PATH PLANNING FOR DRONES
Abstract
Path Planning for autonomous robots is finding the shortest optimum path from a given source to a destination while also taking obstacles into consideration. In our case the autonomous robots are drones.. The algorithm needs to work dynamically and continuously as the drone is traversing as all the information about the environment would not be given initially but will be perceived as traversing. Thus the purpose of this project is to study and compare 3 methods for path planning on drones with low-end hardware, thus the algorithm needs to have time and space complexity less than or equal to what is required. The algorithms that are studied are path planning using fuzzy logic, path planning using reinforcement learning, and modified A-star algorithm.
References
Bart Remes, Dino Hensen, Freek van Tienen, Christophe De Wagter, Erik van der Horst, and Guido de Croon, “Paparazzi: how to make a swarm of Parrot AR Drones fly autonomously based on GPS”, published in the International Micro Air Vehicle Conference and Flight Competition (IMAV2013) 17-20 September 2013, Toulouse, France
Axel Bürkle, Matthias Kollmann,Florian Sego,” Towards Autonomous Micro UAV Swarms”, published in Journal of Intelligent & Robotic Systems on 27 October 2010
Jose Luis Sanchez-Lopez ; Jesús Pestana ; Paloma de la Puente ; Ramon Suarez-Fernandez, “A system for the design and development of vision-based multi-robot quadrotor swarms” published in IEEE in 2014 May from USA
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