Flocking Of Agents Research Articles

Connectivity Preservation and Collision Avoidance in Multi-Agent Systems Using Model Predictive Control

This paper presents an innovative predictive control scheme based on a potential field in order to maintain the connectivity of a flock of agents in a leader-follower configuration with dynamic topology. We consider a group of agents navigating in an environment with obstacles towards their target. The followers avoid collisions with each other and obstacles without using any communication links. It is also required to maintain connectivity with the leader, which is unidentified to the followers. The potential field is dynamically updated by introducing time-varying weighted links between the followers to preserve connectivity as we assume only the leader knows the target position. The values of these weights are adjusted continuously according to agents' trajectories by which the critical neighbours of each agent are determined. The superior performance of the proposed predictive controller for navigation of agents to quickly reach their target is demonstrated comparatively by simulation.

ИНФОРМАЦИОННО-КОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИ И ЭЛЕМЕНТЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМАХ

The article conducts information on modeling the process of forming a flock of agents representing drones or vehicles. As a modeling environment, the NetLogo simulation program is used. An algorithm for the formation of a pack is considered by allocating an agent of a leader among agents, the position of which in space is a guideline for agents included in the flock. The process of the formation of the flock is shown using the final UML machines.

Multi-gas source localization and mapping by flocking robots

Multi-Gas source localization and mapping is a challenging problem because multiple measurements must be taken to ensure accurate localization. This paper presents a novel flocking control strategy for multi-robot exploration and gas field mapping to address this problem. The algorithm includes an active sensing mechanism for driving a flock of agents towards target measurement locations that optimize the posterior probability density and a collaborative sequential Monte Carlo information fusion approach for estimating gas fields. We tested the performance of our system on Jackal mobile robots in a chemical leak scenario with two gas leakage sources. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recent single-agent and centralized sequential Monte Carlo-based gas concentration mapping in terms of the estimate accuracy, the convergence time, and the mapping error.

Shepherding algorithm for heterogeneous flock with model-based discrimination

The problem of guiding a flock of agents to a destination by the repulsion forces exerted by a smaller number of external agents is called the shepherding problem. This problem has attracted attention due to its potential applications, including diverting birds away for preventing airplane accidents, recovering spilled oil in the ocean, and guiding a swarm of robots for mapping. Although there have been various studies on the shepherding problem, most of them place the uniformity assumption on the dynamics of agents to be guided. However, we can find various practical situations where this assumption does not necessarily hold. In this paper, we consider the situation in which we are given a flock of agents consisting of normal agents to be guided and other variant agents. Under this situation, we propose a shepherding method for guiding the normal sheep. Specifically, in this method, the shepherd discriminates normal and variant agents based on their behaviors' deviation from the one predicted by the potentially inaccurate model of the normal agents. As for the discrimination process, we propose two methods using static and dynamic thresholds. Our simulation results show that the proposed methods outperform a conventional method for various types of variant agents.

A Software Framework for Self-Organized Flocking System Motion Coordination Research

We describe and analyze the basic algorithms for the self-organization of a swarm of robots in coordinated motion as a flock of agents as a strategy for the solution of multi-agent tasks. This analysis allows us to postulate a simulation framework for such systems based on the behavioral rules that characterize the dynamics of these systems. The problem is approached from the perspective of autonomous navigation in an unknown but restricted and locally observable environment. The simulation framework allows defining individually the characteristics of the basic behaviors identified as fundamental to show a flocking behavior, as well as the specific characteristics of the naviga-tion environment. It also allows the incorporation of different path planning approaches to enable the system to navigate the environment for different strategies, both geometric and reactive. The basic behaviors modeled include safe wandering, following, aggregation, dispersion, and homing, which interact to generate flocking behavior, i.e., the swarm aggregates, reach a stable formation and move in an organized fashion toward the target point. The framework concept follows the principle of constrained target tracking, which allows the problem to be solved similarly as a small robot with limited computation would solve it. It is shown that the algorithm and the framework that implements it are robust to the defined constraints and manage to generate the flocking behavior while accomplishing the navigation task. These results provide key guidelines for the implementation of these algorithms on real platforms.

Iterative shepherding control for agents with heterogeneous responsivity.

In the context of the theory of multi-agent systems, the shepherding problem refers to designing the dynamics of a herding agent, called a sheepdog, so that a given flock of agents, called sheep, is guided into a goal region. Although several effective methodologies and algorithms have been proposed in the last decade for the shepherding problem under various formulations, little research has been directed to the practically important case in which the flock contains sheep agents unresponsive to the sheepdog agent. To fill in this gap, we propose a sheepdog algorithm for guiding unresponsive sheep in this paper. In the algorithm, the sheepdog iteratively applies an existing shepherding algorithm, the farthest-agent targeting algorithm, while dynamically switching its destination. This procedure achieves the incremental growth of a controllable flock, which finally enables the sheepdog to guide the entire flock into the goal region. Furthermore, we illustrate by numerical simulations that the proposed algorithm can outperform the farthest-agent targeting algorithm.

Distributed control for flocking and group maneuvering of nonholonomic agents

AbstractIn this paper, we propose a distributed control approach for flocking and group maneuvering of nonholonomic agents, with constrained kinematic properties commonly found in practical systems, such as fixed‐wing unmanned aerial vehicles. Flocking of agents with differential drive kinematics is addressed by introducing a virtual leader–follower mechanism into the Olfati‐Saber's algorithm, which is originally proposed for holonomic agents with double integrator kinematics. Then, group maneuverability of the flock is achieved by superimposing a group motion onto each agent's flocking motion. Moreover, it is proven that speed limits are intrinsically guaranteed by the approach, which renders it more applicable in practical systems. Experimental results in MATLAB and Gazebo, a popular robotic simulator, are presented to evaluate the performance and demonstrate the effectiveness of the proposed approach.

A novel alignment repulsion algorithm for flocking of multi-agent systems based on the number of neighbours per agent

In this paper, an energy-based control methodology is proposed to satisfy the Reynolds three rules in a flock of multiple agents. First, a control law is provided that is directly derived from the passivity theorem. In the next step, the Number of Neighbours Alignment/Repulsion algorithm is introduced for a flock of agents which loses the cohesion ability and uniformly joint connectivity condition. With this method, each agent tries to follow the agents which escape its neighbourhood by considering the velocity of escape time and number of neighbours. It is mathematically proved that the motion of multiple agents converges to a rigid and uncrowded flock if the group is jointly connected just for an instant. Moreover, the conditions for collision avoidance are guaranteed during the entire process. Finally, simulation results are presented to show the effectiveness of the proposed methodology.

Position convergence of informed agents in flocking problem with general linear dynamic agents

In most existing works on the flocking problem, results were obtained for the case where a double integrator dynamic is used to describe motion of agents and virtual leader. These results, however, may not be generalised to agents with general linear dynamic. In this study, it is shown that in the flocking of agents with a linear dynamic, the velocity convergence of agents does not generally lead to the position convergence of informed agents and the position convergence is possible only for a specified group of linear dynamics. In this study, a control protocol is proposed to flock agents with specified linear dynamics and guarantee the velocity convergence of all agents as well as average position convergence of informed agents to those of the virtual leader.

A New Approach of Data Clustering Using a Flock of Agents

This paper presents a new bio-inspired algorithm (FClust) that dynamically creates and visualizes groups of data. This algorithm uses the concepts of a flock of agents that move together in a complex manner with simple local rules. Each agent represents one data. The agents move together in a 2D environment with the aim of creating homogeneous groups of data. These groups are visualized in real time, and help the domain expert to understand the underlying structure of the data set, like for example a realistic number of classes, clusters of similar data, isolated data. We also present several extensions of this algorithm, which reduce its computational cost, and make use of a 3D display. This algorithm is then tested on artificial and real-world data, and a heuristic algorithm is used to evaluate the relevance of the obtained partitioning.