Path Planning Methods for Mobile Robots: A systematic and Bibliometric review

Authors

  • Aisha muhammad
  • Mohammed A. H. Ali
  • Ibrahim Haruna Shanono

DOI:

https://doi.org/10.11113/elektrika.v19n3.225

Keywords:

Path planning, Bibliometric, VOSviewer, navigation, Fuzzy logic

Abstract

Robots are currently replacing humans in different tasks in various sectors. Among the vital features desirable in autonomous robots is the capability of navigating safely through a given environment. Robot navigation is a process designed with the ability of avoiding any hitches or obstacles while aiming at a specific predefined position. Many studies have been proposed to find solutions to robot path-planning problems. This paper presents a survey of the heuristic and classical path-planning approaches. Focal strengths, together with the weaknesses of these approaches, were also identified to provide deep insight for future studies. As several literature studies have recommended, classical methods might not be effective in real-time applications as a result of their failure to confront the unpredictable nature of the real-world. They require a considerable amount of computation and space, while heuristic-based methods can overcome real-world problems with some modifications. To summarize the research progress and also suggest future directions of path-planning research, this study performs a bibliometric analysis of the relevant publications published from 2000 to 2020. The results show that 5385 articles were published in 1128 journals, hence indicating publication diversity. There is a steady rise in the yearly publication output, reflecting an increase in global research interest in the topic. In general, this research provides useful insight into path-planning research so that researchers in this area can better recognize the relevant research study topics and search for the appropriate research partners.

References

G. Alessandro, B. Paol, L. Albano, V. Renato. “Path Planning and Trajectory Planning Algorithms: A General Overviewâ€. In Motion and Operation Planning of Robotic Systems. Mechanisms and Machine Science: 29, Springer, Cham, 2015, p. 3-27.

B.Y. Shih, H. Chang, C.Y. Chen.: “Path planning for autonomous robots – a comprehensive analysis by a greedy algorithmâ€. Journal of Vibration and Control, vol. 19, no 1, pp. 130-142. 2013

V. Kunchev, L. Jain , V. Ivancevic, A. Finn. “Path planning and obstacle avoidance for autonomous mobile robots: a reviewâ€. In 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Berlin, Heidelberg, 2006

K. Amrit, K. J. Dheerendr, M. Sangheeta. “Path Planning by a Robot is Static Environment using Hybridization of Particle Swarm Optimization and A* Algorithmâ€. International journal of computer Architecture and Mobility, vol. 4, no, 2319-9229.

Q. Yuan-Qing, S. De-Bao S, Ning L, Yi-Gang C. “Path planning for mobile robot using the particle swarm optimization with mutation operatorâ€. In Third International Conference on Machine learning and Cybernetics, Shanghai, 2004.

S. Roland, R.N. Illah, S. Davide. “Introduction to autonomous mobile robotsâ€. MIT-Press, 2004.

T. M. Thi, C. Cosmin, T.T. Duc, D.K. Robin. “Heuristic approaches in robot path planning: A surveyâ€. Robotics and Autonomous Systems 2016; 86: pp 13-28.

C. Howie, M.L. Kevin, H. Seth, K. George, B. Wolfram, E.K. Lydia, T. Sebastian. “Principles of robot motion: theory, algorithms, and implementationsâ€. MIT Press, Principles of robot motion: theory, algorithms, and implementations, 2005.

S. Russel, P. Norvig. “Artificial Intelligence: A Modern Approachâ€, PEARSON, 2009.

M.S. LaValle. “Planning Algorithmsâ€. Cambridge, U.K.: Cambridge University Press, 2006.

J. Wit, C.D. Crane, D. Armstrong. “Autonomous ground vehicle path trackingâ€. Journal of Robotic Systems 2004: 21(8): 439–449.

A. Nash, K. Daniel, S. Koenig, A. Feiner. “Theta*: any-angle path planning on gridsâ€. In 22nd national conference on Artificial intelligence, 2007.

J.P. Laumond. “Robot Motion Planning and Controlâ€. New York, Inc: Springer-Verlag, 1998.

A. Pritchard. Statistical bibliography or bibliometric? Journal of Documentation 1969; 25(4): pp 348-349.

H. Michael, S. Simon, Ken F. “The quantitative crunch: The impact of bibliometric research quality assessment exercises on academic development at small conferences. Campus-Wide Information Systems 2009; 26(3): 149-167.

G. Mao, X. Liu, H. Du, J. Zuo, L. Wang. Way forward for alternative energy research: a bibliometric analysis during 1994-2013. Renew sustain energy review 2015; 48: 276-286.

Q. Yuan-Qing, S. De-Bao, L. Ning, Yi-Gang C. Path planning for mobile robot using the particle swarm optimization with mutation operator. In Third International Conference on Machine learning and Cybernetics, Shanghai, 2004.

P. Raja, S. Pugazhenthi. “Optimal path planning of mobile robots: A review. International journal of physical sciences 2012:7(9): 1314-1320.

S.H. Tang, W. Khaksar, B.N. Ismail, M.K.A. Ariffin. “A review on robot motion planning approachesâ€. Pertanika J. Sci. & Technol 2012: 20(1): 15 – 29.

P. Raja, S. Pugazhenthi. Optimal path planning of mobile robots: a review. International journal of physical sciences 2012; 7(9), pp 1314- 1320.

N. Sariff, N. Buniyamin. “An overview of autonomous mobile robot path planning algorithms. In Student Conference on Research on Development (Scored 2006), 2006.

E. Ahmed, A.A. Hussein, A. Shawki. “Genetic algorithm for dynamic path planningâ€. In CCECE, 2004.

M. Ellips, S. Davoud. “Classic and heuristic approaches in robot motion planning - a chronological reviewâ€. In Proceedings of world academy of science, engineering and technology, 2007.

N. Christoph, Ejan R, Markus G. Generic path planning for real time application. In Computer Graphics International (CGI 04), 2004.

M. Ellips, Davoud S. Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review. World Academy of Science,Engineering and Technology 2007; 29: 101-106.

S. H. Tang, W. Khaksar, Ismail BN, Ariffin MKA. A review on robot motion planning approaches. Pertanika J. Sci. & Technol 2012: 20(1): 15 – 29.

A. Adham, David MWP. Review of classical and heuristic-based navigation and path planning approaches. International Journal of Advancements in Computing Technology 2013; 5(14): 1-15.

S. H. V. Anantha, K.G.Suresh. “A Survey of Autonomous Mobile Robot Path Planning Approaches. In International conference on Recent Innovations is Signal Processing and Embedded Systems (RISE-2017), 2017.

A. Adham, S. epideh . “Particle Swarm Optimization: A Survey. In Louis P. Walters, ed. Applications of Swarm Intelligence. Hauppauge, USA: 2011.

A. Atyabi, PhonAmnuaisuk S, Ho C, Samadzadegan S. Particle Swarm Optimizations: A Critical Review. In Third conference of Information and Knowledge Technology, Ferdowsi University, Iran, 2007.

C. Gordon, Alexander Z. A physically grounded search in a behavior based robot. In Eighth Australian Joint Conference on Artificial Intelligence, Austrailia, 1995.

M. Zacksenhouse, DeFigueiredo RJP, Johnson DH. Neural network architecture for cue-based motion planning. In IEEE International Conference on Decision and Control, 1988.

N. Sadati, J. Taheri. Solving robot motion planning problem using Hopfield neural network in a fuzzified environment. in Proc. IEEE/FUZZ, 2002.

D. Janglova. “Neural networks in mobile robot motionâ€. International Journal of Advance Robotic System 2004; 1: 15-22.

An-Min Z, Zeng-Guang H, Si-Yao F, Min T. Neural networks for mobile robot navigation: A survey. In Advances in Neural Networks, Berlin Heidelberg, Springer, p. 1218-1226, 2006.

A. Zhu. Yang, SX "A Neural Network Approach to Dynamic Task Assignment of Multirobots. EEE Transaction on Neural Networks 2006; 7(5): 1278-1287.

Jian F, MinRui F, ShiWei M. RL-ART2 Neural Network Based Mobile Robot Path Planning Proc. ISDA 2006; 2: 581-585.

L. Yangmin, C. Xin. “Mobile robot navigation using particle swarm optimization and adaptive NN. In Advances in Natural Computation, Lecture Notes in Computer Science, p. 628-631, 2005.

P. Jim, M. Alcherio. Parallel learning in heterogeneous multi-robot swarms. In IEEE Congress on Evolutionary Computation CEC07, Singapore, 2007.

M.K Singh, D.R. Parhi. Intelligent neuron controller for navigation of mobile robot. In International Conference on Advances in Computing, Communication and Control, p. 123–128., 2009.

M.K Singh, D.R. Parhi. Path optimisation of a mobile robot using an artificial neural network controller. International Journal Syst. Sci 2011; 42(1): 107–120.

L.A. Zadeh. “Fuzzy setsâ€. Information and Control 1965; 8: 338-353.

S.H. Tang, Danial N, BabakKarasfi. Application of fuzzy logic in mobile robot navigation. Fuzzy Logic-Controls, Concepts, Theories and Applications, p. 21-36, 2012.

Y. Yupei, Yangmin L. Mobile robot autonomous path planning based on fuzzy logic and filter smoothing in dynamic environment. In World Congress on Intelligent Control and Automation (WCICA), 2016.

Chelsea S, Kelly C. fuzzy logic unmanned air veicle motion planning. Advances in Fuzzy Systems, 2012.

Walker K, Esterline AC. Fuzzy motion planning using the Takagi-Sugeno method Southeast, 2000.

Qingfu Z, Jianyong S, Gaoxi XT, Edward. Evolutionary Algorithms Refining a Heuristic: A Hybrid Method for Shared-Path Protections in WDM Networks Under SRLG Constraints. IEEE Trans. on Systems, Man and Cybernetics, Part B 2007; 37(1): 51-61.

Iraj H, Sadigh SM. Path planning for a mobile robot using fuzzy logic controller tuned by GA. In the International Symposium on Mechatronics and its Applications(ISMA09), UAE, 2009.

Mahdi F, Amirreza K, Mohsen J. Humanoid robot path planning with fuzzy markov decision processes. Journal of Applied Research and Technology 2016; 14(5): 300-310

Wang M, Liu, JNK. Fuzzy logic based robot path planning in unknown environment. In Int. Conf. Machine Learning & Cybernetics, 2005.

Araujo. Prune-able Fuzzy ART Neural Architecture for Robot Map. IEEE Transaction on Neural Networks 2006; 7(5): 1235-1249.

Antonelli G, Chiaverini S, Fusco G. A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking. IEEE Transaction on Fuzzy Systems 2007;15(2): 211-221.

Ellips M., and Davoud S. Classic and Heuristic Approaches in Robot Motion Planning – A Chronological Review. International Journal of Mechanical and Mechatronics Engineering 2007; 1(5): 228-233.

Ellips M., and Davoud S. Multi–objective PSO and NPSO based algorithms for robot path planning. Advance in Electronics and Computer Engineering 2010; 10(4): 69-76.

Yang C, Simon D. A new particle swarm optimization technique. In IEEE International Conference on Systems Engineering, p. 164–169, 2005.

Andrew S, Mirjana J, Ian G. Particle swarm optimization with mutation. In The 2003 Congress on Evolutionary Computation, 2003.

Srinivas P, Roberto B. The gregarious particle swarm optimizer (g-pso). In 8th annual conference on Genetic and evolutionary computation, Washington, USA, 2006.

Asanga R, Saman KH, Harry CW. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on evolutionary computation 2004; 8(3): 240-255.

Yuan-Qing Q, De-Bao S, Ning L, Yi-Gang C. Path planning for mobile robot using the particle swarm optimization with mutation operator. In Third International Conference on Machine learning and Cybernetics, Shanghai, 2004.

Fourie P, Albert G. The particle swarm optimization algorithm in size and shape optimization. Structural and Multidisciplinary Optimization 2002; 23(4): 59-267.

James K, Russell E. Particle swarm optimization. In IEEE International Conference on Neural Networks, 1995.

James K, William MS. Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In IEEE World Congress on Computational Intelligence, 1998.

Jim P, Alcherio M. Multi-robot learning with particle swarm optimization. In International Conference on Autonomous Agents and Multi Agent Systems, Hakodate, 2006.

Jim P, Yizhen Z. Particle swarm optimization for unsupervised robotic learning. In Proceedings of 2005 IEEE in Swarm Intelligence Symposium, 2005.

Yuan-Qing Q, De-Bao S, Ning L, Yi-Gang C. Path planning for mobile robot using the particle swarm optimization with mutation operator. In Third International Conference on Machine learning and Cybernetics, Shanghai, 2004.

Guillaume B, Frédérique Br, Béat H. Multirobot path-planning based on implicit cooperation in a robotic swarm. In second international conference on Autonomous agents, Minneapolis, USA, 1998.

Suranga H. Distributed online evolution for swarm robotics. Autonomous Agents and Multi Agent Systems, 2006

Yuan-Qing Q, De-Bao S, Ning L, Yi-Gang C. Path planning for mobile robot using the particle swarm optimization with mutation operator. In Third International Conference on Machine learning and Cybernetics, Shanghai, 2004.

Hua-Qing M, Jin-Hui Z, Xi-Jing, Z. Obstacle avoidance with multiobjective optimization by PSO in dynamic environment. In Int. Conf. Machine Learning and Cybernetics, 2005.

Saska M, Macas M, Preucil L, Lhotska L. Robot Path Planning using Particle Swarm Optimization of Ferguson Splines. In IEEE/ETFA '06, 2006.

Li W, Yushu L, Hongbin XD, Yuanqing. Obstacle-avoidance Path Planning for Soccer Robots Using Particle Swarm Optimization. In IEEE Int. Conf. on Rob. and Biomimetics (ROBIO '06)., 2006.

Xin CL, Yangmin. Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization. In IEEE on Mechatronics and Aut., 2006.

Chengyu H, Xiangning W, Qingzhong L, Yongji W. Autonomous robot path planning based on swarm intelligence and stream functions. In ICES2007, 2007.

David MP, Adham AA. Cooperative Area Extension of PSO Transfer Learning vs. Uncertainty in a simulated Swarm Robotics. In 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Island, 2013.

Adham A, Somnuk P, Chin KH. Navigating a robotic swarm in an uncharted 2D landscape. Applied Soft Computing 2010: 149-169.

Adham A, Somnuk P, Chin KH. Applying Area Extension PSO in Robotic Swarm. Intell Robot System 2010; 58(3-4): 253-285.

Adham A, David MWP. The Use of Area Extended Particle Swarm Optimization (AEPSO) in Swarm Robotics. In Eleventh International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), 2011.

Zhang L, Chen J, Deng F. Aircraft trajectory planning for improving visionbased target geolocation performance. In 13th IEEE International Conference Control & Automation (ICCA), 2017.

Hafez AT, Kamel MA, Jardin PT, Givigi SN. Task assignment/trajectory planning for unmanned vehicles via hflc and pso. In 2017 International Conference on Unmanned Aircraft Systems (ICUAS), 2017.

Ren S, Chen Y, Xiong L, Chen Z, Chen M. Path planning for the marsupial double-uavs system in air-ground collaborative application. In 37th Chinese Control Conference (CCC), 2018.

Chen J, Ye F, Li Y. Travelling salesman problem for uav path planning with two parallel optimization algorithms. In Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), 2017.

Li G, Chou W. Path planning for mobile robot using self-adaptive learning particle swarm optimization. Science China Information Sciences 2017; 61(5): 052204.

Das PK, Behera HS, Panigrahi BK. A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation 2016; 28: 14-28.

Jean-Claude L. Robot motion planning. Kluwer International Series in Engineering, London, 1991.

Hao M, Yantao T, Linan Z. A hybrid ant colony optimization algorithm for path planning of robot in dynamic environment.

International Journal of Information Technology 2006; 12(3): 78-88.

Xiaoping F, Xiong L, Sheng Y, Shengyue Y, Heng Z. Optimal path planning for mobile robots based on intensified ant colony optimization algorithm. In IEEE on Rob. Intel. Sys. & Sig. Processing IEEE, 2003.

Ying-Tung C, Cheng-Long C, Cheng-Chih H. Ant colony optimization for best path planning," in IEEE/ISCIT'04, 2004.

Mohamad MM, Dunnigan MW, Taylor NK. Ant Colony Robot Motion Planning. In Int. Conf. on EUROCON'05, 2005.

Mohamad MM, Taylor NK, Dunnigan MW. Articulated Robot Motion Planning Using Ant Colony. In IEEE Int. Conf. Optimization. Intel. Sys, 2006.

Francois L, Christophe G. Hybrid Solving Technique for Vehicle Planning with Communication Constraints. In Military Communications Conference, MILCOM, 2013.

Qi Z, Jiachen M, Qiang L. Path Planning based Quadtree Representation for Mobile Robot Using Hybrid-Simulated Annealing and Ant Colony Optimization Algorithm. In 10th World Congress on Intelligent Control and automation, 2012.

Joon-Woo L, Byoung-Suk C, Kyoung-Taik P, Ju-Jang L. Comparison between Heterogeneous Ant Colony Optimization Algorithm and Genetic Algorithm for Global Path Planning of Mobile Robot, In IEEE International Symposium on Industrial Electronics (ISIE), 2011.

James M, Daniel Y. Dynamic task assignment in robot swarms. In Robotics: Science and Systems, Cambridge, USA, 2005.

Chunxue S, Yingyong B, Jianghui L. Mobile robot path planning in three-dimensional environment based on aco-pso hybrid algorithm. In 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Xian, China, 2008

Leena N, Saju KK. A survey on path planning techniques for autonomous mobile robots. In International Conference on Advances in Engineering & Technology, 2014.

Konar A. Computational intelligence principles, techniques and applications, Springer Verlag, 2005.

ParkerJK, Khoogar AR, Goldberg DE. Inverse kinematics of redundant robots using genetic algorithms. In IEEE ICRA, 1989.

Davidor Y. Robot programming with a genetic algorithm. In IEEE Int. Conf. on Computer Sys. & Soft. Eng, 1990.

Hamdan M, El-Hawary ME. A novel genetic algorithm searching approach for dynamic constrained multicast routing. In IEEE/CCECE, 2003.

Amin G, Saeed S, Ali N. Using genetic algorithm for a mobile robot path planning. In International Conference on Future Computer and Communiction, 2009.

Riaan B, Andries PE, Bergh F. Scalability of niche pso. In 2003 IEEE Swarm Intelligence Symposium, 2003.

Yong KH, Pang CC. A heuristic and complete planner for the classical mover’s problem. In 1995 IEEE International Conference on Robotics and Automation, 1995.

Nasser S, Javid T. Genetic algorithm in robot path planning problem in crisp and fuzzified environments. In IEEE ICIT02, Bangkok, Thailand, 2002.

Ergezer H, Leblebicioglu K. Path Planning for UAVs for Maximum Information Collection. IEEE Transactin on Aearospace and Electronic Systems 2013; 41(9):502-520.

Ji X, Xie H, Zhou L, Jia S. Flight path planning based on an improved genetic algorithm. In Third International Conference on Intelligent System Design and Engineering Applications, 2013.

Zein-Sabatto S, Ramakrishnan R. Multiple path planning for a group of mobile robots in a 3D environment using genetic algorithms. Southeast, 2002.

Wilson LA, Moore MD, Picarazzi JP, Miquel SDS. Parallel genetic algorithm for search and constrained multi-objective optimization. In Parallel and Distributed Processing Symp, 2004.

Qing T, Xinhai X, Sijiang Z. Yingchun L. Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm. In Proc. HIS'06, 2006.

Chi-Tsun C, Kia F, Henry L, Chi K.T. A Genetic Algorithm-Inspired UUV Path Planner Based on Dynamic Programming. IEEE Trans on Systems, Man, and Cyberneticspart C: applications and reviews 2012; 42(6): 1128-1134.

Yanrong H, Simon XY. A knowledge based genetic algorithm for path planning of a mobile robot. In 2004 IEEE International Conference on Robotic & Automation, 2004.

Krzysztof T, Zbigniew M, Jing X. Adding memory to the evolutionary planner navigator. In IEEE International Conference on Evolutionary Computation, 1997.

Dijkstra WE. A note on two problems in connexion with graphs. Numerische Mathematik, 1959; 1: 269–271.

Verscheure L, Peyrodie L, Makni N, Betrouni N, Maouche S, Vermandel M. Dijkstra’s algorithm applied to 3D skeletonization of the brain vascular tree: evaluation and application to symbolic. Iin Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC ’10), Buenos Aires, Argentina, 2010.

Wan M, Liang Z, Ke Q, Hong L, Bitter I, Kaufman A. Automatic centerline extraction for virtual colonoscopy. IEEE Transactions on Medical Imaging 2002; 21(12):1450–1460.

Musliman IA, Rahman AA, aCoors V. Implementing 3d network analysis in 3d-gis. In 21st ISPRS Congress Silk Road for Information from Imagery, China, 2008.

Liang Y, Juntong Q, Dalei S, Jizhong X Jianda H, and Yong X. Survey of Robot 3D Path Planning Algorithms. Journal of Control Science and Engineering 2016: 1-22.

Hart PE, Nilsson NJ, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics. 1968; 1(2): 100–107.

Carreras M, Galceran E. A survey on coverage path planning for robotics. Robotics and Autonomous Systems 2013; 61(12):.1258–1276.

Yan F, Liu YS, Xiao JZ. Path planning in complex 3D environments using a probabilistic roadmap method. International Journal of Automation and Computing 2013; 10(6): 525–533.

Zhuo G, Niu L. An improved real 3d a∗ algorithm for difficult path finding situation. In Proceeding of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, China, 2008.

Likhachev M, Koenig S. Improved fast replanning for robot navigation in unknown terrain. In International Conference on Robotics and Automation, Washington,Wash, USA, 2002.

Ragno RJ, Williams BC. Conflict-directed A∗ and its role in model-based embedded systems. Discrete Applied Mathematics 2007; 155(12): 1562–1595.

Nash A, Daniel K, Koenig S, Felner A. Theta∗: Anyangle path planning on grids. In National Conference on Artificial Intelligence, Vancouver, Canada, 2007.

Chen T, Zhang G, Hu X, Xiao J. Unmanned aerial vehicle route planning method based on a star algorithm. In 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018.

He Z, Zhao L. The comparison of four uav path planning algorithms based on geometry search algorithm. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2017 9th International Conference, 2017.

De Filippis L, Guglieri G, Quagliotti F. Path planning strategies for UAVS in 3D environments. Journal of Intelligent and Robotic Systems 2012; 65(1-4): 247–264.

Nash A, Koenig S and ToveyC. Lazy theta∗: any-angle path planning and path length analysis in 3D. In Third Annual Symposium on Combinatorial Search, Atlanta, Ga, USA, 2010.

Ferguson D, Likhachev M. Planning long dynamically feasible maneuvers for autonomous vehicles. The International Journal of Robotics Research 2009; 28(8): 933–945.

Benders S, Schopferer S. A line-graph path planner for performance constrained fixed-wing uavs in wind fields. In Unmanned Aircraft Systems (ICUAS),, 2017.

Li J, Deng G, Luo C, Lin Q, Yan Q, Ming Z. hybrid path planning method in unmanned air/ground vehicle (uav/ugv) cooperative systems. IEEE Transaction on Vehicular Technology 2016; 65(12):. 9585–9596.

Chengjun Z, Xiuyun M. Spare a∗ search approach for uav route planning. In Unmanned Systems (ICUS), 2017 IEEE International Conference, 2017.

Stentz A. Optimal and efficient path planning for partiallyknown environment. In IEEE International Conference on Robotics and Automation, San Diego, Calif, USA, 1994.

Stentz A. The focussed d-star algorithm for real-time replanning. In International Joint Conference on AI, Montreal, Canada, 1995.

Ramalingam G, Thomas R. An Incremental Algorithm for a Generalization of the Shortest-Path Problem. Journal of Algorithms 1996; 21(2):267-305.

Likhachev M, Koenig S. D* Lite. In 18th national conference on Artificial intelligence., Menlo Park, CA, USA, 2002.

Manley K. Pathfinding: From A* to LPA. Ph.D. dissertation, University, University of Minnesota, 2003.

M. L. a. A. S. D. Ferguson. A Guide to Heuristicbased Path Planning. In Proceedings of the Workshop on Planning under Uncertainty for Autonomous Systems at The International Conference on Automated Planning and Scheduling, 2005.

Sreenatha GA, Sobers LXF, Matthew G. Challenges, Path-Planning Modules for Autonomous Vehicles: Current Status and. In Int’l Conf. on Advanced Mechantronics, Intelligent Manufactur, and Industrial Application, Surabaya, Indonesia, 2015.

Ellips M., and Davoud S. Classic and Heuristic Approaches in Robot Motion Planning – A Chronological Review. International Journal of Mechanical and Mechatronics Engineering 2007; 1(5): 228-233.

Prahlad V, Tong HL, Liu X. Application of evolutionary artificial potential field in robot soccer system. In Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2001.

Omer C, Ibrahim Z, Guray Y. Establishing Obstacle and Collision Free Communication Relay for UAVs with Artificial Potential Fields. Journal of Intelligent Robotic Systems 2013; 69(1-4) vol. 69 361-372.

Mohammad AJ, Mohammad HG, Eyad AF. Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Computing 2012; 16(1):153-164.

Saravanakumar S, Thomas G, Asokan T. Obstacle Avoidance Using Multi-Point Potential Field Approach for an Underactuated Flat-Fish Type AUV in Dynamic Environment. Trends in Intelligent Robotics, Automation, and Manufacturing, Communications in Computer and Information Science 2012; 330: 20-27.

Seda M. Roadmap method vs. cell decomposition in robot motion planning. In Proceedings of 6th WSEAS international conf on signal processing, Robotics and automation, Greece, 2007.

Luis V, Herbert GT. Hybrid Potential Field Based Control of Di erential Drive Mobile Robots. Journal of Intelligent Robotic Systems 2012; 68(3-4): 307-322.

Song P. A potential field based approach to multirobot manipulation general robotics. In IEEE International Conference on Robotics and Automation (ICRA02), 2002.

Elon R, Daniel EK. Exact robot navigation using artificial potential functions. IEEE Trans. On Robotics and Automation 1992; 8(5): 501-518.

Yunfeng W, Gregory SC. A new potential field method for robot path planning. In Proceeding of the 2000 IEEE International Conference on Robotics and Automation, 2000.

Dai J, Wang Y, Wang C, Ying J, Zhai J. Research on hierarchical potential field method of path planning for uav. In 2nd IEEE Advanced, 2018.

Bai W, Wu X, Xie Y, Wang Y, Zhao H, Chen K, Li Y, Hao Y. A cooperative route planning method for multi-uavs based-on the fusion of artificial potential field and b-spline interpolation," in 37th Chinese Control Conference (CCC), 2018.

Budiyanto A, Cahyadi A, Adji TB, Wahyunggoro O. Uav obstacle avoidance using potential field under dynamic environment. In Control, Electronics, Renewable Energy and Communications (ICCEREC), 2015.

Mac TT, Copot C, Hernandez A, De Keyser R. Improved potential field method for unknown obstacle avoidance using uav in indoor environment, in: Applied Machine Intelligence and Informatics (SAMI). In 4th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2016.

Mansard N, DelPrete A, Geisert M, Tonneau S, Stasse O. Using a memory of motion to efficiently warm-start a nonlinear predictive controller. In IEEE International Conference on Robotics and Automation (ICRA), 2018.

Roland S, IllahR N. Introduction to autonomous mobile robots, heMIT Press Cambridge, 2004.

Priyadarshi B, Marina LG. Roadmap-based path planning using the Voronoi diagram for a clearance-based shortest path. IEEE Robotics and Automation Magazine 2008:58-66.

Zhi Y, Nicolas J, Arab AC. ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning. Intelligent Autonomous Systems 12, Advances in Intelligent Systems and Computing 2013; 193: 843-851.

Mika TR, Martti J. A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots. International Journal of Automation and Computing 2012; 9(2): 155-164.

Ali NN, Alireza D, Philippe P. Multi-Agent Area Coverage Using a Single Query Roadmap: A Swarm Intelligence Approach. Advances in Practical Multi-Agent Systems, Studies in Computational Intelligence 2011; 325: 95-112.

Mika TR A Connectivity-Based Method for Enhancing Sampling in Probabilistic Roadmap Planners. Journal of Intelligent Robotic Systems 2011; 64(2): 161-178, 2011.

Yuandong Y, Oliver B. Elastic roadmaps motion generation for autonomous mobile. Auton Robot 2010; 28: 113130.

LaValle MS. Rapidly-exploring random trees a new tool for path planning. Iowa State University, USA.

Chitta S, Sucan I, Cousins S,. Moveit![ros topics]," IEEE Robotics & Automation Magazine 2012; 19(1): 18–19.

Frazzoli E, Karaman S. Optimal kinodynamic motion planning using incremental sampling-based methods. In 49th IEEE Conference on Decision and Control, Atlanta, Ga, USA, 2010.

Kala R. Rapidly exploring randomgraphs:motion planning of multiple mobile robots. Advanced Robotics 2013; 27(14): 1113–1122.

Fedorenko R, Gabdullin A. Global ugv path planning on point cloud maps created by uav. In 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE), 2018.

D. Zhang, Y. Xu, Yao X. An improved path planning algorithm for unmanned aerial vehicle based on rrt-connect. In 37th Chinese Control Conference (CCC), 2018.

Wen N, Zhao L, Su X, Ma P. Uav online path planning algorithm in a low altitude dangerous environment. J. Autom. Sin 2015; 2: 173–185.

Lin Y, Saripalli S. Sampling-based path planning for uav collision avoidance. IEEE Trans. Intell. Transp. Syst 2017; 18(11): 3179–3192.

Yang H, Jia Q, Zhang W. An environmental potential field based rrt algorithm for uav path planning. In 37th Chinese Control Conference (CCC), 2018.

Zu W, Fan G, Gao Y, Ma Y, Zhang H, Zeng H. Multi-uavs cooperative path planning method based on improved rrt algorithm. In 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018.

Sun Q, Li M, Wang T, Zhao C. Uav path planning based on improved rapidly exploring. In 2018 Chinese Control and Decision Conference (CCDC), 2018.

Goerzen C, Kong Z, Mettler B. Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance. Journal of Intelligent and Robotic Systems 2010; 57(1-4): 65-100.

Nakju LD, Chanki K, Wan KC. A practical path planner for the robotic vacuum cleaner in rectilinear environments. IEEE Transactions on Consumer Electronics 2007; 53: 519-527.

Lingelbach F. Path planning using probabilistic cell decomposition. In Int. Conf. on Robotics and Automation, 2004.

Chenghui C, Silvia F. Information-driven sensor path planning by approximate cell decomposition. IEEE Transactions on Systems, Man and Cybernetics, Part B(Cybernetics) 2009;39(3).

Greg F, Ashleigh S and Silvia F. A Model-based Cell Decomposition Approach to On-line Pursuit-Evasion Path Planning and the Video Game Ms. Pac-Man. In IEEE Conference on Computational Intelligence and Games (CIG), 2012.

Rosell J, Iniguez P. Path planning using harmonic functions and probabilistic cell decomposition. In IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005.

Jin-xue Z. Robot Real-Time Motion Planning and Collision Avoidance in Dynamically Changing Environments. Emerging Research in Artificial Intelligence and Computational Intelligence, Communications in Computer and Information Science 2011;237: 325-334.

Pang CC, Yong KH. Sandros: a motion planner with performance proportional to task difficulty. In IEEE International Conference on Robotics and Automation, 1992.

Amitava C, Anjan R,Ni rmal NS. Vision-Based Mobile Robot Navigation Using Subgoals. Vision Based Autonomous Robot Navigation, Studies in Computational Intelligence 2013; 455: 47-82.

Soheil K, Shahram P. Multi-robot, dynamic task allocation: a case study. Intelligent Service Robotics, 2013.

Hoey D, Shamos MI. Closest-point problems. In 16th Annual Symposium on Foundations of Computer Science, Berkeley, Calif, USA, 1975.

Luchnikov VA, N.N Medvede, Oger L, Troadec JP. Voronoi-Delaunay analysis of voids in systems of nonspherical particles. Physical Review E 1999; 59(6):7205–7212.

Cho Y, Kim D, Kim DS. Topology representation for the voronoi diagram of 3d spheres. International Journal of CAD/CAM 2005; 5(1):59–68.

Kim DS, Kim D. Region-expansion for the Voronoi diagram of 3D sphere. CAD Computer Aided Design 2006; 38(5): 417–430.

Kim DS, Cho Y, Kim D, Kim, S Bhak J, Lee SH. Euclidean voronoi diagrams of 3D spheres and applications to protein structure analysis. Japan Journal of Industrial and Applied Mathematics 2005; 22(2): 251–265.

Sharifi F, Zhang Y, Gordon BW. Voronoi-based coverage control for multi-quadrotor UAVs. In ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, USA, 2011.

Amin-Naseri MRN, Masehian E. voronoi diagramvisibility graph-potencial field compound algorith for robot path planning. Journal of Robotic Systems 2004;21(6); 275-300.

Shen Z, Cheng X, Zhou S, Tang XM, Wang H. A dynamic airspace planning framework with ads-b tracks for manned and unmanned aircraft at lowaltitude sharing airspace. In Digital Avionics Systems Conference (DASC), 2017.

Mohammed HA, Musa M, Tang HT. Path planning of mobile robot for autonomous navigation of road roundabout intersection. International Journal of Mechanics 2012; 6(4): 203-211

Mohammed HA, Musa M, Tang HH. Implementation of laser simulator search graph approach for detection and path planning in roundabout environments. WSEAS Transactions on Signal Processing 2014; 10(1): 106-115.

Mohammed HA, Wan Azhar BW. Yusoff, ZB. Hamedon, ZB. Yusssof M, Musa M. Global mobile robot path planning using laser simulator. In 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), 2016.

Mohammed AHA, Musa M. Path Planning and Control of Mobile Robot in Road Environments Using Sensor Fusion and Active Force Control. IEEE Transactions on Vehicular Technology 2019; 68(3): 2176 – 2195.

Mohammed AHA, Musa M, Wan Azhar B. Yussof, ZH, Zulkili, Yussof MB. An Intelligent Robust Control of Wheeled Mobile Robot in Restricted Environment. International Journal of Control Systems and Robotics 2017;2: 6-11

Mohammed AHA, Musa M. Online Mapping-Based Navigation System for Wheeled Mobile Robot in Road Following and Roundabout. In Applications of Mobile Robots, Efren Gorrostieta Hurtado, IntechOpen, 2018

Koskinen J, Isohanni M, Paajala H, Jääskeläinen E, Nieminen P, Koponen H, Tienari P, Miettunen J. How to use bibliometric methods in evaluation of scientific research? An example from Finnish schizophrenia research.. Nord. J. Psychiatry 2008; 62: 136–143.

McKerlich R., Ives C., McGreal R.. Comparing bibliometric statistics obtained from the web of sciences and scopus. Int.Rev.Res.Open Distance Learn 2013a;14:90–103.

Aisha M, Mohammed AHA, Ibrahim HS. ANSYS –A Bibliometric study. In 10th International Conference of Materials Processing and Characterization. India, 2020

Mongeon P., Paul-Hus A.. The journal coverage of web of science and scopus: a comparative analysis.Scientometrics 2016; 106: 213–228.

Mingers J., Leydesdorff L. A review of theory and practice in scientometricsEur. J. Oper. Res 2015; 246: 1–19.

Dehdarirad T., Villarroya A., Barrios M. Research on women in science and higher education: a bibliometric analysis. Scientometrics 2015; 103: 795–812..

Fahimnia B., Sarkis J., Davarzani H. Green supply chain management: a review and bibliometric analysis. Int. J. Prod. Econ 2015; 162: 101–114.

McKerlich R., Ives C., McGreal R. Comparing keywords plus of WOS and author keywords: a case study of patient adherence research. Int. Rev. Res. Open Distance Learn 2013b; 14: 90–103.

Chadegani AA., Salehi H, Yunus MM, Farhadi H, Fooladi M, Farhadi ., Ebrahim NA. A comparison between two main academic literature collections: web of science and scopus databases. Asian Soc. Sci 2013; 9: 18–26.

Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010; 84: 523–538.

Sun J, Wang MH., Ho YS. A historical review and bibliometric analysis of research on estuary pollution. Mar. Pollution Bulletin 2012;64: 13-21.

Wu X, Chen X, Zhan F B, Hong S. Global research trends in landslides during 1991 – 2014: a bibliometric analysis. Landslides 2015; 12: 215–1226.

Downloads

Published

2020-12-25

How to Cite

muhammad, A., Ali, M. A. H., & Shanono, I. H. (2020). Path Planning Methods for Mobile Robots: A systematic and Bibliometric review. ELEKTRIKA- Journal of Electrical Engineering, 19(3), 14–34. https://doi.org/10.11113/elektrika.v19n3.225

Issue

Section

Articles