Skip to main content
SpringerLink
Log in

Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot

A Control Theory with Genetic Programming Approach

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviors to relate each control objective with a desired behavior for the mobile robot. Natural behaviors lead the system to fulfill a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behavior. The adaptive behavior, learned through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behavior of the mobile robot is the addition of the natural and the adaptive behaviors. Our proposed methodology achieves the discovery of 9402 behaviors without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Arambula, F., Padilla, M.: Autonomous robot navigation using adaptive potential fields. Math. Comput. Model. 40(9), 1141–1156 (2004). https://doi.org/10.1016/j.mcm.2004.05.001

    Article  MATH  Google Scholar 

  2. Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., Bouzouia, B.: Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robot. Auton. Syst. 89, 95–109 (2017). https://doi.org/10.1016/j.robot.2016.12.008

    Article  Google Scholar 

  3. Bugarin, E., Kelly, R.: RTSVC: real-time system for visual control of robots. Int. J. Imaging Syst. Technol. 18(4), 251–256 (2008). https://doi.org/10.1002/ima.20135

    Article  Google Scholar 

  4. Campion, G., Bastin, G., Dandrea-Novel, B.: Structural properties and classification of kinematic and dynamic models of wheeled mobile robots. IEEE Trans. Robot. Autom. 12(1), 47–62 (1996). https://doi.org/10.1109/70.481750

    Article  Google Scholar 

  5. Chaturvedi, D.K.: Soft Computing: Techniques and its Applications in Electrical Engineering. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-77481-5

    Book  Google Scholar 

  6. Dierks, T., Jagannathan, S.: Control of nonholonomic mobile robot formations using neural networks. In: 2007 IEEE 22nd International Symposium on Intelligent Control, pp. 132–137. (2007). https://doi.org/10.1109/ISIC.2007.4450873

  7. Dolsma, J.: Nonlinear controller design based on genetic programming. Master’s thesis, Technische Universiteit Eindhoven, Department Mechanical Engineering, Dynamics and Control Group, Eindhoven (2007)

  8. Duguleana, M., Mogan, G.: Neural networks based reinforcement learning for mobile robots obstacle avoidance. Exp. Syst. Appl. 62, 104–115 (2016). https://doi.org/10.1016/j.eswa.2016.06.021

    Article  Google Scholar 

  9. Fang, M.C., Zhuo, Y.Z., Lee, Z.Y.: The application of the self-tuning neural network PID controller on the ship roll reduction in random waves. Ocean Eng. 37(7), 529–538 (2010). https://doi.org/10.1016/j.oceaneng.2010.02.013

    Article  Google Scholar 

  10. Floreano, D., Husbands, P., Nolfi, S.: Evolutionary Robotics, pp 1423–1451. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-30301-5_62

    Google Scholar 

  11. Ge, S., Cui, Y.: Dynamic motion planning for mobile robots using potential field method. Auton. Robot. 13(3), 207–222 (2002). https://doi.org/10.1023/A:1020564024509

    Article  MATH  Google Scholar 

  12. Guldner, J., Utkin, V.I.: Tracking the gradient of artificial potential fields: sliding mode control for mobile robots. Int. J. Control 63(3), 417–432 (1996). https://doi.org/10.1080/00207179608921850

    Article  MathSciNet  Google Scholar 

  13. Hoy, M., Matveev, A.S., Savkin, A.V.: Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey. Robotica 33(3), 463–497 (2015)

    Article  Google Scholar 

  14. Hu, T., Lin, Z.: Control Systems with Actuator Saturation: Analysis and Design. Birkhauser, Boston (2001)

    Book  Google Scholar 

  15. Huang, J., Wen, C., Wang, W., Jiang, Z.P.: Adaptive stabilization and tracking control of a nonholonomic mobile robot with input saturation and disturbance. Syst. Control Lett. 62(3), 234–241 (2013). https://doi.org/10.1016/j.sysconle.2012.11.020

    Article  MathSciNet  MATH  Google Scholar 

  16. Ibrahim, D.: An overview of soft computing. Procedia Comput. Sci. 102, 34–38 (2016). https://doi.org/10.1016/j.procs.2016.09.366 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29–30 August 2016, Vienna

    Article  Google Scholar 

  17. Jaradat, M.A.K., Garibeh, M.H., Feilat, E.A.: Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Comput. 16(1), 153–164 (2012). https://doi.org/10.1007/s00500-011-0742-z

    Article  Google Scholar 

  18. Johnson, E., Calise, A.: Neural network adaptive control of systems with input saturation. In: Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), vol. 5, pp. 3527–3532 (2001)

  19. Kamel, M.E., Beji, L., Abichou, A., Mammar, S.: A novel obstacle avoidance approach for mobile robot system including target capturing. AIP Conf. Proc. 1107, 249–253 (2009). https://doi.org/10.1063/1.3106481

    Article  Google Scholar 

  20. Khalil, H.: Nonlinear Systems, 3rd edn. Prentice-Hall, New Jersey (2002)

    Google Scholar 

  21. Khatib, O.: Commande Dynamique dans l’Espace Opérationnel des Robots Manipulateurs en Présence d’Obstacles. Ph.D. thesis, École Nationale Supérieure de l’Aéronautique et de l’Espace (ENSAE), Tolouse (1980)

  22. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings. 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505. (1985). https://doi.org/10.1109/ROBOT.1985.1087247

  23. Kim, D., Park, J.: Intelligent PID Controller Tuning of AVR System Using GA and PSO, pp. 366–375. Springer, Berlin (2005). https://doi.org/10.1007/11538356_38

    Google Scholar 

  24. Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings. 1991 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1398–1404 (1991). https://doi.org/10.1109/ROBOT.1991.131810

  25. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  26. Masoud, A.A.: A harmonic potential field approach for joint planning and control of a rigid, separable nonholonomic, mobile robot. Robot. Auton. Syst. 61(6), 593–615 (2013). https://doi.org/10.1016/j.robot.2013.02.007

    Article  Google Scholar 

  27. Mataric, M.: Interaction and intelligent behavior. Ph.D. thesis, Cambridge. AAI0575115 (1994)

  28. Mataric, M.: Designing and understanding adaptive group behavior. Adapt. Behav. 4, 51–80 (1995)

    Article  Google Scholar 

  29. Matariċ, M.J., Michaud, F.: Behavior-Based Systems, pp 891–909. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-30301-5_39

    Google Scholar 

  30. Minguez, J., Lamiraux, F., Laumond, J.: Motion Planning and Obstacle Avoidance, pp 827–852. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-30301-5_36

    Google Scholar 

  31. Mohanty, P.K., Parhi, D.R.: Optimal path planning for a mobile robot using cuckoo search algorithm. J. Exp. Theor. Artif. Intell. 28(1–2), 35–52 (2016). https://doi.org/10.1080/0952813X.2014.971442

    Article  Google Scholar 

  32. Montiel, O., Orozco-Rosas, U., Sepúlveda, R.: Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Exp. Syst. Appl. 42(12), 5177–5191 (2015). https://doi.org/10.1016/j.eswa.2015.02.033

    Article  Google Scholar 

  33. Nelson, A.L., Barlow, G.J., Doitsidis, L.: Fitness functions in evolutionary robotics: a survey and analysis. Robot. Auton. Syst. 57(4), 345–370 (2009). https://doi.org/10.1016/j.robot.2008.09.009

    Article  Google Scholar 

  34. Oftadeh, R., Aref, M., Ghabcheloo, R., Mattila, J.: Bounded-velocity motion control of four wheel steered mobile robots. In: 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 255–260. (2013). https://doi.org/10.1109/AIM.2013.6584101

  35. Ohnishi, N., Imiya, A.: Visual Navigation of Mobile Robot Using Optical Flow and Visual Potential Field, pp 412–426. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-78157-8_32

    Google Scholar 

  36. Pacejka, H.B.: Chapter 2—basic tire modeling considerations. In: Pacejka, H.B. (ed.) Tire and Vehicle Dynamics, 3rd edn. https://doi.org/10.1016/B978-0-08-097016-5.00002-4, pp 59–85. Butterworth-Heinemann, Oxford (2012)

    Chapter  Google Scholar 

  37. Parker, L.E.: Multiple Mobile Robot Teams, Path Planning and Motion Coordination in, pp. 5783–5800. Springer, New York (2009). https://doi.org/10.1007/978-0-387-30440-3_344

    Google Scholar 

  38. Pazderski, D.: Waypoint following for differentially driven wheeled robots with limited velocity perturbations. J. Intell. Robot. Syst. 85(3), 553–575 (2017). https://doi.org/10.1007/s10846-016-0391-7

    Article  Google Scholar 

  39. Peñaloza-Mejía, O., Márquez-Martínez, L., Alvarez, J., Villarreal-Cervantes, M., García-Hernández, R.: Motion control design for an omnidirectional mobile robot subject to velocity constraints. Math. Probl. Eng. 2015, 201–213 (2015). https://doi.org/10.1155/2015/608015

    Article  MathSciNet  MATH  Google Scholar 

  40. Pilat, M., Oppacher, F.: Evolution of Khepera Robotic Controllers with Hierarchical Genetic Programming Techniques, pp 43–71. Springer, Berlin (2005). https://doi.org/10.1007/3-540-32364-3_3

    Google Scholar 

  41. Pretorius, C., du Plessis, M., Gonsalves, J.: Neuroevolution of inverted pendulum control: a comparative study of simulation techniques. J. Intell. Robot. Syst. 1–27. (2017). https://doi.org/10.1007/s10846-017-0465-1

    Article  Google Scholar 

  42. Rajamani, R.: Lateral and Longitudinal Tire Forces, pp 355–396. Springer US, Boston (2012). https://doi.org/10.1007/978-1-4614-1433-9_13

    Google Scholar 

  43. Ren, W., Sorensen, N.: Distributed coordination architecture for multi-robot formation control. Robot. Auton. Syst. 56(4), 324–333 (2008). https://doi.org/10.1016/j.robot.2007.08.005

    Article  MATH  Google Scholar 

  44. Sandou, G.: Metaheuristic Optimization for the Design of Automatic Control Laws. Wiley-ISTE (2013)

  45. Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81–85 (2009). https://doi.org/10.1126/science.1165893

    Article  Google Scholar 

  46. Seeler, K.: Transfer Functions, Block Diagrams and the s-Plane, pp 519–576. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-9152-1_9

    Google Scholar 

  47. Senanayake, M., Senthooran, I., Barca, J.C., Chung, H., Kamruzzaman, J., Murshed, M.: Search and tracking algorithms for swarms of robots: a survey. Robot. Auton. Syst. 75, 422–434 (2016). https://doi.org/10.1016/j.robot.2015.08.010

    Article  Google Scholar 

  48. Štimac, G., Braut, S., žigulić, R.: Comparative analysis of PSO algorithms for PID controller tuning. Chin. J. Mech. Eng. 27(5), 928–936 (2014)

    Article  Google Scholar 

  49. Åström, K.J., Kumar, P.: Control: a perspective. Automatica 50(1), 3–43 (2014). https://doi.org/10.1016/j.automatica.2013.10.012

    Article  MathSciNet  Google Scholar 

  50. Tzafestas, S.G.: 11—mobile robot path, motion, and task planning. In: Tzafestas, S.G. (ed.) Introduction to Mobile Robot Control. https://doi.org/10.1016/B978-0-12-417049-0.00011-0, pp 429–478. Elsevier, Oxford (2014)

    Chapter  Google Scholar 

  51. Urakubo, T.: Stability analysis and control of nonholonomic systems with potential fields. J. Intell. Robot. Syst. 1–17. https://doi.org/10.1007/s10846-017-0473-1 (2017)

    Article  Google Scholar 

  52. Valavanis, K.P., Hebert, T., Kolluru, R., Tsourveloudis, N.: Mobile robot navigation in 2-d dynamic environments using an electrostatic potential field. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(2), 187–196 (2000). https://doi.org/10.1109/3468.833100

    Article  Google Scholar 

  53. Valipour, M.: Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorol. Appl. 23(1), 91–100 (2016)

    Article  Google Scholar 

  54. Viero, D.P., Valipour, M.: Modeling anisotropy in free-surface overland and shallow inundation flows. Adv. Water Resour. 104, 1–14 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by TecNM projects 5567.15-P and 5748.16-P; CONACyT Cátedras 2459. The authors would like to thank to Consejo Nacional de Ciencia y Tecnología, Tecnológico Nacional de México, Instituto Tecnológico de Ensenada, and Eng. Raúl Miguel Figueroa Nuñez for assistance with the experiments.

Author information

Authors and Affiliations

  1. División de Estudios de Posgrado e Investigación, TecNM – Instituto Tecnológico de Ensenada, Blvd. Tecnológico No. 150 Col. Ex Ejido Chapultepec, 22780, Ensenada, Baja California, México

    Eddie Clemente, Eusebio Bugarin & Ana Yaveni Aguilar-Bustos

  2. CONACYT – Instituto Tecnológico de Ensenada, Mexico City, Mexico

    Marlen Meza-Sánchez

Authors
  1. Eddie Clemente
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marlen Meza-Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eusebio Bugarin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ana Yaveni Aguilar-Bustos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eddie Clemente.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Clemente, E., Meza-Sánchez, M., Bugarin, E. et al. Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot. J Intell Robot Syst 92, 359–380 (2018). https://doi.org/10.1007/s10846-017-0751-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-017-0751-y

Keywords

Search

Navigation