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THESIS BIBILIOGRAPHY

5 Fischer, Ian. Geometric method for determining joint rotations in the inverse
kinematics of robotic manipulators. Journal of Robotic Systems 2000.17, 107 - 117.
Doi: 10.1002/(SICI)1097-4563(200002)17:2<107::AID-ROB3>3.0.CO;2-Y.
6 Iliukhin, V.N.; Mitkovskii, K.B.; Bizyanova, D.A.; Akopyan, A.A. The Modeling of
Inverse Kinematics for 5 DOF Manipulator. Procedia Engineering 2017, 176, 498-
505. Doi :10.1016/j.proeng.2017.02.349.
7 Petrenko, Viacheslav ; Tebueva, Fariza ; Gurchinsky, Mikhail ; Antonov, V. ;
Shutova, Yu. The method of forming a geometric solution of the inverse kinematics
problem for chains with kinematic pairs of rotational type only. IOP Conference
Series: Materials Science and Engineering 2018, 450(4),1-7. Doi: 10.1088/1757-
899X/450/4/042016.
8 Abaas, Tahseen F.; Ali A. Khleif; Mohanad Q. Abbood. Inverse Kinematics Analysis
and Simulation of a 5 DOF Robotic Arm using MATLAB. Al-Khwarizmi
Engineering Journal 2020,16(1),1-10.
9 Lin, Ming-Tzong; Hong-Bo Lin; Chung-Ching Liu;Ying-Lung Lin; Che-Hau Wu; and
Cheng-Wei Tung. Algebraic-elimination based solution of inverse kinematics for a
humanoid robot finger. Proceedings of the 2011 IEEE International Conference on
Mechatronics and Automation,Beijing, China, 7-10 Aug;2011; pp. 46-51.
10 Kumar, Virendra ; Sen, Soumen ; Roy, S.S. ; Das, S.K ; Shome, Sankar. (2015).
Inverse Kinematics of Redundant Manipulator using Interval Newton Method.
International Journal of Engineering and Manufacturing. 2015, 5,19-29. Doi:
10.5815/ijem.2015.02.03.
11 Shukla, Priya, and G. C. Nandi. Reinforcement Learning for Robots with special
reference to the Inverse kinematics solutions. Proceedings of the 2018 Conference on
Information and Communication Technology (CICT), Jabalpur, India,26-28 Oct;2018;
pp. 1-6.
12 Perrusquía, Adolfo; Wen Yu; Xiaoou Li. Multi-agent reinforcement learning for
redundant robot control in task-space. International Journal of Machine Learning and
Cybernetics 2021,12(1),231-241.
13 Šegota SB, Anđelić N, Mrzljak V, Lorencin I, Kuric I, Car Z. Utilization of multilayer
perceptron for determining the inverse kinematics of an industrial robotic
manipulator. International Journal of Advanced Robotic Systems. 2021;18(4).
doi:10.1177/1729881420925283
14 Rijalusalam, D.U. and Iswanto, I., 2021. Implementation kinematics modeling and
odometry of four omni wheel mobile robot on the trajectory planning and motion
control based microcontroller. Journal of Robotics and Control (JRC), 2(5), pp.448-
455.
15 Mansoor, M.I. and Kadri, M.B., 2021, January. Evaluating different Kinematic
Models of Mobile robots using Linear and Non-linear controls. In 2021 International
Bhurban Conference on Applied Sciences and Technologies (IBCAST) (pp. 560-567).
IEEE.
16 Tao, B., Zhao, X., Yan, S. and Ding, H., 2022. Kinematic modeling and control of
mobile robot for large-scale workpiece machining. Proceedings of the Institution of
Mechanical Engineers, Part B: Journal of Engineering Manufacture, 236(1-2), pp.29-
38.
17 Nguyen, A.T. and Vu, C.T., 2022. Mobile Robot Motion Control Using a
Combination of Fuzzy Logic Method and Kinematic Model. In Intelligent Systems
and Networks (pp. 495-503). Springer, Singapore.
18 Mai, T.A., Dang, T.S., Duong, D.T. et al. A combined backstepping and adaptive
fuzzy PID approach for trajectory tracking of autonomous mobile robots. J Braz. Soc.
Mech. Sci. Eng. 43, 156 (2021). https://doi.org/10.1007/s40430-020-02767-8
19 R. Singh, G. Singh and V. Kumar, "Control of closed-loop differential drive mobile
robot using forward and reverse Kinematics," 2020 Third International Conference
on Smart Systems and Inventive Technology (ICSSIT), 2020, pp. 430-433, doi:
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20 M. Bakirci and B. Toptas, "Kinematics and Autoregressive Model Analysis of a
Differential Drive Mobile Robot," 2022 International Congress on Human-Computer
Interaction, Optimization and Robotic Applications (HORA), 2022, pp. 1-6, doi:
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21 Hentout, A., Maoudj, A. & Aouache, M. A review of the literature on fuzzy-logic
approaches for collision-free path planning of manipulator robots. Artif Intell
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22 Z. -c. Du, G. -Y. Ouyang, J. Xue and Y. -b. Yao, "A Review on Kinematic,
Workspace, Trajectory Planning and Path Planning of Hyper-Redundant
manipulators," 2020 10th Institute of Electrical and Electronics Engineers
International Conference on Cyber Technology in Automation, Control, and
Intelligent Systems (CYBER), 2020, pp. 444-449, doi:
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23 Zhong, J., Wang, T. & Cheng, L. Collision-free path planning for welding
manipulator via hybrid algorithm of deep reinforcement learning and inverse
kinematics. Complex Intell. Syst. 8, 1899–1912 (2022).
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24 Kumar, S., Reddy, B.S. (2022). Path-Planning of Robot End-Effector for Hairline
Crack Sealing Using Intelligent Techniques. In: Popat, K.C., Kanagaraj, S.,
Sreekanth, P.S.R., Kumar, V.M.R. (eds) Advances in Mechanical Engineering and
Material Science. ICAMEMS 2022. Lecture Notes in Mechanical Engineering.
Springer, Singapore. https://doi.org/10.1007/978-981-19-0676-3_22
25 Kang J-G, Lim D-W, Choi Y-S, Jang W-J, Jung J-W. Improved RRT-Connect
Algorithm Based on Triangular Inequality for Robot Path Planning. Sensors. 2021;
21(2):333. https://doi.org/10.3390/s21020333
26 Yuan X, Yuan X, Wang X. Path Planning for Mobile Robot Based on Improved Bat
Algorithm. Sensors. 2021; 21(13):4389. https://doi.org/10.3390/s21134389
27 Zhang, TW., Xu, GH., Zhan, XS. et al. A new hybrid algorithm for path planning of
mobile robot. J Supercomput 78, 4158–4181 (2022). https://doi.org/10.1007/s11227-
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28 S. Zhang, J. Pu and Y. Si, "An Adaptive Improved Ant Colony System Based on
Population Information Entropy for Path Planning of Mobile Robot," in IEEE Access,
vol. 9, pp. 24933-24945, 2021, doi: 10.1109/ACCESS.2021.3056651
29 Rybus, T., Wojtunik, M. and Basmadji, F.L., 2022. Optimal collision-free path
planning of a free-floating space robot using spline-based trajectories. Acta
Astronautica, 190, pp.395-408.
30 Gul, F., Rahiman, W., Alhady, S.S.N. et al. Meta-heuristic approach for solving
multi-objective path planning for autonomous guided robot using PSO–GWO
optimization algorithm with evolutionary programming. J Ambient Intell Human
Comput 12, 7873–7890 (2021). https://doi.org/10.1007/s12652-020-02514-w
31 Song, B., Wang, Z. and Zou, L., 2021. An improved PSO algorithm for smooth path
planning of mobile robots using continuous high-degree Bezier curve. Applied Soft
Computing, 100, p.106960.
32 Xu, L., Cao, M. and Song, B., 2022. A new approach to smooth path planning of
mobile robot based on quartic Bezier transition curve and improved PSO
algorithm. Neurocomputing, 473, pp.98-106.
33 Outamazirt F, Djaidja D, Boudjimar C, et al. Multi-sensor fusion approach based on
nonlinear H∞ filter with interval type 2 fuzzy adaptive parameters tuning for
unmanned vehicle localization. Proceedings of the Institution of Mechanical
Engineers, Part I: Journal of Systems and Control Engineering. 2021;235(6):881-
897. doi:10.1177/0959651820961603
34 Faria, S., Lima, J., Costa, P. (2021). Sensor Fusion for Mobile Robot Localization
Using Extended Kalman Filter, UWB ToF and ArUco Markers. In: , et
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Communications in Computer and Information Science, vol 1488. Springer, Cham.
https://doi.org/10.1007/978-3-030-91885-9_17
35 Zhou, G., Luo, J., Xu, S., Zhang, S., Meng, S. and Xiang, K. (2021), "An EKF-based
multiple data fusion for mobile robot indoor localization", Assembly Automation, Vol.
41 No. 3, pp. 274-282. https://doi.org/10.1108/AA-12-2020-0199
36 Housein, A.A., Xingyu, G., Li, W. and Huang, Y., 2022. Extended Kalman Filter
Sensor Fusion in Practice for Mobile Robot Localization. International Journal of
Advanced Computer Science and Applications, 13(2).
37 Sangeetha, V., Krishankumar, R., Ravichandran, K.S. et al. Energy-efficient green ant
colony optimization for path planning in dynamic 3D environments. Soft Comput 25,
4749–4769 (2021). https://doi.org/10.1007/s00500-020-05483-6
38 Mohammadpour M, Zeghmi L, Kelouwani S, Gaudreau M-A, Amamou A, Graba M.
An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile
Robots. Energies. 2021; 14(12):3517. https://doi.org/10.3390/en14123517
39 Zhang, Z., Wu, L., Zhang, W., Peng, T. and Zheng, J., 2021. Energy-efficient path
planning for a single-load automated guided vehicle in a manufacturing
workshop. Computers & Industrial Engineering, 158, p.107397.
40 Wu G, Zhao W, Zhang X. Optimum time-energy-jerk trajectory planning for serial
robotic manipulators by reparameterized quintic NURBS curves. Proceedings of the
Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering
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41 Lonklang A, Botzheim J. Improved Rapidly Exploring Random Tree with Bacterial
Mutation and Node Deletion for Offline Path Planning of Mobile Robot. Electronics.
2022; 11(9):1459. https://doi.org/10.3390/electronics11091459
42 Kumar, S., Sikander, A. An intelligent optimize path planner for efficient mobile
robot path planning in a complex terrain. Microsyst Technol (2022).
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43 Yan Y, Zhang B, Zhou J, Zhang Y, Liu X. Real-Time Localization and Mapping
Utilizing Multi-Sensor Fusion and Visual–IMU–Wheel Odometry for Agricultural
Robots in Unstructured, Dynamic and GPS-Denied Greenhouse
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44 R. Szczepanski, T. Tarczewski and K. Erwinski, "Energy Efficient Local Path
Planning Algorithm Based on Predictive Artificial Potential Field," in IEEE Access,
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45 Takahashi, T., Sun, H., Tian, D., & Wang, Y. (2021). Learning Heuristic Functions
for Mobile Robot Path Planning Using Deep Neural Networks. Proceedings of the
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46 S. Bai, F. Chen and B. Englot, "Toward autonomous mapping and exploration for
mobile robots through deep supervised learning," 2017 IEEE/RSJ International
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47 Zou, Y; Zhou, W. Automatic seam detection and tracking system for robots based on
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48 Pandya, H., Gaud, A., Kumar, G., & Krishna, K. M. (2019). Instance invariant visual
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49 Shi, H., Xu, M., & Hwang, K. S. (2019). A fuzzy adaptive approach to decoupled
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50 Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., & Corke, P. (2017). Visual
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51 Castelli, F., Michieletto, S., Ghidoni, S., & Pagello, E. (2017). A machine learning-
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52 Wenming Guo, Lin Huang, Lihong Liang, A Weld Seam Dataset and Automatic
Detection of Welding Defects Using Convolutional Neural Network, Springer Nature
Switzerland AG, 2020, pp. 434–443, AISC 905.
53 Yongcui Mi, Fredrik Sikstrom, Morgan Nilsen, Antonio Ancona, Vision based beam
offset detection in laser stake welding of T-joints using Neural Networks, in: 17th
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54 Wu, P., Cao, Y., He, Y., & Li, D. (2017). Vision-Based Robot Path Planning with
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55 Nuzzi, C., Pasinetti, S., Lancini, M., Docchio, F., & Sansoni, G. (2019). Deep
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56 Miroslav, S., Karel, K., Miroslav, K., Viktor, K., & Libor, P. (2019). Visual Data
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57 Zheng, W., Wang, HB., Zhang, ZM. et al. Multi-layer Feed-forward Neural Network
Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile
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58 C. Zhihong, Z. Hebin, W. Yanbo, L. Binyan and L. Yu, "A vision-based robotic
grasping system using deep learning for garbage sorting," 2017 36th Chinese Control
Conference (CCC), 2017, pp. 11223-11226, doi: 10.23919/ChiCC.2017.8029147.
59 Lin, C.-M., Tsai, C.-Y., Lai, Y.-C., Li, S.-A., & Wong, -C.-C. (2018). Visual Object
Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network.
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60 Yang, Y., Juntao, L. and Lingling, P., 2020. Multi‐robot path planning based on a
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61 Cherroun, L., Boumehraz, M., Kouzou, A. (2019). Mobile Robot Path Planning Based
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62 Wang B, Liu Z, Li Q, Prorok A. Mobile robot path planning in dynamic environments
through globally guided reinforcement learning. IEEE Robotics and Automation
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63 Lee H, Jeong J. Mobile robot path optimization technique based on reinforcement
learning algorithm in warehouse environment. Applied Sciences. 2021 Jan;11(3):1-16.
64 Dong Y, Zou X. Mobile Robot Path Planning Based on Improved DDPG
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65 Quan H, Li Y, Zhang Y. A novel mobile robot navigation method based on deep
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66 Bae H, Kim G, Kim J, Qian D, Lee S. Multi-Robot Path Planning Method Using
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67 J. Xie, Z. Shao, Y. Li, Y. Guan and J. Tan, "Deep Reinforcement Learning With
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68 V. N. Sichkar, "Reinforcement Learning Algorithms in Global Path Planning for
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69 Iriondo, A., Lazkano, E., Susperregi, L., Urain, J., Fernandez, A., & Molina, J.
(2019). Pick and place operations in logistics using a mobile manipulator controlled
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70 Wang, B., Liu, Z., Li, Q., & Prorok, A. (2020). Mobile robot path planning in
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71 Gao, J., Ye, W., Guo, J., & Li, Z. (2020). Deep reinforcement learning for indoor
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72 https://www.strategicmarketresearch.com/blogs/robotics-industry-statistics

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