Flocking algorithm for autonomous flying robots

Abstract

Abstract. Animal swarms displaying a variety of typical flocking patterns would not exist without underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in the control algorithm of the robots. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour of robots requires the thorough and realistic modeling of the robot and the environment as well. In this paper, first, we present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of the communication, inaccuracy of the onboard sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results about the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. Bio-inspiration works in our case two-ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flights of bird flocks.

Generalized Regressive Motion: a Visual Cue to Collision

“…While our simulations used agents that can immediately measure the position and velocity of any point on their retina, optic-flow-based motion detection has been successfully implemented on minimalistic hardware (Barrows et al, 2002;Beyeler et al, 2009). We see GRM's largest potential in control of small swarming or flocking vehicles (Kushleyev et al, 2013;Virágh et al, 2014). More sophisticated algorithms for robot navigation are often based on Probabilistic Roadmaps (Kavraki et al, 1996;Boor et al, 1999;Karaman and Frazzoli, 2011) or Rapidly Exploring Random Trees (LaValle, 1998;Petti and Fraichard, 2005;Kuwata et al, 2009;Karaman and Frazzoli, 2011).…”

A review of swarm robotics tasks

“…In a seminal paper, Reynolds [55] simulated the behavior of flocking animals with three basic rules: collision avoidance, velocity matching and flock centering. If robots are endowed with the ability to know the heading direction of their neighbors, this ability can be exploited to implement flocking [17,56,57,58,59,60,61]. However, knowledge of the heading of neighbors is not a fixed requirement, as demonstrated in various studies [62,63,64,65,66] where robots do not have this knowledge.…”

“…In [60], flocking in a three-dimensional space is implemented with real flying robots. The control algorithm of the robots incorporates a repulsion force to avoid collisions and an alignment force to align the heading directions of neighbors; relative position and heading of neighbors are obtained via local wireless communication.…”

“…In [17], the flocking behavior is described by measuring the spatial density and the distribution of heading direction values at specific time instants after the beginning of experiments: by taking different "screenshots" during the course of an experiment, insightful representations of the swarm behavior can be analyzed. In [60], the If the flocking task is defined so that robots should move toward a global goal direction, performance metrics must incorporate this direction. For example, in various studies [57,58,66,59] the accuracy value measures how the average direction of robots differs from the goal direction, and in [66] the effective traveled distance projects the vector representing the displacement of the center of mass of the swarm on the vector indicating the goal direction.…”

Swarm-Enabling Technology for Multi-Robot Systems

“…The control community subsequently established a formal framework (Jadbabaie et al, 2003a;Olfati-Saber et al, 2007;Ren and Beard, 2008), which has been put into practice and expanded by multi-robot systems and swarm robotics community (Turgut et al, 2008;Brambilla et al, 2013). Recently, Virágh et al (2014) have established the connection between dynamical update rules of locally interacting agents and cooperative control strategies for flocks of autonomous flying robots. This endeavor was fueled by an intense research activity from biologists and physicists who have sought to identify local update rules at the agent level, which result into observed collective animal behavior FIgURe 1 | block diagram of the swarm-enabling unit (SeU).…”