Towards Drone Flocking Using Relative Distance Measurements

• Published in: Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning Vol. 13703; pp. 97 - 109
• Main Authors: Brandstätter, Andreas, Smolka, Scott A., Stoller, Scott D., Tiwari, Ashish, Grosu, Radu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Distributed controller; Drones; Flock; Quadcopters; Swarm
• ISBN: 3031197585; 9783031197581
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-19759-8_7

We introduce a method to form and maintain a flock of drones only based on relative distance measurements. This means our approach is able to work in GPS-denied environments. It is fully distributed and therefore does not need any information exchange between the individual drones. Relative distance measurements to other drones and information about its own relative movement are used to estimate the current state of the environment. This makes it possible to perform lookahead and estimate the next state for any potential next movement. A distributed cost function is then used to determine the best next action in every time step. Using a high-fidelity simulation environment, we show that our approach is able to form and maintain a flock for a set of drones.