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Electronics
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AI in Mobile Robotics
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AI in Mobile Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 8187

Special Issue Editors

Prof. Dr. Tae-Yong Kuc

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-dong Jangan-gu, Suwon 440-746, Gyeonggi-do, Republic of Korea
Interests: autonomous navigation of mobile robots; VSLAM; 3D SLAM; semantic SLAM
Special Issues, Collections and Topics in MDPI journals
Dr. Ho Seok Ahn

E-Mail Website
Guest Editor
Centre for Automation and Robotic Engineering Science, Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand
Interests: agricultural robots; IPT; smart farm; human-robot interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Inkyu Sa

E-Mail Website
Guest Editor
Tencent XR Vision Lab, Canberra, ACT 2601, Australia
Interests: UAV; robot vision; state estimation; deep learning in agriculture (horticulture); reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Historically, AI (artificial intelligence) and robotics are related fields that have been researched for some time, but their principles are completely different. Robotics is aimed at the creation of robots to automatically perform tasks without further intervention. The reason is that robots, differently from humans, can perform a given task without fatigue or feeling bored even when the tasks are routine and repetitive. On the other hand, AI is concerned with how to model and emulate human intelligence to solve a problem and make a system or process understand its current situation in a way that leads to a better decision being made than without its involvement. This means that AI can, over time, provide a passage for evolution of a system by gradually learning from its previous task.

While these two disciplines are usually developed independently of each other, they can be combined together in such a way as “a robotic system with an AI algorithm” and, vice versa, “an AI with a robotic realization”. Nowadays, AI and robotics are treated as two closely related research fields in that, generally speaking, robotics is developed with AI to advance its intellectual capability for better performance in a sense–plan–act scenario based on learning and decision making. The role of AI in robotics is growing with the expansion of high-performance robotic applications in personal robotic service areas and professional service areas. Examples are home service robots for households, medical robots and healthcare robots, advanced industry 4.0 manufacturing robots for smart factory, last mile delivery robots and logistics robotic vehicles, cultivation robots and harvesting robots in agriculture, excavator robots and crane robots for digital transformation of construction technology, reconnaissance robots and combat robots for defense service, and exploration robots and repair robots in aerospace service.

The Special Issue, “AI in Mobile Robotics”, focuses on the recent developments and applications of AI techniques, such as deep learning neural network, to mobile robotics. The specific topics of interest can include but are not limited to:

-intelligent control

-localization

-map building

-object and place recognition

-scene understanding

-task planning and dynamic task allocation

-human-like cognitive skill learning

-human-friendly UI and UX for HRI (human–robot interaction)

-high-level SLAM (simultaneous localization and mapping) such as 3D SLAM, visual SLAM, and semantic SLAM

-Reinforcement Learning in Robotics

-other relevant topics based on AI.

To share the recent technical achievements and enlarge technical insights, we invite AI and robotics researchers to contribute their original works and review articles to our Special Issue.

Prof. Tae-Yong Kuc
Dr. Ho Seok Ahn
Dr. Inkyu Sa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent control
  • Localization
  • Map building
  • Object recognition
  • Place recognition
  • Scene understanding
  • Task planning
  • Dynamic task allocation
  • Skill learning
  • UI
  • UX
  • HRI
  • SLAM
  • 3D SLAM
  • Visual SLAM
  • Semantic SLAM

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Published Papers (4 papers)

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18 pages, 1397 KiB  
Article
Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning
by Jianjun Ni, Yu Gu, Guangyi Tang, Chunyan Ke and Yang Gu
Electronics 2024, 13(5), 944; https://doi.org/10.3390/electronics13050944 - 29 Feb 2024
Cited by 3 | Viewed by 1703
Abstract
With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and [...] Read more.
With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality. Full article
(This article belongs to the Special Issue AI in Mobile Robotics)
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19 pages, 7702 KiB  
Article
Application of Improved Butterfly Optimization Algorithm in Mobile Robot Path Planning
by Rongjie Zhai, Ping Xiao, Da Shu, Yongjiu Sun and Min Jiang
Electronics 2023, 12(16), 3424; https://doi.org/10.3390/electronics12163424 - 13 Aug 2023
Cited by 3 | Viewed by 1434
Abstract
An improved butterfly optimization algorithm (IBOA) is proposed to overcome the disadvantages, including slow convergence, generation of local optimum solutions, and deadlock phenomenon, of the optimization algorithm in the path planning of mobile robots. A path-planning grid model is established based on an [...] Read more.
An improved butterfly optimization algorithm (IBOA) is proposed to overcome the disadvantages, including slow convergence, generation of local optimum solutions, and deadlock phenomenon, of the optimization algorithm in the path planning of mobile robots. A path-planning grid model is established based on an improved obstacle model. First, the population diversity is improved by introducing kent mapping during population position renewal in the normal butterfly optimization algorithm (BOA) to enhance the global search ability of the butterfly population. Second, an adaptive weight coefficient is introduced in the renewal process of each generation to increase the convergence speed and accuracy. An opposition-based learning strategy based on convex lens imaging is introduced to help the butterfly population jump out of the local optimum. Finally, a mutation strategy is introduced to solve the path planning problem. On this basis, two path simplification strategies are proposed to make up for the shortcomings of planning paths in grid maps. The shortest path lengths solved by IBOA, BOA, and GA in the 20 × 20 map are 30.97, 31.799, and 31.799, respectively. The numbers of iterations for the shortest paths searched by IBOA, BOA, and GA are 14, 24, and 38 in that order. The shortest path lengths solved by IBOA, BOA and GA in the 40 × 40 map are 63.84, 65.60, and 65.84, respectively. The number of iterations for the shortest paths searched by IBOA, BOA and GA are 32, 40, and 46, respectively. Simulation results show that IBOA has a strong ability to solve robot path planning problems and that the proposed path simplification strategy can effectively reduce the length of the optimal path in the grid map to solve the path planning problem of mobile robots. The shortest paths solved by IBOA in 20 × 20 and 40 × 40 maps are simplified to lengths of 30.2914 and 61.03, respectively. Full article
(This article belongs to the Special Issue AI in Mobile Robotics)
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28 pages, 5774 KiB  
Article
Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems
by Sanghyeon Bae, Sunghyeon Joo, Junhyeon Choi, Jungwon Pyo, Hyunjin Park and Taeyong Kuc
Electronics 2023, 12(9), 2131; https://doi.org/10.3390/electronics12092131 - 6 May 2023
Cited by 1 | Viewed by 1941
Abstract
Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of [...] Read more.
Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments. Full article
(This article belongs to the Special Issue AI in Mobile Robotics)
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19 pages, 9521 KiB  
Article
A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments
by Sunghyeon Joo, Sanghyeon Bae, Junhyeon Choi, Hyunjin Park, Sangwook Lee, Sujeong You, Taeyoung Uhm, Jiyoun Moon and Taeyong Kuc
Electronics 2022, 11(15), 2420; https://doi.org/10.3390/electronics11152420 - 3 Aug 2022
Cited by 3 | Viewed by 2279
Abstract
Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against [...] Read more.
Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various situations. In this paper, we propose a semantic navigation framework based on a Triplet Ontological Semantic Model (TOSM) to manage various conditions affecting the execution of tasks. The framework allows robots with different kinematics to perform tasks in indoor and outdoor environments. We define the TOSM-based semantic knowledge and generate a semantic map for the domains. The robots execute tasks according to their characteristics by converting inferred knowledge to Planning Domain Definition Language (PDDL). Additionally, to make the framework sustainable, we determine a policy of maintaining the map and re-planning when in unexpected situations. The various experiments on four different kinds of robots and four scenarios validate the scalability and reliability of the proposed framework. Full article
(This article belongs to the Special Issue AI in Mobile Robotics)
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