Formation Control of UAVs and Mobile Robots Using Self-organized Communication Topologies

Abstract

Formation control in a robot swarm targets the overall swarm shape and relative positions of individual robots during navigation. Existing approaches often use a global reference or have limited topology flexibility. We propose a novel approach without these constraints, by extending the concept of 'mergeable nervous systems' to establish distributed asymmetric control via a self-organized wireless communication network. In simulated experiments with UAVs and mobile robots, we present a proof-of-concept for three sub-tasks of formation control: formation establishment, maintenance during motion, and deformation. We also assess the fault tolerance and scalability of our approach.

Swarm Robotics: Past, Present, and Future [Point of View]

“…Mathews et al [88] have created the infrastructuremiddleware-that allows a robot swarm to autonomously switch from purely self-organized control to hierarchical control and back. While experiments have demonstrated the feasibility of the approach [88], [89], much needs to be done to understand how the rules that allow the creation of the hierarchical control structure should be designed as a function of the task that the robot swarm has to perform, and how the passage from purely self-organized to hierarchical control and back can be activated as a function of the task and of the environment in which the robot swarm is acting.…”

Systematic literature review of applications and usage potentials for the combination of unmanned aerial vehicles and mobile robot manipulators in production systems

“…Both underlying problems, reference tracking of a velocity profile, and position control to finally target the desired way points, were addressed by the combination of basic control laws. Based on an established concept, the main contribution of [120] is a distributed control using self-organizing wireless communication between UAVs and ground vehicles for formation, maintenance, and de-formation. The network in the form of a rooted tree here results from a self-organizing process.…”

The design of self-organizing human–swarm intelligence

“…On this abstracted level, one may think of the human operator himself as a distributed self-organizing neural system that is extended by a neural–swarm machine. The benefit for swarm engineering by transferring neural principles to swarm networks that goes beyond the usage of neural robot controllers has been demonstrated recently by the Bruessel’s group (Mathews et al, 2017; Zhu et al, 2020), Monaco, Hwang, Schultz, & Zhang (2020), and Otte (2018).…”