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Biomimetics
Special Issues
Artificial Intelligence for Autonomous Robots 2024
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Artificial Intelligence for Autonomous Robots 2024

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

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 6142

Special Issue Editor

Dr. Ming Xie

E-Mail Website
Guest Editor
School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
Interests: humanoid robotics (design, control, biped walking, mobile manipulation) and 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 2024” 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. 5. The results of the experiment.

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

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 (5 papers)

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Research

19 pages, 3931 KiB  
Article
Reinforcement Learning with Task Decomposition and Task-Specific Reward System for Automation of High-Level Tasks
by Gunam Kwon, Byeongjun Kim and Nam Kyu Kwon
Biomimetics 2024, 9(4), 196; https://doi.org/10.3390/biomimetics9040196 - 26 Mar 2024
Viewed by 656
Abstract
This paper introduces a reinforcement learning method that leverages task decomposition and a task-specific reward system to address complex high-level tasks, such as door opening, block stacking, and nut assembly. These tasks are decomposed into various subtasks, with the grasping and putting tasks [...] Read more.
This paper introduces a reinforcement learning method that leverages task decomposition and a task-specific reward system to address complex high-level tasks, such as door opening, block stacking, and nut assembly. These tasks are decomposed into various subtasks, with the grasping and putting tasks executed through single joint and gripper actions, while other tasks are trained using the SAC algorithm alongside the task-specific reward system. The task-specific reward system aims to increase the learning speed, enhance the success rate, and enable more efficient task execution. The experimental results demonstrate the efficacy of the proposed method, achieving success rates of 99.9% for door opening, 95.25% for block stacking, 80.8% for square-nut assembly, and 90.9% for round-nut assembly. Overall, this method presents a promising solution to address the challenges associated with complex tasks, offering improvements over the traditional end-to-end approach. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2024)
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11 pages, 5093 KiB  
Article
Autonomous Robotic System to Prune Sweet Pepper Leaves Using Semantic Segmentation with Deep Learning and Articulated Manipulator
by Truong Thi Huong Giang and Young-Jae Ryoo
Biomimetics 2024, 9(3), 161; https://doi.org/10.3390/biomimetics9030161 - 05 Mar 2024
Viewed by 1032
Abstract
This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the [...] Read more.
This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the articulated manipulator. A semantic segmentation neural network is employed to recognize the different parts of the sweet pepper plant, which is then used to create 3D point clouds for detecting the pruning position and the manipulator pose. Eventually, a manipulator robot is controlled to prune the crop part. This article provides a detailed description of the three tasks involved in building the sweet pepper pruning system and how to integrate them. In the experiments, we used a robot arm to manipulate the pruning leaf actions within a certain height range and a depth camera to obtain 3D point clouds. The control program was developed in different modules using various programming languages running on the ROS (Robot Operating System). Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2024)
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18 pages, 5419 KiB  
Article
Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning
by Yajun Li, Qingchun Feng, Yifan Zhang, Chuanlang Peng and Chunjiang Zhao
Biomimetics 2024, 9(2), 105; https://doi.org/10.3390/biomimetics9020105 - 10 Feb 2024
Viewed by 1307
Abstract
Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm [...] Read more.
Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets. The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2024)
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16 pages, 4477 KiB  
Article
Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay
by Minjae Park, Chaneun Park and Nam Kyu Kwon
Biomimetics 2024, 9(1), 51; https://doi.org/10.3390/biomimetics9010051 - 13 Jan 2024
Viewed by 1316
Abstract
In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to [...] Read more.
In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2024)
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18 pages, 8289 KiB  
Article
Underactuated Humanoid Peeling Approach for Pickled Mustard Tuber Based on Metamorphic Constraints
by Haochuan Wan, Lei Chen, Jiayu Xiao, Nana Chen, Hankun Yin and Lin Zhang
Biomimetics 2023, 8(8), 566; https://doi.org/10.3390/biomimetics8080566 - 24 Nov 2023
Viewed by 1105
Abstract
Pickled mustard tuber (PMT), also known as Brassica juncea var. tumida, is a conical tuberous vegetable with a scaly upper part and a coarse fiber skin covering the lower part. Due to its highly distorted and complex heterogeneous fiber network structure, traditional [...] Read more.
Pickled mustard tuber (PMT), also known as Brassica juncea var. tumida, is a conical tuberous vegetable with a scaly upper part and a coarse fiber skin covering the lower part. Due to its highly distorted and complex heterogeneous fiber network structure, traditional manual labor is still used for peeling and removing fibers from pickled mustard tuber, as there is currently no effective, fully automated method or equipment available. In this study, we designed an underactuated humanoid pickled mustard tuber peeling robot based on variable configuration constraints that emulate the human “insert-clamp-tear” process via probabilistic statistical design. Based on actual pickled mustard tuber morphological cluster analysis and statistical features, we constructed three different types of pickled mustard tuber peeling tool spectral profiles and analyzed the modular mechanical properties of three different tool configurations to optimize the variable configuration constraint effect and improve the robot’s end effector trajectory. Finally, an ADAMS virtual prototype model of the pickled mustard tuber peeling robot was established, and simulation analysis of the “insert-clamp-tear” process was performed based on the three pickled mustard tuber statistical classification selection. The results showed that the pickled mustard tuber peeling robot had a meat loss rate of no more than 15% for each corresponding category of pickled mustard tuber, a theoretical peeling rate of up to 15 pieces per minute, and an average residual rate of only about 2% for old fibers. Based on reasonable meat loss, the efficiency of peeling was greatly improved, which laid the theoretical foundation for fully automated pickled mustard tuber peeling. Full article
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2024)
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