Planning Navigation Routes in Unknown Environments

Author:

Rodriguez LauraORCID,la Rosa Fernando DeORCID,Cardozo NicolásORCID

Publisher

Springer Nature Switzerland

Reference8 articles.

1. Cho, J.H., Pae, D.S., Lim, M.T., Kang, T.K.: A real-time obstacle avoidance method for autonomous vehicles using an obstacle-dependent gaussian potential field. J. Adv. Transp. 1–16 (1: school of Electrical Engineering. Korea University, Seoul, Republic of Korea (2018)

2. Garage, W.: Ros robot operating system. https://www.ros.org/ (12 2007). Stanford Artificial Intelligence Laboratory Open Robotics

3. Han, W.G., Baek, S.M., Kuc, T.Y.: Genetic algorithm based path planning and dynamic obstacle avoidance of mobile robots. In: International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 3, pp. 2747–2751 (1997). Intelligent Control and Dynamic Simulation Lab

4. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

5. Meng, X., Zhu, X.: Autonomous obstacle avoidance path planning for grasping manipulator based on elite smoothing ant colony algorithm. Symmetry 14(9), 1–20 (2022)

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