Current trend in control of artificial intelligence for health robotic manipulator
Article Sidebar
Main Article Content
Abstract
The increasing utilization of artificial intelligence and robots in various services in healthcare makes robots as preferred intelligent agent model. Robotic evolution produces the optimal robotic innovation in the robotic system or its subsystems, morphology, kinematics, and control. An intelligent algorithm is programmed into the control of the robotic manipulator. This paper aims to identify the control of artificial intelligence and identify comparisons of artificial intelligence algorithms control for healthcare robotic manipulators. This study uses a systematic literature review using the Preferred Reporting Items for Systematic Review (PRISMA). The potential for further articles is explored related to the theme of the research carried out. The conclusion obtained many studies have been carried out to optimize the work and tasks of the robotic arm manipulator, specifically developing various types of manipulator control (algorithms) combined with neural networks to get the right and appropriate algorithm.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
A. Miroshnichenko, ‘AI to Bypass Creativity. Will Robots Replace Journalists? (The Answer Is “Yes”)’, Information, vol. 9, no. 7, Art. no. 7, 2018, doi: 10.3390/info9070183.
M. A. Peters, ‘Technological unemployment: Educating for the fourth industrial revolution’, Educational Philosophy and Theory, vol. 49, no. 1, pp. 1–6, 2017, doi: 10.1080/00131857.2016.1177412.
W. Wang and K. Siau, ‘Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity: A Review and Research Agenda’, JDM, vol. 30, no. 1, pp. 61–79, 2019, doi: 10.4018/JDM.2019010104.
A. D. Kaplan, T. T. Kessler, J. C. Brill, and P. A. Hancock, ‘Trust in Artificial Intelligence: Meta-Analytic Findings’, Hum Factors, pp. 1–25, 2021, doi: 10.1177/00187208211013988.
J. Romero et al., ‘A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle’, Entropy (Basel), vol. 22, no. 12, p. E1362, 2020, doi: 10.3390/e22121362.
A. M. Diamond, ‘Robots and Computers Enhance Us More Than They Replace Us’, The American Economist, vol. 65, no. 1, pp. 4–10, 2020, doi: 10.1177/0569434518792674.
A. Chella, L. Iocchi, I. Macaluso, and D. Nardi, ‘Artificial Intelligence and Robotics.’, Intelligenza Artificiale, vol. 3, pp. 87–93, 2006.
J. Cai, J. Deng, W. Zhang, and W. Zhao, ‘Modeling Method of Autonomous Robot Manipulator Based on D-H Algorithm’, Mobile Information Systems, vol. 2021, pp. 1–10, 2021, doi: 10.1155/2021/4448648.
A. H. While, S. Marvin, and M. Kovacic, ‘Urban robotic experimentation: San Francisco, Tokyo and Dubai’, Urban Studies, vol. 58, no. 4, pp. 769–786, 2021, doi: 10.1177/0042098020917790.
A. Kharidege, P. Ri, and Y. Zhang, ‘Control Algorithm Trajectory Planning for Dual Cooperative Manipulators with Experimental Verification’, MATEC Web of Conferences, vol. 75, p. 05005, 2016, doi: 10.1051/matecconf/20167505005.
C. Smith et al., ‘Dual arm manipulation—A survey’, Robotics and Autonomous Systems, vol. 60, no. 10, pp. 1340–1353, 2012, doi: 10.1016/j.robot.2012.07.005.
F. Caccavale, P. Chiacchio, A. Marino, and L. Villani, ‘Six-DOF Impedance Control of Dual-Arm Cooperative Manipulators’, IEEE/ASME Transactions on Mechatronics, vol. 13, no. 5, pp. 576–586, 2008, doi: 10.1109/TMECH.2008.2002816.
I. Dulęba and I. Karcz-Dulęba, ‘A comparison of methods solving repeatable inverse kinematics for robot manipulators’, Archives of Control Sciences, vol. 28, no. 1, pp. 5–18, 2018, doi: 10.24425/119074.
A. Awelewa, K. Mbanisi, S. Majekodunmi, I. Odigwe, A. Agbetuyi, and I. Samuel, ‘Development of a Prototype Robot Manipulator for Industrial Pick-and-Place Operations’, International Journal of Mechanical & Mechatronics Engineering, vol. 13, no. 5, pp. 20–28, 2013.
S. Patel and T. Sobh, ‘Task based synthesis of serial manipulators’, Journal of Advanced Research, vol. 6, no. 3, pp. 479–492, 2015, doi: 10.1016/j.jare.2014.12.006.
S. Ha, S. Coros, A. Alspach, J. M. Bern, J. Kim, and K. Yamane, ‘Computational Design of Robotic Devices From High-Level Motion Specifications’, IEEE Transactions on Robotics, vol. 34, no. 5, pp. 1240–1251, 2018, doi: 10.1109/TRO.2018.2830419.
R. Pastor, Z. Bobovský, D. Huczala, and S. Grushko, ‘Genetic Optimization of a Manipulator: Comparison between Straight, Rounded, and Curved Mechanism Links’, Applied Sciences, vol. 11, no. 6, Art. no. 6, 2021, doi: 10.3390/app11062471.
J. Whitman and H. Choset, ‘Task-Specific Manipulator Design and Trajectory Synthesis’, IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 301–308, 2019, doi: 10.1109/LRA.2018.2890206.
A. Perry and N. Hammond, ‘Systematic Reviews: The Experiences of a PhD Student’, Psychology Learning & Teaching, vol. 2, no. 1, pp. 32–35, 2002, doi: 10.2304/plat.2002.2.1.32.
S. Cordeiro Vasconcelos, I. Frazão, E. Monteiro, M. Lima, J. Albuquerque, and V. Ramos, ‘Nursing Interventions for Drug Users: Qualitative Meta-Synthesis’, American Journal of Nursing Research, vol. 1, no. 1, pp. 24–27, 2013, doi: 10.12691/ajnr-1-1-4.
D. E. Bock, J. S. Wolter, and O. C. Ferrell, ‘Artificial intelligence: disrupting what we know about services’, Journal of Services Marketing, vol. 34, no. 3, pp. 317–334, 2020, doi: 10.1108/JSM-01-2019-0047.
R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, ‘Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions’, Sustainability, vol. 12, no. 2, Art. no. 2, 2020, doi: 10.3390/su12020492.
M. Raj and R. Seamans, ‘Primer on artificial intelligence and robotics’, J Org Design, vol. 8, no. 1, p. 11, 2019, doi: 10.1186/s41469-019-0050-0.
A. Azlan, H. Al-Assadi, and A. Hasan, ‘Positioning control of an under-actuated robot manipulator using artificial neural network inversion technique’, New Developments in Artificial Neural Networks Research, vol. 2011, pp. 207–220, 2011.
S. Mohsienuddin and H. H. Syed, ‘Artificial Intelligence in Information Technology’, SSRN Electronic Journal, vol. 7, no. 6, pp. 168–176, 2020.
V. Dignum, ‘Responsible Artificial Intelligence: Designing Ai for Human Values’, ICT Discoveries, no. 1, pp. 1–8, 2017.
S. H. Tang, C. K. Ang, M. K. A. B. M. Ariffin, and S. B. Mashohor, ‘Predicting the Motion of a Robot Manipulator with Unknown Trajectories Based on an Artificial Neural Network’, International Journal of Advanced Robotic Systems, vol. 11, no. 10, p. 176, 2014, doi: 10.5772/59278.
J. Liu et al., ‘Artificial Intelligence in the 21st Century’, IEEE Access, vol. 99, pp. 1–16, 2018, doi: 10.1109/ACCESS.2018.2819688.
H. Demir and F. Sarı, ‘The Effect of Artificial Intelligence and Industry 4.0 on Robotic Systems’, in Engineering on Energy Materials, Iksad Publications, 2020, pp. 51–72.
J. Arents and M. Greitans, ‘Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing’, Applied Sciences, vol. 12, no. 2, Art. no. 2, 2022, doi: 10.3390/app12020937.
N. Syam and A. Sharma, ‘Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice’, Industrial Marketing Management, vol. 69, pp. 135–146, 2018, doi: 10.1016/j.indmarman.2017.12.019.
T. Haidegger, P. Galambos, and I. Rudas, ‘Robotics 4.0 – Are we there yet?’, in Robotics 4.0 – Are we there yet?, Gödöllő, Hungary, 2019, pp. 000117–000124. doi: 10.1109/INES46365.2019.9109492.
G. Kovács, R. Benotsmane, and L. Dudás, ‘The Concept of Autonomous Systems in Industry 4.0’, Advanced Logistic Systems - Theory and Practice, vol. 12, no. 1, pp. 77–87, 2019, doi: 10.32971/als.2019.006.
G. Muscato, G. Nunnari, and L. Occhipinti, ‘Intelligent Control of a Robot Manipulator’, IFAC Proceedings Volumes, vol. 28, no. 10, pp. 733–738, 1995, doi: 10.1016/S1474-6670(17)51607-9.
M. Marsono, Y. Yoto, A. Suyetno, and R. Nurmalasari, ‘Design and Programming of 5 Axis Manipulator Robot with GrblGru Open Source Software on Preparing Vocational Students’ Robotic Skills’, Journal of Robotics and Control (JRC), vol. 2, no. 6, Art. no. 6, 2021, doi: 10.18196/jrc.26134.
J. Andreu-Perez, F. Deligianni, D. Ravì, and G.-Z. Yang, Artificial Intelligence and Robotics. UK-RAS Network, 2017.
A. P. Piotrowski, M. J. Napiorkowski, J. J. Napiorkowski, and P. M. Rowinski, ‘Swarm Intelligence and Evolutionary Algorithms: Performance versus speed’, Information Sciences, vol. 384, pp. 34–85, 2017, doi: 10.1016/j.ins.2016.12.028.
B. K. Rout and R. K. Mittal, ‘Optimal design of manipulator parameter using evolutionary optimization techniques’, Robotica, vol. 28, no. 3, pp. 381–395, 2010, doi: 10.1017/S0263574709005700.
S. Rubrecht, E. Singla, V. Padois, P. Bidaud, and M. de Broissia, ‘Evolutionary Design of a Robotic Manipulator for a Highly Constrained Environment’, in New Horizons in Evolutionary Robotics, Berlin, Heidelberg, 2011, vol. 341, pp. 109–121. doi: 10.1007/978-3-642-18272-3_8.
H. Bezine, N. Derbel, and A. M. Alimi, ‘Fuzzy control of robot manipulators:: some issues on design and rule base size reduction’, Engineering Applications of Artificial Intelligence, vol. 15, no. 5, pp. 401–416, 2002, doi: 10.1016/S0952-1976(02)00075-1.
H. M. Yudha, T. Dewi, P. Risma, and Y. Oktarina, ‘Arm Robot Manipulator Design and Control for Trajectory Tracking; a Review’, in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018, pp. 304–309. doi: 10.1109/EECSI.2018.8752950.
J. Iqbal, ‘Modern Control Laws for an Articulated Robotic Arm: Modeling and Simulation’, Engineering, Technology & Applied Science Research, vol. 9, no. 2, Art. no. 2, 2019, doi: 10.48084/etasr.2598.
M. Ullah, S. Ajwad, M. Ul Islam, U. Iqbal, and J. Iqbal, ‘Modeling and computed torque control of a 6 degree of freedom robotic arm’, 2014, pp. 133–138. doi: 10.1109/iCREATE.2014.6828353.
T. V. Zudilova and S. E. Ivanov, ‘Mathematical Modeling of the Robot Manipulator with Four Degrees of Freedom’, Global Journal of Pure and Applied Mathematics, vol. 12, no. 5, pp. 4419–4429, 2016.
Y. Ansari, M. Manti, E. Falotico, Y. Mollard, M. Cianchetti, and C. Laschi, ‘Towards the development of a soft manipulator as an assistive robot for personal care of elderly people’, International Journal of Advanced Robotic Systems, vol. 14, no. 2, p. 1729881416687132, 2017, doi: 10.1177/1729881416687132.
B. T. Phillips et al., ‘A Dexterous, Glove-Based Teleoperable Low-Power Soft Robotic Arm for Delicate Deep-Sea Biological Exploration’, Sci Rep, vol. 8, no. 1, p. 14779, 2018, doi: 10.1038/s41598-018-33138-y.
D. Rus and M. T. Tolley, ‘Design, fabrication and control of soft robots’, Nature, vol. 521, no. 7553, Art. no. 7553, 2015, doi: 10.1038/nature14543.
B. Kamrani, V. Berbyuk, D. W�ppling, X. Feng, and H. Andersson, ‘Optimal Usage of Robot Manipulators’, in Robot Manipulators Trends and Development, 2010. doi: 10.5772/9198.
Z. Song, J. Yi, D. Zhao, and X. Li, ‘A computed torque controller for uncertain robotic manipulator systems: Fuzzy approach’, Fuzzy Sets and Systems, vol. 154, no. 2, pp. 208–226, 2005, doi: 10.1016/j.fss.2005.03.007.
Z. Yang, J. Wu, J. Mei, J. Gao, and T. Huang, ‘Mechatronic Model Based Computed Torque Control of a Parallel Manipulator’, International Journal of Advanced Robotic Systems, vol. 5, no. 1, p. 14, 2008, doi: 10.5772/5650.
J. Alvarez, J. C. Arceo, C. Armenta, J. Lauber, and M. Bernal, ‘An Extension of Computed-Torque Control for Parallel Robots in Ankle Reeducation⁎⁎This work has been supported by the ECOS Nord SEP-CONACYT-ANUIES Project (Mexico 291309 / France M17M08). This research is sponsored by ELSAT 2020 of the Haut de France Region, the European Community, the Regional Delegation for Research and Technology, the French Ministry of Higher Education and Research, and the French National Center for Scientific Research.’, IFAC-PapersOnLine, vol. 52, no. 11, pp. 1–6, 2019, doi: 10.1016/j.ifacol.2019.09.109.
C. Pupaza, G. Constantin, and Ștefan Negrila, ‘Computer Aided Engineering of Industrial Robots’, Proceedings in Manufacturing Systems, vol. 9, no. 2, pp. 87–92, 2014.
N. Sreekanth, A. Dinesan, A. R. Nair, G. Udupa, and V. Tirumaladass, ‘Design of robotic manipulator for space applications’, Materials Today: Proceedings, vol. 46, no. 10, pp. 4962–4970, 2021, doi: 10.1016/j.matpr.2020.10.382.
Md. H. Ali, A. K., Y. K., Z. T., and A. O., ‘Vision-based Robot Manipulator for Industrial Applications’, Procedia Computer Science, vol. 133, pp. 205–212, 2018, doi: 10.1016/j.procs.2018.07.025.
K. Lochan and B. K. Roy, ‘Control of Two-link 2-DOF Robot Manipulator Using Fuzzy Logic Techniques: A Review’, in Proceedings of Fourth International Conference on Soft Computing for Problem Solving, New Delhi, 2015, pp. 499–511. doi: 10.1007/978-81-322-2217-0_41.
A. Kumar and V. Kumar, ‘Hybridized ABC-GA optimized fractional order fuzzy pre-compensated FOPID control design for 2-DOF robot manipulator’, AEU - International Journal of Electronics and Communications, vol. 79, pp. 219–233, 2017, doi: 10.1016/j.aeue.2017.06.008.
T. Zin, Htun, A. Kyaw, Soe, K. Win, and K. Phyu, ‘Design of 2-DOF Robot Manipulator’, May 2020.
G. Cui, H. Zhang, D. Zhang, and F. Xu, ‘Analysis of the Kinematic Accuracy Reliability of a 3-DOF Parallel Robot Manipulator’, International Journal of Advanced Robotic Systems, vol. 12, no. 2, p. 15, 2015, doi: 10.5772/60056.
S. E. Ivanov, T. Zudilova, T. Voitiuk, and L. N. Ivanova, ‘Mathematical Modeling of the Dynamics of 3-DOF Robot-Manipulator with Software Control’, Procedia Computer Science, vol. 178, pp. 311–319, 2020, doi: 10.1016/j.procs.2020.11.033.
A. Ashagrie, A. O. Salau, and T. Weldcherkos, ‘Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller’, Cogent Engineering, vol. 8, no. 1, p. 1950105, 2021, doi: 10.1080/23311916.2021.1950105.
S. Habibkhah and R. V. Mayorga, ‘The computation of the inverse kinematics of a 3 DOF redundant manipulator via an ann approach and a virtual function’, Potugal, 2020, pp. 471–477. [Online]. Available: https://www.scitepress.org/Papers/2020/98349/98349.pdf
F. Liu, G. Gao, L. Shi, and Y. Lv, ‘Kinematic analysis and simulation of a 3-DOF robotic manipulator’, in 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, India, 2017, pp. 1–5. doi: 10.1109/CIACT.2017.7977291.
V. N. Iliukhin, K. B. Mitkovskii, D. A. Bizyanova, and A. A. Akopyan, ‘The Modeling of Inverse Kinematics for 5 DOF Manipulator’, Procedia Engineering, vol. 176, pp. 498–505, 2017, doi: 10.1016/j.proeng.2017.02.349.
L. Žlajpah, R. Korba, S. Strmcnik, and F. Bremsak, ‘Modelling And Simulation Of Robot Arm’, International Journal of Modelling and Simulation, vol. 2, no. 4, pp. 188–191, 1982, doi: 10.1080/02286203.1982.11759798.
Y. Oktarina, F. Septiarini, T. Dewi, P. Risma, and M. Nawawi, ‘Fuzzy-PID Controller Design of 4 DOF Industrial Arm Robot Manipulator’, Computer Engineering and Applications Journal, vol. 8, no. 2, Art. no. 2, 2019, doi: 10.18495/comengapp.v8i2.300.
S. Gómez et al., ‘Design of a 4-DOF Robot Manipulator with Optimized Algorithm for Inverse Kinematics’, International Journal of Mechanical and Mechatronics Engineering, vol. 9, no. 6, pp. 929–934, 2015.
M. J. Hayawi, ‘Analytical Inverse kinematics Algorithm Of A 5-DOF Robot Arm’, Journal of Education for Pure Science, vol. 1, no. 4, 2011, Accessed: Jul. 24, 2022. [Online]. Available: https://www.iasj.net/iasj/article/19522
A. D. Marchese and D. Rus, ‘Design, kinematics, and control of a soft spatial fluidic elastomer manipulator’, The International Journal of Robotics Research, vol. 35, no. 7, pp. 840–869, 2016, doi: 10.1177/0278364915587925.
W. Widhiada, I. G. N. Santhiarsa, and C. G. I. Partha, ‘Design of Motion Control for Mobile Robot Manipulator’, IJMERR, vol. 9, no. 11, pp. 1509–1514, 2020, doi: 10.18178/ijmerr.9.11.1509-1514.
W. H. V. Deberg, ‘Robotic manipulator’, US4435116A, 1984 Accessed: Jul. 24, 2022. [Online]. Available: https://patents.google.com/patent/US4435116A/en
J. Shah, S. S. Rattan, and B. C. Nakra, ‘Dynamic analysis of two link robot manipulator for control design using computed torque control’, International Journal of Research in Computer Applications and Robotics, vol. 3, no. 1, Art. no. 1, 2015.
R. Benotsmane, S. Kacemi, L. Dudás, and G. Kovács, ‘Simulation of Industrial Robots Six Axes Manipulator Arms - A Case Study’, Academic Journal of Manufacturing Engineering, vol. 19, no. 1, pp. 89–97, 2021.
H. S. Lee and S. L. Chang, ‘Development of a CAD/CAE/CAM system for a robot manipulator’, Journal of Materials Processing Technology, vol. 140, no. 1, pp. 100–104, 2003, doi: 10.1016/S0924-0136(03)00695-2.
M. C. Reddy et al., ‘C-Phycocyanin, a selective cyclooxygenase-2 inhibitor, induces apoptosis in lipopolysaccharide-stimulated RAW 264.7 macrophages’, Biochem Biophys Res Commun, vol. 304, no. 2, pp. 385–392, 2003, doi: 10.1016/s0006-291x(03)00586-2.
K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002, doi: 10.1109/4235.996017.
M. Ceccarelli and C. Lanni, ‘A multi-objective optimum design of general 3R manipulators for prescribed workspace limits’, Mechanism and Machine Theory, vol. 39, no. 2, pp. 119–132, 2004, doi: 10.1016/S0094-114X(03)00109-5.
S. Panda, D. Mishra, and B. B. Biswal, ‘A Multi-objective Workspace Optimization of 3R Manipulator Using Modified PSO’, in Swarm, Evolutionary, and Memetic Computing, Berlin, Heidelberg, 2012, pp. 90–97. doi: 10.1007/978-3-642-35380-2_12.
S. Gale, H. Rahmati, J. T. Gravdahl, and H. Martens, ‘Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling’, Frontiers in Robotics and AI, vol. 4, no. 28, pp. 1–17, 2017.
S. Ramalingam and S. R. Mohideen, ‘Advanced Structural Fibre Material for Single Link Robotic Manipulator Simulation Analysis with Flexibility’, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 13, no. 4, pp. 38–46, 2019, doi: 10.14313/JAMRIS/4-2019/36.
F. Can, Ö. Lapçin, B. Ayan, and M. Çevik, ‘Human Machine Interface Design for a 3 DoF Robot Manipulator’, Universal Journal of Mechanical Engineering, vol. 6, no. 3, pp. 47–53, 2018, doi: 10.13189/ujme.2018.060301.
S. K. Shah and R. Mishra, ‘Experimental validation for inverse kinematics of a five axis robotic manipulator using deep artificial neural network’, International Journal of Innovation Technology and Exploring Engineering, vol. 9, no. 1, pp. 3943–3946, 2019, doi: 10.35940/ijitee.A5025.119119.
S. Baressi Šegota, N. Anđelić, I. Lorencin, M. Saga, and Z. Car, ‘Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms’, International Journal of Advanced Robotic Systems, vol. 17, no. 2, pp. 1–16, 2020, doi: 10.1177/1729881420908076.
C. Chu, K. Takahashi, and M. Hashimoto, ‘Comparison of Deep Reinforcement Learning Algorithms in a Robot Manipulator Control Application’, in 2020 International Symposium on Computer, Consumer and Control (IS3C), 2020, pp. 284–287. doi: 10.1109/IS3C50286.2020.00080.
W. S. Pambudi, E. Alfianto, A. Rachman, and D. P. Hapsari, ‘Simulation design of trajectory planning robot manipulator’, Bulletin of Electrical Engineering and Informatics, vol. 8, no. 1, Art. no. 1, 2019, doi: 10.11591/eei.v8i1.1179.
D. Silver et al., ‘A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play’, Science, vol. 362, no. 6419, pp. 1140–1144, 2018, doi: 10.1126/science.aar6404.
T. Lillicrap et al., ‘Continuous control with deep reinforcement learning’, CoRR, 2015.
S. Park, S. Chung, and H. Jeon, ‘Application of Genetic Algorithm to Hybrid Fuzzy Inference Engine’, Journal of the Korean Institute of Intelligent Systems, vol. 2, no. 3, pp. 58–67, 1992.
K. Saidi, A. Bournediene, and D. Boubekeur, ‘Genetic Algorithm Optimization of sliding Mode Controller Parameters for Robot Manipulator’, International Journal on Emerging Technologies, vol. 12, no. 2, pp. 119–127, 2021.
R. Köker, ‘A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization’, Information Sciences, vol. 222, pp. 528–543, 2013, doi: 10.1016/j.ins.2012.07.051.
T. Salloom, X. Yu, W. He, and O. Kaynak, ‘Adaptive Neural Network Control of Underwater Robotic Manipulators Tuned by a Genetic Algorithm’, J Intell Robot Syst, vol. 97, no. 3, pp. 657–672, 2020, doi: 10.1007/s10846-019-01008-y.
X. Meng, M. Zhang, and B. Adhikari, ‘Prediction of storage quality of fresh‐cut green peppers using artificial neural network’, International Journal of Food Science & Technology, vol. 47, no. 8, pp. 1–7, 2012, doi: 10.1111/j.1365-2621.2012.03007.x.
D. A. Korzhakin and E. Sugiharti, ‘Implementation of Genetic Algorithm and Adaptive Neuro Fuzzy Inference System in Predicting Survival of Patients with Heart Failure’, Scientific Journal of Informatics, vol. 8, no. 2, Art. no. 2, 2021, doi: 10.15294/sji.v8i2.32803.
P.-Z. Lin, W.-Y. Wang, T.-T. Lee, and C.-H. Wang, ‘On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm’, International Journal of Systems Science, vol. 40, no. 6, pp. 571–585, 2009, doi: 10.1080/00207720902750011.
S. Momani, Z. Abo-Hammour, and P.-O. Alsmadi, ‘Solution of Inverse Kinematics Problem using Genetic Algorithms’, Applied Mathematics & Information Sciences, vol. 10, no. 1, pp. 1–9, Jan. 2015, doi: 10.18576/amis/100122.
A. D. Dubey, R. B. Mishra, and A. K. Jha, ‘Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm’, International Journal of Computer Applications, vol. 51, no. 13, pp. 26–33, 2012.
S. Wang, Z. Wang, and Y. Hu, ‘Optimal control research on a manipulator’s combined feedback device by the variational method genetic algorithm radial basis function method’, International Journal of Advanced Robotic Systems, vol. 16, no. 3, pp. 1–14, 2019, doi: 10.1177/1729881419855824.
A. Sehgal, N. Ward, H. La, and S. Louis, Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks. 2022.
Y. Bai, M. Luo, and F. Pang, ‘An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network’, Applied Sciences, vol. 11, no. 15, Art. no. 15, 2021, doi: 10.3390/app11157129.
N. H. Singh and K. Thongam, ‘Fuzzy Logic-Genetic Algorithm-Neural Network for Mobile Robot Navigation: A Survey’, International Research Journal of Engineering and Technology, vol. 4, no. 8, pp. 24–35, 2017.
C.-T. Lee and J.-Y. (James) Chang, ‘A Workspace-Analysis-Based Genetic Algorithm for Solving Inverse Kinematics of a Multi-Fingered Anthropomorphic Hand’, Applied Sciences, vol. 11, no. 6, Art. no. 6, 2021, doi: 10.3390/app11062668.