Non-Holonomic Robot Navigation Path Planning Using Fuzzy - Steepest Ascent Hill Climb
Keywords:Path Planning, Fuzzy, Steepest Ascent, Hill Climb
Robot navigation is determination position as well as direction movement in a trip . Planning track is matter most important in the world of mobile robots. in short , determination track will produce dynasty that can traversed (feasible) with availability information that has there is that is map robotic environment . modelling environment map is step first for determine path planning. on the contrary for environment that doesn't known , modeling will done with the information obtained from the sensors on the robot . on the environment actually , noise and limitations accuracy from the sensor leads to the most basic problem in modeling, that is accuracy , and speed , where influence system navigation live in real-time. For resolve matter the needed the existence of a Fuzzy Description Environment which consists of over the fuzzy model of information obtained in the environment around robots. of these models will Becomes base reference path planner that is used reference to path planning. From the map environment modeling will processed by Steepest Ascent Hill Climb so produce track going to point end . From the results program simulation is obtained waypoints with _ distance shortest that is 363.2724 units distance
AR and DP Vinchurkar , “Robot Path Planning using An Ant Colony,” Int. J. Adv. Res. artif . Intell ., vol. 2, p. 65, 2013
Boylestad , Robert. 1992. Electronic Devices and Circuit Theory. Englewood Cliffs: Prentice Hall. MD and T. Stutzle , Ant Colony Optimization. The MIT Press, 2009.
GD and M. Jenkin, Computational Principles of Mobile robotics. New York: Cambridge University Press, 2010.
MD and LM Gambardella, "Ant Colony System: A cooperative learning approach to the traveling salesman problem," IEEE Trans. Evol ., vol. 1, no. 1, pp. 53–66, 1997.
Microcontroller Databook. 1995. San Jose: Atmel Corporation.
Nist Sematech , 2007. e-Handbook of Statistical Methods: Single Response Case. <http://www.it 1.nist . gov/d iv898/handbook/ pri /section5/pri5 31.htm>
Rich, Elaine. 1991. Artificial Intelligence. New York: McGraw-Hill.
TS and TRG B, “Design and development of an autonomous mobile smart vehicle: a mechatronics application,” Mechatronics 14, pp. 491–514, 2014.
Quanser User Manual Qbot . Innovative Educate, 2012.
T. Lehtla, “Introduction to Robotics,” 2008.
YY and YXB ZENG, “Mobile robot Navigation in Unknown Dynamic Environments,” in Global Congress on Intelligent Systems, 2009.
Kusumadewi, S. (2003). Artificial Intelligence. Yogyakarta: Graha Ilmu
Zemma, LA, & Qur, A. (2015). Application of the Steepest Ascent Hill Climbing Method in the Android-Based Route Search Model for Nearest Emergency Service Facilities in Bogor City.
Puzzle (online) (http://www.academia.edu/9717051/, accessed on 19 October 2017).
Zakiah, A., & Masalah, R. (2012). Penyelesaian Masalah 8 Puzzle Dengan Algoritma Hill Climbing, (Sentika), 158–163.
Uriawan, W., Faroqi, A., Fathonah, R., Informatika, J. T., Sains, F., & Negeri, U. I. (2015). Game slider puzzle. Pembuatan Game Slider Puzzle Menggunakan Metode Steepest Ascent Hill Climbing Berbasis Android, IX(1), 204–221
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