Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

Faikul Umam, Muhammad Fuad, Iswanto Suwarno, Alfian Ma'arif, Wahyu Caesarendra

Abstract


This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.

Keywords


Artificial Intelligence, Stereo Vision, Navigation, Obstacle Avoidance, Omnidirecional robot

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References


T. E. Mora and E. N. Sanchez, “Fuzzy logic-based real-time navigation controller for a mobile robot,” in Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190), vol. 1, pp. 612–617, 1998, doi: 10.1109/IROS.1998.724686.

L. M. Waghmare, P. Tallapragada, and N. Bidwai, “Reactive Navigation of Autonomous Vehicle using Neuro-Fuzzy Controller,” in 2006 IEEE International Conference on Industrial Technology, pp. 2681–2685, 2006, doi: 10.1109/ICIT.2006.372675.

S. Seghour and M. Tadjine, “Consensus-based approach and reactive fuzzy navigation for multiple no-holonomic mobile robots,” in 2017 6th International Conference on Systems and Control (ICSC), pp. 492–497, 2017, doi: 10.1109/ICoSC.2017.7958658.

T. Shen and J. Zhai, “Reactive Obstacle Avoidance Strategy Based on Fuzzy Neural Network and Arc Trajectory,” in 2019 Chinese Automation Congress (CAC), pp. 4792–4796, 2019, doi: 10.1109/CAC48633.2019.8996374.

Y. Wang and Y. Yuan, “A dynamic reactive power compensation method for high-power and high-voltage electronic motors based on self-adaptive fuzzy PID control,” in 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), pp. 10–15, 2016, doi: 10.1109/CGNCC.2016.7828749.

E. Ruiz, R. Acuña, P. Véélez, and G. Fernández-López, “Hybrid Deliberative Reactive Navigation System for Mobile Robots Using ROS and Fuzzy Logic Control,” in 2015 12th Latin American Robotics Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR), pp. 67–72, 2015, doi: 10.1109/LARS-SBR.2015.24.

P. Chand, “Fuzzy reactive control for wheeled mobile robots,” in 2015 6th International Conference on Automation, Robotics and Applications (ICARA), pp. 167–172, 2015, doi: 10.1109/ICARA.2015.7081142.

H. Zerfa and W. Nouibat, “Fuzzy reactive navigation for autonomous mobile robot with an offline adaptive neuro fuzzy system,” in 3rd International Conference on Systems and Control, pp. 950–955, 2013, doi: 10.1109/ICoSC.2013.6750971.

A. Melendez and O. Castillo, “Optimization of type-2 fuzzy reactive controllers for an autonomous mobile robot,” in 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 207–211, 2012, doi: 10.1109/NaBIC.2012.6402263.

E. Baklouti, M. Jallouli, N. Ben Amor, S. Titi, and A. Nafti, “Autonomous mobile robot navigation coupling fuzzy logic and reactive DVZ 3D obstacle avoidance control,” in 2015 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–6, 2015,doi: 10.1109/INISTA.2015.7276748.

Y. Lv and P. Jiang, “The Design of Indoor Mobile Robot Navigation System Based on UWB Location,” in 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), pp. 334–338, 2018, doi: 10.1109/IMCCC.2018.00077.

A. Itta, G. Attolico, and A. Distante, “Combining reactive behaviors using a hierarchy of fuzzy controllers,” in Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), vol. 2, pp. 1041–1044, 2000, doi: 10.1109/FUZZY.2000.839194.

M. A. O. Mendez and J. A. F. Madrigal, “Fuzzy Logic User Adaptive Navigation Control System For Mobile Robots In Unknown Environments,” in 2007 IEEE International Symposium on Intelligent Signal Processing, pp. 1–6, 2007, doi: 10.1109/WISP.2007.4447633.

I. I. Ismail and M. F. Nordin, “Reactive navigation of autonomous guided vehicle using fuzzy logic,” in Student Conference on Research and Development, pp. 153–156, 2002, doi: 10.1109/SCORED.2002.1033080.

N. Zhang, D. Beetner, D. C. Wunsch, B. Hemmelman, and A. Hasan, “An Embedded Real-Time Neuro-Fuzzy Controller for Mobile Robot Navigation,” in The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ ’05., pp. 319–324, 2005, doi: 10.1109/FUZZY.2005.1452413.

G. Castellano, G. Attolico, E. Stella, and A. Distante, “Reactive navigation by fuzzy control,” in Proceedings of IEEE 5th International Fuzzy Systems, vol. 3, pp. 2143–2149, 1996, doi: 10.1109/FUZZY.1996.552796.

W. L. Xu and S. K. Tso, “Sensor-based fuzzy reactive navigation of a mobile robot through local target switching,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 29, no. 3, pp. 451–459, 1999, doi: 10.1109/5326.777079.

M. M. Joshi and M. A. Zaveri, “Fuzzy Based Autonomous Robot Navigation System,” in 2009 Annual IEEE India Conference, pp. 1–4, 2009, doi: 10.1109/INDCON.2009.5409419.

M. Alwan, P. Y. K. Cheung, A. Saleh, and N. E. C. Obeid, “Combining goal-directed, reactive and reflexive navigation in autonomous mobile robots,” in 1996 Australian New Zealand Conference on Intelligent Information Systems. Proceedings. ANZIIS 96, pp. 346–349, 1996, doi: 10.1109/ANZIIS.1996.573982.

A. A. S. Al-Jumaily and S. H. M. Amin, “Fuzzy logic based behaviors blending for intelligent reactive navigation of walking robot,” in ISSPA ’99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359), vol. 1, pp. 155–158, 1999, doi: 10.1109/ISSPA.1999.818136.

M. Skubic, S. Graves, and J. Mollenhauer, “Design of a two-level fuzzy controller for a reactive miniature mobile robot,” in Third International Conference on Industrial Fuzzy Control and Intelligent Systems, pp. 224–227, 1993, doi: 10.1109/IFIS.1993.324183.

J. L. Overholt, G. R. Hudas, and K. C. Cheok, “A modular neural-fuzzy controller for autonomous reactive navigation,” in NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 121–126, 2005, doi: 10.1109/NAFIPS.2005.1548519.

W. Li, “Fuzzy logic based robot navigation in uncertain environments by multisensor integration,” in Proceedings of 1994 IEEE International Conference on MFI ’94. Multisensor Fusion and Integration for Intelligent Systems, pp. 259–264, 1994, doi: 10.1109/MFI.1994.398444.

N. Melik and N. Slimane, “Autonomous navigation with obstacle avoidance of tricycle mobile robot based on fuzzy controller,” in 2015 4th International Conference on Electrical Engineering (ICEE), pp. 1–4, 2015, doi: 10.1109/INTEE.2015.7416799.

O. Aycard, F. Charpillet, and J.-P. Haton, “A new approach to design fuzzy controllers for mobile robots navigation,” in Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97. ‘Towards New Computational Principles for Robotics and Automation,’ pp. 68–73, 1997, doi: 10.1109/CIRA.1997.613840.

B. C. Arrue, F. Cuesta, R. Braunstingl, and A. Ollero, “Fuzzy behaviors combination to control a nonholonomic mobile robot using virtual perception memory,” in Proceedings of 6th International Fuzzy Systems Conference, vol. 3. pp. 1239–1244, 1997, doi: 10.1109/FUZZY.1997.619465.

W. Li and X. Feng, “Behavior fusion for robot navigation in uncertain environments using fuzzy logic,” in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1790–1796, 1994, doi: 10.1109/ICSMC.1994.400110.

A. A. S. Al-Jumaily and S. H. M. Amin, “Behaviors blending for intelligent reactive navigation of climbing robot,” in 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies, vol. 2, pp. 795–799, 2000, doi: 10.1109/IECON.2000.972224.

X. Yang, M. Moallem, and R. V Patel, “A fuzzy logic-based reactive navigation algorithm for mobile robots,” in Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005., pp. 197–202, 2005, doi: 10.1109/CCA.2005.1507124.

Y. Duan and Xin-Hexu, “Fuzzy reinforcement learning and its application in robot navigation,” in 2005 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 899-904, 2005, doi: 10.1109/ICMLC.2005.1527071.

W. L. Xu, S. K. Tso, and Y. H. Fung, “Sensor-based reactive navigation of a mobile robot through local target switching,” in 1997 8th International Conference on Advanced Robotics. Proceedings. ICAR’97, pp. 361–366, 1997, doi: 10.1109/ICAR.1997.620207.

A. M. Anvar, “Intelligent navigation process for autonomous underwater vehicles (AUVs) using time-based fuzzy temporal reasoning,” in 10th International Symposium on Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic. Proceedings., pp. 56–61, 2003, doi: 10.1109/TIME.2003.1214880.

H. Maaref and C. Barret, “Fuzzy help in mobile robot navigation,” in Proceedings IEEE Conference on Industrial Automation and Control Emerging Technology Applications, pp. 387–390, 1995, doi: 10.1109/IACET.1995.527593.

C. Barret, M. Benreguieg, and H. Maaref, “Fuzzy agents for reactive navigation of a mobile robot,” in Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES ’97, vol. 2, pp. 649–658, 1997, doi: 10.1109/KES.1997.619449.

A. A. S. Al-Jumaily, S. H. M. Amin, and M. Khalil, “A fuzzy multi-behaviour reactive obstacle avoidance navigation for a climbing mobile robot,” in Proceedings of IEEE International Conference on Intelligent Engineering Systems, pp. 147–152, 1997, doi: 10.1109/INES.1997.632408.

W. Li, “A hybrid neuro-fuzzy system for sensor based robot navigation in unknown environments,” in Proceedings of 1995 American Control Conference - ACC’95, vol. 4, pp. 2749–2753, 1995, doi: 10.1109/ACC.1995.532349.

G. Mester, “Obstacle Avoidance of Mobile Robots in Unknown Environments,” in 2007 5th International Symposium on Intelligent Systems and Informatics, pp. 123–127, 2007, doi: 10.1109/SISY.2007.4342637.

M. Benreguieg, H. Maaref, and C. Barret, “Fusion of fuzzy agents for the reactive navigation of a mobile robot,” in Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS ’97, vol. 1, pp. 388–394, 1997, doi: 10.1109/IROS.1997.649091.

M. Dupre and S. X. Yang, “Two-Stage Fuzzy Logic-Based Controller for Mobile Robot Navigation,” in 2006 International Conference on Mechatronics and Automation, pp. 745–750, 2006, doi: 10.1109/ICMA.2006.257683.

A. Jayasiri, G. K. I. Mann, and R. G. Gosine, “Supervisory control of Fuzzy Discrete Event Systems and its application to mobile robot navigation,” in 2009 Canadian Conference on Electrical and Computer Engineering, pp. 1147–1151, 2009, doi: 10.1109/CCECE.2009.5090305.

D. Shi, M. F. Selekwa, E. G. Collins, and C. A. Moore, “Fuzzy behavior navigation for an unmanned helicopter in unknown environments,” in 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3897-3902. 2005, doi: 10.1109/ICSMC.2005.1571754.

G. Mondelli, G. Castellano, G. Attolico, E. Stella, and A. Distante, “Self-tuning fuzzy logic controller for reactive navigation,” in Proceedings of Conference on Intelligent Vehicles, pp. 87–92, 1996, doi: 10.1109/IVS.1996.566358.

M. F. Selekwa, D. D. Dunlap, and E. G. Collins, “Implementation of Multi-valued Fuzzy Behavior Control for Robot Navigation in Cluttered Environments,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 3688–3695, 2005, doi: 10.1109/ROBOT.2005.1570682.

B. B. K. Reddy, B. Kimiaghalam, and A. Homaifar, “Reactive real time behavior for mobile robots in unknown environments,” in 2004 IEEE International Symposium on Industrial Electronics, vol. 1, pp. 693–697, 2004,doi: 10.1109/ISIE.2004.1571890.

S. Kundu and D. R. Parhi, “Behavior-based navigation of multiple robotic agents using hybrid-fuzzy controller,” in 2010 International Conference on Computer and Communication Technology (ICCCT), pp. 706–711, 2010, doi: 10.1109/ICCCT.2010.5640437.

A. Safiotti, “Fuzzy logic in autonomous robotics: behavior coordination,” in Proceedings of 6th International Fuzzy Systems Conference, vol. 1, pp. 573–578, 1997, doi: 10.1109/FUZZY.1997.616430.

H. Maaref and C. Barret, “Progressive optimization of a fuzzy inference system,” in Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), vol. 1, pp. 47–52, 2001, doi: 10.1109/NAFIPS.2001.944225.

J. C. Vega Oliver and P. F. Huamaní Navarrete, “Fuzzy control to simulate 4 autonomous navigation behaviors in a differential-drive mobile robot,” in 2017 IEEE International Conference on Aerospace and Signals (INCAS), pp. 1–4, 2017, doi: 10.1109/INCAS.2017.8123498.

J. Yung-Jen Hsu, D.-C. Lo, and S.-C. Hsu, “Fuzzy control for behavior-based mobile robots,” in NAFIPS/IFIS/NASA ’94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent, pp. 209–213, 1994, doi: 10.1109/IJCF.1994.375097.

A. Zhu and S. X. Yang, “A goal-oriented fuzzy reactive control for mobile robots with automatic rule optimization,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3688–3693, 2010, doi: 10.1109/IROS.2010.5651799.

A. Zhu and S. X. Yang, “A fuzzy logic approach to reactive navigation of behavior-based mobile robots,” in IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004, vol. 5, pp. 5045-5050, 2004, doi: 10.1109/ROBOT.2004.1302517.

S. Kundu and D. R. Parhi, “Fuzzy based reactive navigational strategy for mobile agent,” in 2010 International Conference on Industrial Electronics, Control and Robotics, pp. 12–17, 2010, doi: 10.1109/IECR.2010.5720149.

S. G. Goodridge and R. C. Luo, “Fuzzy behavior fusion for reactive control of an autonomous mobile robot: MARGE,” in Proceedings of the 1994 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1622–1627, 1994, doi: 10.1109/ROBOT.1994.351358.

K. C. Tan, K. K. Tan, T. H. Lee, S. Zhao, and Y. J. Chen, “Autonomous robot navigation based on fuzzy sensor fusion and reinforcement learning,” in Proceedings of the IEEE International Symposium on Intelligent Control, pp. 182–187, 2002, doi: 10.1109/ISIC.2002.1157759.

M. A. Sotelo et al., “Vehicle fuzzy driving based on DGPS and vision,” in Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), vol. 3, pp. 1472–1477, 2001, doi: 10.1109/NAFIPS.2001.943766.

A. Howard, B. Werger, and H. Seraji, “Integrating terrain maps into a reactive navigation strategy,” in 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), vol. 2, pp. 2012–2017, 2003, doi: 10.1109/ROBOT.2003.1241889.

H. Liu, P. Hu, Y. Luo, and C. Li, “A goal-oriented fuzzy reactive control method for mobile robot navigation in unknown environment,” in 2009 IEEE International Symposium on Industrial Electronics, pp. 1950–1955, 2009, doi: 10.1109/ISIE.2009.5219773.

H. A. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 524–539, 2004, doi: 10.1109/TFUZZ.2004.832538.

S. Kundu and R. P. Dayal, “A fuzzy approach towards behavioral strategy for navigation of mobile agent,” in INTERACT-2010, pp. 292–297, 2010, doi: 10.1109/INTERACT.2010.5706164.

B.-K. Shim, J.-H. Kim, I.-M. Park, and S.-H. Han, “An intelligent control of non-holonomic mobile robot based on fuzzy perception,” in ICCAS 2010, pp. 2111–2114, 2010, doi: 10.1109/ICCAS.2010.5670182.

A. A. S. Al-Jumaily and S. H. M. Amin, “Blending multi-behaviors of intelligent reactive navigation for legged walking robot in unstructured environment,” in 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119), vol. 2, pp. 297–302, 2000, doi: 10.1109/TENCON.2000.888751.

J. G. Ortega and E. F. Camacho, “Mobile robot navigation in a partially structured static environment, using neural predictive control,” Control Eng Pract, vol. 4, no. 12, pp. 1669–1679, 1996, doi: https://doi.org/10.1016/S0967-0661(96)00184-0.

L. E. Zarate, M. Becker, B. D. M. Garrido, and H. S. C. Rocha, “An artificial neural network structure able to obstacle avoidance behavior used in mobile robots,” in IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, vol. 3, pp. 2457–2461, 2002, doi: 10.1109/IECON.2002.1185358.

O. Gamal, X. Cai, and H. Roth, “Learning from Fuzzy System Demonstration: Autonomous Navigation of Mobile Robot in Static Indoor Environment using Multimodal Deep Learning,” in 2020 24th International Conference on System Theory, Control and Computing (ICSTCC), pp. 218–225, 2020, doi: 10.1109/ICSTCC50638.2020.9259786.

A. S. Lafmejani, S. Berman, and G. Fainekos, “NMPC-LBF: Nonlinear MPC with Learned Barrier Function for Decentralized Safe Navigation of Multiple Robots in Unknown Environments,” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10297-10303, 2022, doi: 10.1109/IROS47612.2022.9981177.

G. Chen et al., "Robot Navigation with Map-Based Deep Reinforcement Learning," 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC), pp. 1-6, 2020, doi: 10.1109/ICNSC48988.2020.9238090.

Y. Pan and J. Wang, “A neurodynamic optimization approach to nonlinear model predictive control,” in 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 1597–1602, 2010, doi: 10.1109/ICSMC.2010.5642367.

N. Hirose, F. Xia, R. Martín-Martín, A. Sadeghian, and S. Savarese, “Deep Visual MPC-Policy Learning for Navigation,” IEEE Robot Autom Lett, vol. 4, no. 4, pp. 3184–3191, 2019, doi: 10.1109/LRA.2019.2925731.

T. Ono and T. Kanamaru, “Prediction of pedestrian trajectory based on long short-term memory of data,” in 2021 21st International Conference on Control, Automation and Systems (ICCAS), pp. 1676–1679, 2021, doi: 10.23919/ICCAS52745.2021.9649937.

T. Kim, H. Lee, S. Hong, and W. Lee, “TOAST: Trajectory Optimization and Simultaneous Tracking Using Shared Neural Network Dynamics,” IEEE Robot Autom Lett, vol. 7, no. 4, pp. 9747–9754, 2022, doi: 10.1109/LRA.2022.3184769.

Y. Guo, R. Jena, D. Hughes, M. Lewis, and K. Sycara, “Transfer Learning for Human Navigation and Triage Strategies Prediction in a Simulated Urban Search and Rescue Task,” in 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), pp. 784–791, 2021, doi: 10.1109/RO-MAN50785.2021.9515526.

A. Polevoy, C. Knuth, K. M. Popek, and K. D. Katyal, “Complex Terrain Navigation via Model Error Prediction,” 2022 International Conference on Robotics and Automation (ICRA), pp. 9411-9417, 2022, doi: 10.1109/ICRA46639.2022.9811644.

S. Lai, M. Lan, and B. M. Chen, “Model Predictive Local Motion Planning With Boundary State Constrained Primitives,” IEEE Robot Autom Lett, vol. 4, no. 4, pp. 3577–3584, 2019, doi: 10.1109/LRA.2019.2928255.

F. Gauthier-Clerc, A. Hill, J. Laneurit, R. Lenain, and É. Lucet, “Online velocity fluctuation of off-road wheeled mobile robots: A reinforcement learning approach,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 2421–2427, 2021, doi: 10.1109/ICRA48506.2021.9560816.

E. P. Ferreira and V. M. Miranda, “Development of static neural networks as full predictors or controllers for multi-articulated mobile robots in backward movements - new models and tools,” in 2011 9th IEEE International Conference on Control and Automation (ICCA), pp. 985–990, 2011, doi: 10.1109/ICCA.2011.6138028.

N. D. Weerakkodi Mudalige et al., “DogTouch: CNN-based Recognition of Surface Textures by Quadruped Robot with High Density Tactile Sensors,” in 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), pp. 1–5, 2022, doi: 10.1109/VTC2022-Spring54318.2022.9860815.

X. Zou, B. Sun, D. Zhao, Z. Zhu, J. Zhao, and Y. He, “Multi-Modal Pedestrian Trajectory Prediction for Edge Agents Based on Spatial-Temporal Graph,” IEEE Access, vol. 8, pp. 83321–83332, 2020, doi: 10.1109/ACCESS.2020.2991435.

I. Engedy and G. Horvath, “Artificial neural network based mobile robot navigation,” in 2009 IEEE International Symposium on Intelligent Signal Processing, pp. 241–246, 2009, doi: 10.1109/WISP.2009.5286557.

M. K. Bugeja, S. G. Fabri, and L. Camilleri, “Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 1, pp. 129–141, 2009, doi: 10.1109/TSMCB.2008.2002851.

J. Yuan, H. Wang, C. Lin, D. Liu, and D. Yu, “A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot,” IEEE Access, vol. 7, pp. 15140–15151, 2019, doi: 10.1109/ACCESS.2019.2894626.

M. J. Er, T. P. Tan, and S. Y. Loh, “Control of a mobile robot using generalized dynamic fuzzy neural networks,” Microprocess Microsyst, vol. 28, no. 9, pp. 491–498, 2004, doi: https://doi.org/10.1016/j.micpro.2004.04.002.

B.-Q. Huang, G.-Y. Cao, and M. Guo, “Reinforcement Learning Neural Network to the Problem of Autonomous Mobile Robot Obstacle Avoidance,” in 2005 International Conference on Machine Learning and Cybernetics, pp. 85–89, 2005, doi: 10.1109/ICMLC.2005.1526924.

S. Soumare, A. Ohya, and S. Yuta, “Real-time obstacle avoidance by an autonomous mobile robot using an active vision sensor and a vertically emitted laser slit,” In Intelligent Autonomous Systems, vol. 7, pp. 301-308, 2002.

M. Lauer, M. Schönbein, S. Lange, and S. Welker, “3D-objecttracking with a mixed omnidirectional stereo camera system,” Mechatronics, vol. 21, pp. 390–398, 2011.

M. Tahmasebi, M. Gohari, and A. Emami, “An Autonomous Pesticide Sprayer Robot with a Color-based Vision System,” International Journal of Robotics and Control Systems, vol. 2, no. 1, pp. 115–123, Feb. 2022, doi: 10.31763/ijrcs.v2i1.480.

S. D. Perkasa, P. Megantoro, and H. A. Winarno, “Implementation of a camera sensor pixy 2 cmucam5 to a two wheeled robot to follow colored object,” Journal of Robotics and Control (JRC), vol. 2, no. 6, pp. 496–501, Nov. 2021, doi: 10.18196/jrc.26128.

M. I. Rusydi et al., “Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera,” Journal of Robotics and Control (JRC), vol. 3, no. 3, pp. 361–373, May 2022, doi: 10.18196/jrc.v3i3.14750.




DOI: https://doi.org/10.18196/jrc.v4i2.17977

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