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Improved Neural Network 3D Space Obstacle Avoidance Algorithm for Mobile Robot

  • Yuchuang Tong
  • Jinguo LiuEmail author
  • Yuwang Liu
  • Zhaojie Ju
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)

Abstract

Path planning problems are classical optimization problems in many fields, such as computers, mathematics, transportation, robots, etc., which can be described as an optimization problem in mathematics. In this paper, the mathematical model of obstacle environment is established. The characteristics of neural network algorithm, simulated annealing algorithm and adaptive variable stepsize via linear reinforcement are studied respectively. A new neural network 3D space obstacle avoidance algorithm for mobile robot is proposed, which solves the problem of the computational duration and minimum distance of the traditional neural network obstacle avoidance algorithm in solving the optimal path. According to the characteristics of the improved neural network algorithm, it is fused with a variety of algorithms to obtain the optimal path algorithm that achieves the shortest path distance and meets the requirements of obstacle avoidance security. The simulation experiment of the algorithm is simulated by Matlab. The results show that the improved neural network spatial obstacle avoidance algorithm based on the multiple algorithms proposed in this paper can effectively accelerate the convergence speed of path planning, realize the minimum path distance, and achieve very good path planning effect.

Keywords

Global path planning Obstacle avoidance algorithm Improved neural network algorithm Adaptive variable stepsize Simulated annealing 

References

  1. 1.
    Foux, G., Heymann, M., Bruckstein, A.: Two-dimensional robot navigation among unknown stationary polygonal obstacles. IEEE Trans. Robot. Autom. 9(1), 96–102 (1993)CrossRefGoogle Scholar
  2. 2.
    Luo, Y.F., Liu, J.G., Gao, Y., Lu, Z.L.: Smartphone-controlled robot snake for urban search and rescue. In: The 7th International Conference on Intelligent Robotics and Application (ICIRA), pp. 352–363 (2014)CrossRefGoogle Scholar
  3. 3.
    Khosla, P., Volpe R.: Superquadric artificial potentials for obstacle avoidance and approach. In: IEEE International Conference on Robotics & Automation, pp. 1778–1784. IEEE (2002)Google Scholar
  4. 4.
    Lee, M., Park, M.: Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 735–740. IEEE (2003)Google Scholar
  5. 5.
    Zhang, X., Liu, J.G.: Effective motion planning strategy for space robot capturing targets under consideration of the berth position. Acta Astronaut. 148, 403–416 (2018)CrossRefGoogle Scholar
  6. 6.
    Fei, K., YaoNan, W.: Robot path planning based on hybrid artificial potential field/genetic algorithm. J. Syst. Simul. 18(3), 774–777 (2006)Google Scholar
  7. 7.
    Yun, S.C., Ganapathy, V., Chong, L.O.: Improved genetic algorithms based optimum path planning for mobile robot. In: International Conference on Control Automation Robotics & Vision, pp. 1565–1570. IEEE (2011)Google Scholar
  8. 8.
    Wzorek, M., Doherty, P.: Reconfigurable path planning for an autonomous unmanned areal vehicle. In: International Conference on Hybrid Information Technology (ICHIT 2006), pp. 438–441. IEEE (2006)Google Scholar
  9. 9.
    Liu, T.L., Wu, C.D., Li, B., Liu, J.G.: The adaptive path planning research for a shape-shifting robot using particle swarm optimization. In: 5th International Conference on Natural Computation, pp. 324–328. IEEE (2009)Google Scholar
  10. 10.
    Ge, S.S., Cu, Y.J.: New potential functions for mobile robot path planning. IEEE Trans. Robot Autom. 16(5), 615–620 (2000)CrossRefGoogle Scholar
  11. 11.
    Yang, S., Meng, M.: Real-time collision-free path planning of robot manipulators using neural network approaches. Auton. Robots 9(1), 27–39 (2000)CrossRefGoogle Scholar
  12. 12.
    Xu, X., Xie, J., Xie, K.: Path planning and obstacle-avoidance for soccer robot based on artificial potential field and genetic algorithm. In: World Congress on Intelligent Control & Automation, pp. 3494–3498. IEEE (2006)Google Scholar
  13. 13.
    Li, Q., Zhang, W., Yin, Y., Wang, Z., Liu, G.: An improved genetic algorithm of optimum path planning for mobile robots. In: 6th International Conference on Intelligent Systems Design and Applications, pp. 637–642. IEEE (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuchuang Tong
    • 1
    • 2
    • 3
  • Jinguo Liu
    • 1
    • 2
    Email author
  • Yuwang Liu
    • 1
    • 2
  • Zhaojie Ju
    • 4
  1. 1.State Key Laboratory of Robotics, Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina
  2. 2.Institutes for Robotics and Intelligent ManufacturingChinese Academy of SciencesShenyangChina
  3. 3.University of the Chinese Academy of ScienceBeijingChina
  4. 4.School of ComputingUniversity of PortsmouthPortsmouthUK

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