Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered Dynamic Environments with Dynamic Target Tracking

Hacene, Nacer; Mendil, Boubekeur

doi:10.1007/s11633-020-1264-x

Cited by 21 publications

(10 citation statements)

References 41 publications

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“…Particularly, the overall control actions of the vehicles are then generated by a weighted combination of achieving these different goals. Due to the multi-objective and distributed features, behavior-based approach has attracted extensive attention over the past few decades in the research areas of multi-agent cooperation and coordination [60][61][62][63][64] .…”

Section: Behavior-based Approachesmentioning

confidence: 99%

Formation control of multiple autonomous underwater vehicles: a review

Yan¹,

Xu²,

Yang³

et al. 2023

Intell Robot

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This paper presents a comprehensive overview of recent developments in formation control of multiple autonomous underwater vehicles (AUVs). Several commonly used structures and approaches for formation coordination are listed, and the advantages and deficiencies of each method are discussed. The difficulties confronted in synthesis of a practical AUVs formation system are clarified and analyzed in terms of the characteristic of AUVs, adverse underwater environments, and communication constraints. The state-of-the-art solutions available for addressing these challenges are reviewed comprehensively. Based on that, a brief discussion is made, and a list of promising future work is pointed out, which aims to be helpful for the further promotion of AUVs formation applications.

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Section: Behavior-based Approachesmentioning

confidence: 99%

Formation control of multiple autonomous underwater vehicles: a review

Yan¹,

Xu²,

Yang³

et al. 2023

Intell Robot

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“…As service robots are starting to coexist with humans, they ought to deal with two fundamental aspects: sturdy navigation in cluttered environments [1,2] and effective humanrobotic interaction (HRI). There have been many research conducted in navigation area whereas, a significant amount of problems in HRI field need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Non-Verbal Human-Robot Interaction Using Neural Network for The Application of Service Robot

Soomro

Shamsudin

Rahim

et al. 2023

IIUMEJ

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Service robots are prevailing in many industries to assist humans in conducting repetitive tasks, which require a natural interaction called Human Robot Interaction (HRI). In particular, nonverbal HRI plays an important role in social interactions, which highlights the need to accurately detect the subject's attention by evaluating the programmed cues. In this paper, a conceptual attentiveness model algorithm called Attentive Recognition Model (ARM) is proposed to recognize a person’s attentiveness, which improves the accuracy of detection and subjective experience during nonverbal HRI using three combined detection models: face tracking, iris tracking and eye blinking. The face tracking model was trained using a Long Short-Term Memory (LSTM) neural network, which is based on deep learning. Meanwhile, the iris tracking and eye blinking use a mathematical model. The eye blinking model uses a random face landmark point to calculate the Eye Aspect Ratio (EAR), which is much more reliable compared to the prior method, which could detect a person blinking at a further distance even if the person was not blinking. The conducted experiments for face and iris tracking were able to detect direction up to 2 meters. Meanwhile, the tested eye blinking model gave an accuracy of 83.33% at up to 2 meters. The overall attentive accuracy of ARM was up to 85.7%. The experiments showed that the service robot was able to understand the programmed cues and hence perform certain tasks, such as approaching the interested person. ABSTRAK: Robot perkhidmatan lazim dalam banyak industri untuk membantu manusia menjalankan tugas berulang, yang memerlukan interaksi semula jadi yang dipanggil Interaksi Robot Manusia (HRI). Khususnya, HRI bukan lisan memainkan peranan penting dalam interaksi sosial, yang menonjolkan keperluan untuk mengesan perhatian subjek dengan tepat dengan menilai isyarat yang diprogramkan. Dalam makalah ini, algoritma model perhatian konseptual yang dipanggil Model Pengecaman Perhatian (ARM) dicadangkan untuk mengenali perhatian seseorang, yang meningkatkan ketepatan pengesanan dan pengalaman subjektif semasa HRI bukan lisan menggunakan tiga model pengesanan gabungan: pengesanan muka, pengesanan iris dan mata berkedip. . Model penjejakan muka telah dilatih menggunakan rangkaian saraf Memori Jangka Pendek Panjang (LSTM), yang berdasarkan pembelajaran mendalam. Manakala, pengesanan iris dan mata berkelip menggunakan model matematik. Model mata berkelip menggunakan titik mercu tanda muka rawak untuk mengira Nisbah Aspek Mata (EAR), yang jauh lebih dipercayai berbanding kaedah sebelumnya, yang boleh mengesan seseorang berkelip pada jarak yang lebih jauh walaupun orang itu tidak berkelip. Eksperimen yang dijalankan untuk pengesanan muka dan iris dapat mengesan arah sehingga 2 meter. Sementara itu, model berkelip mata yang diuji memberikan ketepatan 83.33% sehingga 2 meter. Ketepatan perhatian keseluruhan ARM adalah sehingga 85.7%. Eksperimen menunjukkan bahawa robot perkhidmatan dapat memahami isyarat yang diprogramkan dan seterusnya melaksanakan tugas tertentu, seperti mendekati orang yang berminat.

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“…Zhou et al, 2020). This method is more suitable for distributed multirobot systems, and every robot is strongly autonomous (Hacene & Mendil, 2021). The artificial potential field method is a formation control strategy that uses a potential field to guide the agents to their desired positions (Lin et al, 2022;Sang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Minimalistic and Decentralised Approach to Formation Control for Crowded UUV Swarms Inspired by Fish Schooling

Deng²,

Shao

et al. 2023

Preprint

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In this paper, we propose a novel approach to formation control for a swarm of unmanned underwater vehicles (UUVs) inspired by the collective behaviour of fish schools. The approach addresses the challenges of centralised control, inflexibility, and high computational complexity in existing formation control methods. The proposed strategy for an individual agent consists of three parts: (1) coordinated strategy of collective motion without a predefined target, in which each individual agent adjusts its motion by rotating a fixed angle to the left or right based on the observed motion direction of its neighbours; (2) target guidance policy that the motion direction of each agent is guided by the target area; and (3) dispersion control strategy of avoiding overcrowding near the target area, in which each individual determines its own target position based on the relative positions of its neighbours and the target area. The simulation experiments demonstrate that using the proposed method, a group of individuals can perform an area coverage search in a flexible collective motion manner by simply making a left or right movement based on their own local perception. Furthermore, we validated the effectiveness of the proposed method using a UUV swarm consisting of 9 UUVs in a UUV simulator. The proposed method is flexible, decentralised, and has low computational complexity. Therefore, it can be applied to the formation control of large swarms of UUVs with limited perception and decision-making abilities.

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Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered Dynamic Environments with Dynamic Target Tracking

Cited by 21 publications

References 41 publications

Formation control of multiple autonomous underwater vehicles: a review

Formation control of multiple autonomous underwater vehicles: a review

Non-Verbal Human-Robot Interaction Using Neural Network for The Application of Service Robot

A Minimalistic and Decentralised Approach to Formation Control for Crowded UUV Swarms Inspired by Fish Schooling

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