depends on the assumption of constant wave velocity inside the material. However, this assumption is hardly satisfied in actual engineering. The uncertainty of wave velocity can easily lead to low localization accuracy of sweeping robots. To overcome these deficiencies, a complete probability multi-directional measurement method based on the Bayesian inference mechanism is proposed. In the proposed method, based on the Bayesian probabilistic model, the extracted sensor time difference is subjected to probabilistic inference using the coordinate input model to determine the posterior distribution of the source’s position and wave velocity of the given arrival time. Compared with the traditional time-difference method, the proposed method achieves excellent results and outperforms the standard time-difference method in localization accuracy and anti-interference. In addition, the proposed method can conveniently, quickly, and effectively determine the location of the colliding point without considering the source emission time and wave velocity. The research in this paper provides an effective method for solving the collision localization problem of the sweeping robot shell under the acoustic emission time and wave velocity are unknown.

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A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Cited by 16 publications

References 26 publications

An Exploration of the Pepper Robot’s Capabilities: Unveiling Its Potential

An Exploration of the Pepper Robot’s Capabilities: Unveiling Its Potential

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell

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