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Electronics
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Application of Artificial Intelligence in Robotics
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Application of Artificial Intelligence in Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 7105

Special Issue Editors

Dr. Guanglei Wu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: robotic design and control; robotic technology and industrial application
Special Issues, Collections and Topics in MDPI journals
Dr. Stéphane Caro

E-Mail Website
Guest Editor
National Centre for Scientific Research (CNRS) and the Laboratory of Digital Sciences of Nantes (LS2N), UMR CNRS, 6004 Nantes, France
Interests: design, modeling, and control of cable-driven parallel robots and reconfigurable parallel robots
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Shen

E-Mail Website
Guest Editor
Department of Electronic Systems, Aalborg University, DK-9220 Aalborg, Denmark
Interests: human–machine interaction; machine learning; communication systems; biomedical sensors
Dr. Ricardo Soto

E-Mail Website
Guest Editor
Informatics Engineering School, Pontifical Catholic University of Valparaíso, Valparaíso, Chile
Interests: metaheuristics; autonomous search; machine learning; constraint programming

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) holds great potential for robotics, and enables a wide range of benefits in diverse sectors such as manufacturing and healthcare. The robots are becoming ‘smarter’ and more efficient with the development and help of computer science, while most of the existing robotics systems were created with AI-free consideration because of the past limitations of artificial intelligence. Although AI is already making its mark on robotics, it is occurring at a much slower pace and in a far narrower field of application than is commonly assumed.

As the technology continues to advance in leaps and bounds each year, AI has been playing a major and increasingly important role not only in increasing the comfort of humans, but also increasing industrial productivity (including both quantitative and qualitative production and cost-efficiency). This collection aims to present the recent advances in the integration of AI and robotics, particularly the application of AI in robotics, which can may help robotics manufacturers to feel increasingly confident in pushing the limits in what can be achieved by marrying the two disciplines. Application examples of AI in manufacturing, aerospace, healthcare and agriculture would help to build confidence that the future is bright for robotics and artificial intelligence.

The topics of interest include but are not limited to the following:

  • AI-based learning and control of robot systems;
  • Safety standards and regulation for AI in robotics;
  • The impact of AI in robotics on work and jobs;
  • Human–robot interactions, human–robot interfaces, and brain–computer interfaces;
  • Wearable technologies, haptic technologies, and hardware-in-the-loop;
  • AI and mobile robots, surgical robots, agriculture robots, service robots, and assistive robots;
  • Open robotics systems (ORSs), virtual reality (VR), augmented reality (AR), extended reality (ER), and robot development platforms;
  • Learning and transferring human skills to robotic manipulators;
  • Robot system intelligentization technologies.

Dr. Guanglei Wu
Dr. Stéphane Caro
Dr. Ming Shen
Dr. Ricardo Soto
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

  • machine learning
  • artificial intelligence
  • intelligent control
  • human–robot interaction
  • virtual reality
  • robotic system
  • robotic technology and application

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Published Papers (6 papers)

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Research

33 pages, 10845 KiB  
Article
ORPP—An Ontology for Skill-Based Robotic Process Planning in Agile Manufacturing
by Congyu Zhang Sprenger, Juan Antonio Corrales Ramón and Norman Urs Baier
Electronics 2024, 13(18), 3666; https://doi.org/10.3390/electronics13183666 - 14 Sep 2024
Viewed by 461
Abstract
Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. [...] Read more.
Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Robotics)
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21 pages, 4274 KiB  
Article
An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware
by Nicola Russo, Thomas Madsen and Konstantin Nikolic
Electronics 2024, 13(17), 3448; https://doi.org/10.3390/electronics13173448 - 30 Aug 2024
Viewed by 442
Abstract
Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. [...] Read more.
Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. Regarding the hardware part, we can find different sensory systems, such as Dynamic Vision Sensors, touch sensors, and actuators, which are linked together through specific interface boards. To run real-time SNN models, specialised hardware such as SpiNNaker, Loihi, and TrueNorth have been implemented. However, neuromorphic computing is still in development, and neuromorphic platforms are still not easily accessible to researchers. In addition, for Neuromorphic Robotics, we often need specially designed and fabricated PCBs for communication with peripheral components and sensors. Therefore, we developed an all-in-one neuromorphic system that emulates neuromorphic computing by running a Virtual Machine on a conventional low-power CPU. The Virtual Machine includes Python and Brian2 simulation packages, which allow the running of SNNs, emulating neuromorphic hardware. An additional, significant advantage of using conventional hardware such as Raspberry Pi in comparison to purpose-built neuromorphic hardware is that we can utilise the built-in physical input–output (GPIO) and USB ports to directly communicate with sensors. As a proof of concept platform, a robotic goalkeeper has been implemented, using a Raspberry Pi 5 board and SNN model in Brian2. All the sensors, namely DVS128, with an infrared module as the touch sensor and Futaba S9257 as the actuator, were linked to a Raspberry Pi 5 board. We show that it is possible to simulate SNNs on a conventional low-power CPU running real-time tasks for low-latency and low-power robotic applications. Furthermore, the system excels in the goalkeeper task, achieving an overall accuracy of 84% across various environmental conditions while maintaining a maximum power consumption of 20 W. Additionally, it reaches 88% accuracy in the online controlled setup and 80% in the offline setup, marking an improvement over previous results. This work demonstrates that the combination of a conventional low-power CPU running a Virtual Machine with only selected software is a viable competitor to neuromorphic computing hardware for robotic applications. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Robotics)
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16 pages, 10025 KiB  
Article
Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations
by Guanglei Wu, Bin Niu and Qiancheng Li
Electronics 2023, 12(24), 4995; https://doi.org/10.3390/electronics12244995 - 13 Dec 2023
Cited by 1 | Viewed by 1101
Abstract
Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for [...] Read more.
Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Robotics)
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20 pages, 570 KiB  
Article
Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search
by Ziyun Chen, Dengsheng Zhang, Chengxiang Wang and Qixin Sha
Electronics 2023, 12(22), 4575; https://doi.org/10.3390/electronics12224575 - 8 Nov 2023
Viewed by 1309
Abstract
For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the [...] Read more.
For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Robotics)
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16 pages, 17137 KiB  
Article
Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption
by Xiru Wu, Shuqiao Huang and Guoming Huang
Electronics 2023, 12(18), 3840; https://doi.org/10.3390/electronics12183840 - 11 Sep 2023
Cited by 2 | Viewed by 1447
Abstract
Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off [...] Read more.
Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Robotics)
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17 pages, 3675 KiB  
Article
Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm
by Xudong Li, Bin Tian, Shuaidong Hou, Xinxin Li, Yang Li, Chong Liu and Jingmin Li
Electronics 2023, 12(15), 3289; https://doi.org/10.3390/electronics12153289 - 31 Jul 2023
Cited by 5 | Viewed by 1169
Abstract
To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of [...] Read more.
To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Robotics)
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