BEHAVIOUR-DEFINED NAVIGATION FRAMEWORK FOR DYNAMICAL OBSTACLE AVOIDANCE IN MULTI-ROBOT SYSTEMS CONSISTING OF HOLONOMIC ROBOTS, 379-390.

Tahniat Khayyam, S.G. Ponnambalam, Mukund Nilakantan Janardhanan, and Izabela Ewa Nielsen

References

  1. [1] E. Di Mario and A. Martinoli, Distributed particle swarmoptimization for limited-time adaptation with real robots,Robotica, 32, 2014, 193–208.
  2. [2] M. Yuan, Y. Jiang, X. Hua, B. Wang, and Y. Shen, A real-timeimmune planning algorithm incorporating a specific immunemechanism for multi-robots in complex environments, Proc.of the Institution of Mechanical Engineers, Part I: Journal ofSystems and Control Engineering, 2017, 29–42.
  3. [3] M. Nazarahari, E. Khanmirza, and S. Doostie, Multi-objectivemulti-robot path planning in continuous environment using anenhanced genetic algorithm, Expert Systems with Applications,115, 2019, 106–120.
  4. [4] H. Xue and H.-X. Ma, Swarm intelligence based dynamic obsta-cle avoidance for mobile robots under unknown environmentusing WSN, Journal of Central South University of Technology,15, 2008, 860–868.
  5. [5] A.V. Savkin and C.A. Wang, Simple biologically inspiredalgorithm for collision-free navigation of a unicycle-like robotin dynamic environments with moving obstacles, Robotica, 31,2013, 993–1001.
  6. [6] Y. Zhaofeng and Z. Ruizhe, Path planning of multi-robot cooperation for avoiding obstacle based on improvedartificial potential field method, Sensors & Transducers, 165,2014, 221.
  7. [7] J.B. Mbede, X. Huang, and M. Wang, Fuzzy motion planningamong dynamic obstacles using artificial potential fields forrobot manipulators, Robotics and Autonomous Systems, 32,2000, 61–72.
  8. [8] S.S. Ge and Y.J Cui, Dynamic motion planning for mobilerobots using potential field method, Autonomous Robots, 13,2002, 207–222.
  9. [9] U. Orozco-Rosas, O. Montiel, and R. Sep´ulveda, Pseudo-bacterial potential field based path planner for autonomousmobile robot navigation, International Journal of AdvancedRobotic Systems, 12, 2015, 81.
  10. [10] S. Cifuentes, J.M. Gir´on-Sierra, and J. Jim´enez, Virtualfields and behavior blending for the coordinated navigation ofrobot teams: Some experimental results, Expert Systems WithApplications, 42, 2015, 4778–4796.
  11. [11] Z. Pan, D. Wang, H. Deng, and K.A. Li, Virtual spring methodfor the multi-robot path planning and formation control,International Journal of Control, Automation and Systems,17, 2019, 1272–1282.
  12. [12] T.P. Nascimento, A.G. Concei¸cao, and A.P. Moreira, Multi-robot nonlinear model predictive formation control: the obstacleavoidance problem, Robotica, 34, 2016, 549–567.
  13. [13] Y. Li, J. Gao, X. Su, and J. Zhao, Cooperation control ofmultiple miniature robots in unknown obstacle environment,Proceedings of the Institution of Mechanical Engineers, PartI: Journal of Systems and Control Engineering, 229, 2015,202–214.
  14. [14] Y. Dai, Y. Kim, S. Wee, D. Lee, and S. Lee, A switchingformation strategy for obstacle avoidance of a multi-robotsystem based on robot priority model, ISA Transactions, 56,2015, 123–134.
  15. [15] C. De La Cruz and R. Carelli, Dynamic model basedformation control and obstacle avoidance of multi-robotsystems, Robotica, 26, 2008, 345–356.
  16. [16] W.-B. Xu, X.-B. Chen, J. Zhao, and T.-Y. Huang, Adecentralized method using artificial moments for multi-robotpath-planning, International Journal of Advanced RoboticSystems, 10, 2013, 24.
  17. [17] H.-X. Wei, Q. Mao, Y. Guan, and Y.-D. Li, A centroidalVoronoi tessellation based intelligent control algorithm for theself-assembly path planning of swarm robots, Expert SystemsWith Applications, 85, 2017, 261–269.
  18. [18] C.G. Cena, P.F. Cardenas, R.S. Pazmino, L. Puglisi, and R.A.Santonja, A cooperative multi-agent robotics system: Designand modelling, Expert Systems With Applications, 40, 2013,4737–4748.
  19. [19] A.V. Savkin and C. Wang, Seeking a path through thecrowd: Robot navigation in unknown dynamic environmentswith moving obstacles based on an integrated environmentrepresentation, Robotics and Autonomous Systems, 62, 2014,1568–1580.
  20. [20] G. Divya Vani, S.R. Karumuri, and M. Chinnaiah, Hardwareschemes for autonomous navigation of cooperative-type multi-robot in indoor environment, Journal of The Institution ofEngineers (India): Series B, 103, 2021, 1–12.
  21. [21] S. Wang, X. Hu, J. Xiao, and T. Chen, Repulsion-oriented reciprocal collision avoidance for multiple mobilerobots, Journal of Intelligent & Robotic Systems, 104, 2022,1–21.
  22. [22] W. Pang, D. Zhu, and S.X. Yang, A novel time-varyingformation obstacle avoidance algorithm for multiple AUVs,International Journal of Robotics and Automation, 38(3), 2023,194–207.
  23. [23] M. Duguleana and G. Mogan, Neural networks basedreinforcement learning for mobile robots obstacleavoidance, Expert Systems With Applications, 62, 2016,104–115.388
  24. [24] T. Fan, P. Long, W. Liu, and J. Pan, Distributed multi-robot collision avoidance via deep reinforcement learning fornavigation in complex scenarios, The International Journal ofRobotics Research, 39, 2020, 856–892.
  25. [25] K. Charalampous, I. Kostavelis, and A. Gasteratos, Robotnavigation in large-scale social maps: An action recognitionapproach, Expert Systems with Applications, 66, 2016, 261–273.
  26. [26] P. Fiorini and Z. Shiller, Motion planning in dynamicenvironments using velocity obstacles, The InternationalJournal of Robotics Research, 17, 1998, 760–772.
  27. [27] Wu, and J.P. How, Guaranteed infinite horizon avoidance ofunpredictable, dynamically constrained obstacles, AutonomousRobots, 32, 2012, 227–242.
  28. [28] M. Otte and E. Frazzoli, RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning,The International Journal of Robotics Research, 35, 2016,797–822.
  29. [29] G.S. Aoude, B.D. Luders, J.M. Joseph, N. Roy, and J.P.How, Probabilistically safe motion planning to avoid dynamicobstacles with uncertain motion patterns, Autonomous Robots,35, 2013, 51–76.
  30. [30] M. Shahriari and M. Biglarbegian, Toward safer navigationof heterogeneous mobile robots in distributed scheme: Anovel time-to-collision-based method, IEEE Transactions onCybernetics, 52(9), 2021, 9302–9315.
  31. [31] A.S. Lafmejani and S. Berman, Nonlinear MPC for collision-free and deadlock-free navigation of multiple nonholonomicmobile robots, Robotics and Autonomous Systems, 141, 2021,103774.
  32. [32] H. Zhu, B. Brito, and J. Alonso-Mora, Decentralizedprobabilistic multi-robot collision avoidance using buffereduncertainty-aware Voronoi cells, Autonomous Robots, 46, 2022,1–20.
  33. [33] X. Li, D. Zhu, B. Sun, Q. Chen, W. Gan, and Z. Li, Formationtracking for a multi-AUV system based on an adaptive sliding-mode method in the water flow environment, InternationalJournal of Robotics and Automation, 38(5), 2023, 352–366.
  34. [34] E.S. Moghaddam, M.G. Saryazdi, and A. Taghvaeipour,Trajectory optimization of a spot-welding robot in the jointand cartesian spaces, International Journal of Robotics andAutomation, 38(2), 2023, 109–125.
  35. [35] E.A. Padilla-Garcia, A. Rodriguez-Angeles, J.R. Resendiz, andC.A. Cruz-Villar, Concurrent optimization for selection andcontrol of AC servomotors on the powertrain of industrialrobots, IEEE Access, 6, 2018, 27923–27938.
  36. [36] F. Belkhouche, Reactive path planning in a dynamicenvironment, IEEE Transactions on Robotics, 25, 2009,902–911.
  37. [37] V. Sezer and M. Gokasan, A novel obstacle avoidance algorithm:“Follow the Gap Method”, Robotics and Autonomous Systems,60, 2012, 1123–1134.
  38. [38] W. Mathworld, Circle-circle intersection, Accessed on 15September 2023. Available Online: https://mathworld.wolfram.com/Circle-CircleIntersection.html

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