Artificial Intelligence for Autonomous Robots 2023

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 6370

Special Issue Editor

School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore City, Singapore
Interests: humanoid robotics (design, control, biped walking, mobile manipulation); autonomous vehicles (perception, planning, and control)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are living inside an ocean of signals. Our mental capabilities of transforming signals into knowledge enable us to gain autonomy and adaptation in a dynamically changing environment. Similarly, robots are also living inside the same ocean of signals. Hence, it is our research goal to discover or invent the physical principles behind the transformations from sensory signals to knowledge, from one kind of knowledge into another kind of knowledge, and from knowledge back to control signals.

This Special Issue on “Artificial Intelligence for Autonomous Robots 2023” welcomes original research works which address the above-mentioned transformations in the contexts of various application scenarios, such as autonomous robots for industry, agriculture, land transportation, maritime transportation, transportation in air, medical intervention, elderly care, home care, education, entertainment, general service, defense, etc.

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

I look forward to receiving the submissions of your wonderful research works which will advance artificial intelligence and autonomous  robots to a new height.

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

  • cognitive vision for autonomous robots
  • cognitive speech for autonomous robots
  • intelligent sensor network
  • brain-like computing systems
  • AI-enabled operating systems for robots
  • AI-enabled grasping by robots
  • AI-enabled manipulation by robots
  • AI-enabled locomotion by robots
  • AI-enabled collaborative works by robots
  • AI-enabled human–robot interaction
  • AI-enabled conversational dialogue between human beings and robots
  • autonomous industrial robots with self-intelligence
  • autonomous agricultural robots with self-intelligence
  • autonomous mobile robots with self-intelligence
  • autonomous service robots with self-intelligence

Published Papers (4 papers)

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Research

27 pages, 8113 KiB  
Article
A Robust Semi-Direct 3D SLAM for Mobile Robot Based on Dense Optical Flow in Dynamic Scenes
by Bo Hu and Jingwen Luo
Biomimetics 2023, 8(4), 371; https://doi.org/10.3390/biomimetics8040371 - 16 Aug 2023
Viewed by 989
Abstract
Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust [...] Read more.
Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust semi-direct 3D simultaneous localization and mapping (SLAM) algorithm for mobile robots based on dense optical flow. First, a preliminary estimation of the robot’s pose is conducted using the sparse direct method and the homography matrix is utilized to compensate for the current frame image to reduce the image deformation caused by rotation during the robot’s motion. Then, by calculating the dense optical flow field of two adjacent frames and segmenting the dynamic region in the scene based on the dynamic threshold, the local map points projected within the dynamic regions are eliminated. On this basis, the robot’s pose is optimized by minimizing the reprojection error. Moreover, a high-performance keyframe selection strategy is developed, and keyframes are inserted when the robot’s pose is successfully tracked. Meanwhile, feature points are extracted and matched to the keyframes for subsequent optimization and mapping. Considering that the direct method is subject to tracking failure in practical application scenarios, the feature points and map points of keyframes are employed in robot relocation. Finally, all keyframes and map points are used as optimization variables for global bundle adjustment (BA) optimization, so as to construct a globally consistent 3D dense octree map. A series of simulations and experiments demonstrate the superior performance of the proposed algorithm. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2023)
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19 pages, 2339 KiB  
Article
Facial Anthropomorphic Trustworthiness Scale for Social Robots: A Hybrid Approach
by Yao Song, Ameersing Luximon and Yan Luximon
Biomimetics 2023, 8(4), 335; https://doi.org/10.3390/biomimetics8040335 - 29 Jul 2023
Cited by 2 | Viewed by 1153
Abstract
Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid [...] Read more.
Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid deep convolution approach was employed in this study, involving a crowdsourcing platform for data collection and deep convolution and factor analysis for data processing. The goal was to develop a scale, called Facial Anthropomorphic Trustworthiness towards Social Robots (FATSR-17), to measure the trustworthiness of a robot’s facial appearance. The final measurement scale comprised four dimensions, “ethics concern”, “capability”, “positive affect”, and “anthropomorphism”, consisting of 17 items. An iterative examination and a refinement process were conducted to ensure the scale’s reliability and validity. The study contributes to the field of robot design by providing designers with a structured toolkit to create robots that appear trustworthy to users. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2023)
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20 pages, 13909 KiB  
Article
A New Principle toward Robust Matching in Human-like Stereovision
by Ming Xie, Tingfeng Lai and Yuhui Fang
Biomimetics 2023, 8(3), 285; https://doi.org/10.3390/biomimetics8030285 - 02 Jul 2023
Cited by 2 | Viewed by 1130
Abstract
Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge [...] Read more.
Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from visual signals. Until today, one of the biggest challenges faced by intelligent robots or machines is the matching in stereovision. In this paper, we present the details of a new principle toward achieving a robust matching solution which leverages on the use and integration of top-down image sampling strategy, hybrid feature extraction, and Restricted Coulomb Energy (RCE) neural network for incremental learning (i.e., cognition) as well as robust match-maker (i.e., recognition). A preliminary version of the proposed solution has been implemented and tested with data from Maritime RobotX Challenge. The contribution of this paper is to attract more research interest and effort toward this new direction which may eventually lead to the development of robust solutions expected by future stereovision systems in intelligent robots, vehicles, and machines. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2023)
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17 pages, 4011 KiB  
Article
The Task Decomposition and Dedicated Reward-System-Based Reinforcement Learning Algorithm for Pick-and-Place
by Byeongjun Kim, Gunam Kwon, Chaneun Park and Nam Kyu Kwon
Biomimetics 2023, 8(2), 240; https://doi.org/10.3390/biomimetics8020240 - 06 Jun 2023
Cited by 3 | Viewed by 1752
Abstract
This paper proposes a task decomposition and dedicated reward-system-based reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level tasks of robot manipulators. The proposed method decomposes the Pick-and-Place task into three subtasks: two reaching tasks and one grasping task. [...] Read more.
This paper proposes a task decomposition and dedicated reward-system-based reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level tasks of robot manipulators. The proposed method decomposes the Pick-and-Place task into three subtasks: two reaching tasks and one grasping task. One of the two reaching tasks is approaching the object, and the other is reaching the place position. These two reaching tasks are carried out using each optimal policy of the agents which are trained using Soft Actor-Critic (SAC). Different from the two reaching tasks, the grasping is implemented via simple logic which is easily designable but may result in improper gripping. To assist the grasping task properly, a dedicated reward system for approaching the object is designed through using individual axis-based weights. To verify the validity of the proposed method, wecarry out various experiments in the MuJoCo physics engine with the Robosuite framework. According to the simulation results of four trials, the robot manipulator picked up and released the object in the goal position with an average success rate of 93.2%. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2023)
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