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Biomimetics
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Artificial Intelligence for Autonomous Robots: 3rd Edition
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Artificial Intelligence for Autonomous Robots: 3rd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Locomotion and Bioinspired Robotics".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 1860

Special Issue Editor

Dr. Ming Xie

E-Mail Website
Guest Editor
School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore City, Singapore
Interests: computer science and engineering; info-communication technology; interactive digital media machines & systems; robotics and intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are living in a world of signals. Our mental ability to transform signals into knowledge enables us to develop autonomy and adapt to a dynamically changing environment. Robots also live in this world of signals. Hence, it is our research goal to discover or develop physical principles that can enable the transformation from sensory signals to knowledge, from one type of knowledge into another type of knowledge, and from knowledge to control signals.

For this Special Issue, “Artificial Intelligence for Autonomous Robots: 3rd Edition”, we welcome original research works addressing the above-mentioned transformation in the context of various application scenarios, such as autonomous robots in industry, agriculture, land transportation, maritime transportation, aerial transportation, medical interventions, care of elderly people, care homes, education, entertainment, defense, and other services.

Each submitted paper should clearly state the following: 1. the problem under investigation, 2. existing works, 3. the superior solutions proposed, 4. details of the proposed solutions, and 5. the experimental results.

I am looking forward to receiving your submissions of wonderful research works that will allow our understanding of artificial intelligence and autonomous robots to reach a new level.

Dr. Ming Xie
Guest Editor

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. Biomimetics is an international peer-reviewed open access monthly 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 2200 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

  • large knowledge model (LKM) for autonomous robots
  • knowledge perception by autonomous robots
  • knowledge reasoning and inference in autonomous robots
  • knowledge-based planning and control in autonomous robots
  • knowledge learning by autonomous robots
  • sense-making by autonomous robots
  • sensor-guided navigation
  • sensor-guided grasping
  • sensor-guided manipulation
  • sensor-guided locomotion
  • sensor-guided driving
  • human–robot interaction at a cognitive level
  • conversational dialogue between robots and human beings
  • mechatronic design of autonomous robots
  • applications of autonomous robots

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Published Papers (2 papers)

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16 pages, 9423 KiB  
Article
EchoPT: A Pretrained Transformer Architecture That Predicts 2D In-Air Sonar Images for Mobile Robotics
by Jan Steckel, Wouter Jansen and Nico Huebel
Biomimetics 2024, 9(11), 695; https://doi.org/10.3390/biomimetics9110695 - 13 Nov 2024
Viewed by 636
Abstract
The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of [...] Read more.
The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots: 3rd Edition)
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17 pages, 1623 KiB  
Article
Autonomous Robot Task Execution in Flexible Manufacturing: Integrating PDDL and Behavior Trees in ARIAC 2023
by Ruikai Liu, Guangxi Wan, Maowei Jiang, Haojie Chen and Peng Zeng
Biomimetics 2024, 9(10), 612; https://doi.org/10.3390/biomimetics9100612 - 10 Oct 2024
Viewed by 983
Abstract
The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped parts, faulty grippers, robot [...] Read more.
The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped parts, faulty grippers, robot malfunctions, sensor blackouts, high-priority orders, insufficient parts, and human safety. Given the unpredictability of these scenarios, it is impractical to develop a specific strategy for each possible situation. To address these issues, this paper presents a hierarchical framework for autonomous robotic task generation and execution in dynamic scenarios. The framework is divided into a task level and an execution level. Initially, an immediate task management strategy is adopted at the task level, which reasonably decomposes dynamic tasks and allocates short-term tasks to the floor robot and ceiling robot. Later, at the execution level, each robot is designed with an agent architecture that combines PDDL planning with the quick response of behavior trees. Finally, the effectiveness and practicality of the proposed framework were thoroughly validated in ARIAC 2023. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots: 3rd Edition)
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