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Robot Path Planning–Prediction: A Multidisciplinary Platform: A Survey

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Data Science and Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 132))

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Abstract

In recent times, there is an impressive progress in the field of automation and robotics. Google driverless car or intelligent Unmanned Air Vehicle (UAV) is the latest in the research works aiming for a high degree of autonomy. In this research field of automation and robotics, there is a mandatory requirement of continuously improving path planning algorithms. Path planners aim in finding an optimal and collision-free path in the work environment. The purpose of path planning is to find a kinematical optimal path with the least time complexity as well as model the environment completely. In this paper, we discuss the most successful robot path planning algorithms which have been developed in recent years from different field of science, making it a multidisciplinary approach and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots and underwater robots. The algorithms are analysed from an optimality and completeness area perspective. In our study, we have included the Dynamic Programming Planning approach also which none of the review papers have covered.

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References

  1. Choset HM (2005) Principles of robot motion: theory, algorithms, and implementations, MIT Press

    Google Scholar 

  2. LaValle SM (2006) Planning algorithms, Cambridge University Press

    Google Scholar 

  3. Sebbane YB (2012) Lighter than air robots: guidance and control of autonomous airships, vol 58. Springer

    Google Scholar 

  4. Russell S, Norvig P (2003) Artificial intelligence, a modern approach. Pearson Education, London, UK

    MATH  Google Scholar 

  5. Visvanathan R, Mamduh SM, Kamarudin K, Yeon ASA, Zakaria A, Shakaff AYM, Kamarudin LM, Saad FSA (2015) Mobile robot localization system using multiple ceiling mounted cameras, IEEE Sensors, 1–4

    Google Scholar 

  6. Saito T, Kuroda Y (2013) Mobile robot localization by gps and sequential appearance-based place recognition. In: IEEE/SICE international symposium on system integration (SII), 25–30

    Google Scholar 

  7. Forouher D, Große Besselmann M, Maehle E (2016) Sensor fusion of depth camera and ultrasound data for obstacle detection and robot navigation. In: 14th international conference on control, automation, robotics and vision (ICARCV), pp 1–6

    Google Scholar 

  8. Baglivo L, Bellomo N, Miori G, Marcuzzi E, Pertile M, De Cecco M (2008) An object localization and reaching method for wheeled mobile robots using laser range finder. In: 4th International IEEE conference intelligent systems, IS’08, vol 1, pp 5–6

    Google Scholar 

  9. Nayl T, Mohammed MQ, Muhamed SQ (2017) Obstacles avoidance for an articulated robot using modified smooth path planning. In: International conference on computer and applications (ICCA), pp 185–189

    Google Scholar 

  10. Uriol R, Moran A (2017) Mobile robot path planning in complex environments using ant colony optimization algorithm. In: 3rd international conference on control, automation and robotics (ICCAR), pp 15–21

    Google Scholar 

  11. Dewangan RK, Shukla A, Wilfred Godfrey W (2017) Survey on prioritized multi robot path planning. In: IEEE international conference on smart technologies and management for computing (ICSTM), pp 423–428

    Google Scholar 

  12. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobilerobots. Int J Rob Res 5:90–98

    Article  Google Scholar 

  13. Karaman S, Frazzoli E (2010) Optimal kino dynamic motion planning using incremental sampling-based methods. In: Proceedings of the 49th IEEE conference on decision and control (CDC’10), pp 7681–7687

    Google Scholar 

  14. Aleksandra F, Kenneth O, Oscar R, Anthony F, Lydia T, Marek F, James D (2018) PRM-RL: long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning, pp 5113–5120

    Google Scholar 

  15. LaValle SM, Rapidly-exploring random trees a new tool for path planning. Tech Rep, 98–11

    Google Scholar 

  16. Carpin S, Pillonetto G (2005) Merging the adaptive random walks planner with the randomized potential field planner. In: Proceedings of the fifth international workshop on robot motion and control, pp 151–156

    Google Scholar 

  17. Dijkstra EW (1959) A note on two problems in connexion with graphs. Num Math 1:269–271

    Article  MathSciNet  Google Scholar 

  18. Hart P, Nilsson N, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Sys Sci Cyber 4:100–107

    Article  Google Scholar 

  19. Chamseddine A, Zhang Y, Rabbath CA, Join C, Theilliol D (2012) Flatness-based trajectory planning/replanning fora quadrotor unmanned aerial vehicle. IEEE Trans Aeros Elec Sys 48:2832–2848

    Article  Google Scholar 

  20. Yue R, Xiao J, Joseph SL, Wang S (2009) Modeling and path planning of the city-climber robot part II: 3D path planning using mixed integer linear programming. In: Proceedings of the IEEE international conference on robotics and biomimetics, pp 2391–2396

    Google Scholar 

  21. Aghababa MP (2012) 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Appl Ocean Res 38:48–62

    Article  Google Scholar 

  22. Yang SX, Luo C (2004) A neural network approach to complete coverage path planning. In: IEEE transactions on systems, man, and cybernetics, part b: cybernetics, vol 34, no 1, pp 718–725

    Google Scholar 

  23. Ken A, Ian MM (2009) Efficient dynamic programming for optimal multi-location robot rendezvous. In: Proceedings of the IEEE conference on decision and control, pp 2794–2799

    Google Scholar 

  24. Bakker B, Zoran Z, Krose B (2005) Hierarchical dynamic programming for robot path planning. In: IEEE transactions on pattern analysis and machine intelligence—PAMI, pp 2756–2761

    Google Scholar 

  25. Willms AR, Yang SX (2006) An efficient dynamic system for real-time robot-path planning. In: IEEE Trans Sys Man Cyber—Part B Cyber 36(4):755–766

    Google Scholar 

  26. Kala R, Tiwar R (2012) Robot path planning using dynamic programming with accelerating nodes, Paladyn. J Behav Robot 3(1):23–34

    Google Scholar 

  27. Georgios C, Jeff S (2005) Linear-programming-based multi-vehicle path planning with adversaries. In: Proceedings of the American control conference, pp 1072–1077

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Presidency University, Bangalore, India

    D. D. Diana Steffi, Shilpa Mehta & Sunil Kumar Dasari

  2. Department of Mathematics and Computer Science, Myanmar Institute of Information Technology, Mandalay, Myanmar

    K. A. Venkatesh

Authors
  1. D. D. Diana Steffi
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  2. Shilpa Mehta
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  3. K. A. Venkatesh
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  4. Sunil Kumar Dasari
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Corresponding author

Correspondence to D. D. Diana Steffi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  2. CHRIST (Deemed to be University), Pune Lavasa Campus, Lavasa, India

    Samiksha Shukla

  3. Stanford University, Stanford, CA, USA

    Aynur Unal

  4. Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Diana Steffi, D.D., Mehta, S., Venkatesh, K.A., Dasari, S.K. (2021). Robot Path Planning–Prediction: A Multidisciplinary Platform: A Survey. In: Jat, D.S., Shukla, S., Unal, A., Mishra, D.K. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-15-5309-7_22

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