The development of an autonomous navigation system with optimal control of an UAV in partly unknown indoor environment. (February 2018)

• Record Type: Journal Article
• Authors: Mac, Thi Thoa; Copot, Cosmin; Keyser, Robin De; Ionescu, Clara M.
• Is Part Of: Mechatronics. Volume 49(2018)
• Page Start: 187
• Page End: 196
• Publication Date: 2018-02
• Subjects: Unmanned aerial vehicles (UAVs) -- Sensor fusion -- Autonomous navigation -- Optimal control -- Multi-objective particle swarm optimization; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2017.11.014
• Languages: English
• ISSNs: 0957-4158

Abstract

Highlights: An autonomous methodology for a low-cost UAV in partly unknown indoor flight. The development of a position-estimation method using sensor fusion. An optimal controller based on a multi objective particle swarm optimization with an accelerated update methodology. Graphical abstract: Abstract: This paper presents an autonomous methodology for a low-cost commercial AR.Drone 2.0 in partly unknown indoor flight using only on-board visual and internal sensing. Novelty lies in: ( i ) the development of a position-estimation method using sensor fusion in a structured environment. This localization method presents how to get the UAV localization states (position and orientation), through a sensor fusion scheme, dealing with data provided by an optical sensor and an inertial measurement unit (IMU). Such a data fusion scheme takes also in to account the time delay present in the camera signal due to the communication protocols; ( ii ) improved potential field method which is capable of performing obstacle avoiding in an unknown environment and solving the non-reachable goal problem; and ( iii ) the design and implementation of an optimal proportional - integral - derivative (PID) controller based on a novel multi-objective particle swarm optimization with an accelerated update methodology tracking such reference trajectories, thus characterizing a cascade controller. Experimental results validate the effectiveness of the proposed approach.