O-Flocking: Optimized Flocking Model on Autonomous Navigation for Robotic Swarm

Abstract

Flocking model has been widely used in robotic swarm control. However, the traditional model still has some problems such as manually adjusted parameters, poor stability and low adaptability when dealing with autonomous navigation tasks in large-scale groups and complex environments. Therefore, it is an important and meaningful research problem to automatically generate Optimized Flocking model (O-flocking) with better performance and portability. To solve this problem, we design Comprehensive Flocking (C-flocking) model which can meet the requirements of formation keeping, collision avoidance of convex and non-convex obstacles and directional movement. At the same time, Genetic Optimization Framework for Flocking Model (GF) is proposed. The important parameters of C-flocking model are extracted as seeds to initialize the population, and the offspring are generated through operations such as crossover and mutation. The offspring model is input into the experimental scene of autonomous navigation for robotic swarms, and the comprehensive fitness function value is obtained. The model with smallest value is selected as the new seed to continue evolution repeatedly, which finally generates the O-flocking model. The extended simulation experiments are carried out in more complex scenes, and the O-flocking and C-flocking are compared. Simulation results show that the O-flocking model can be migrated and applied to large-scale and complex scenes, and its performance is better than that of C-flocking model in most aspects.

PACNav: a collective navigation approach for UAV swarms deprived of communication and external localization

“…The reactive collision avoidance method used by several state-of-the-art real-world systems [20,39] often suffers from deadlocks. Some examples are shown in figure 4(a).…”

“…To prevent such deadlocks, we propose a novel method to move away from the obstacles while avoiding the deadlocks as much as possible. Unlike [39], the proposed collision avoidance vector has components in both parallel and orthogonal directions relative to the position vector (p i − o r ), where o r ∈ R 2 is the position of the obstacle considered. This collision vector is depicted in figure 4(b).…”

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

“…The parameter space of this model is also analyzed in this article. Very recently, Vásárhelyi et al [28] proposed a new evolution-based SR controller. In this article, they introduced a virtual-physical-law based SR individual controller with 11 tunable parameters.…”