1,805 research outputs found
Multi-agent Flocking With Random Communication Radius
In this paper, we consider a multi-agent system consisting of mobile agents with second-order dynamics. The communication network is determined by a metric rule based on a random interaction range. The goal of this paper is to determine a bound on the probability that the agents asymptotically agree on a common velocity (i.e. a flocking behavior is achieved). This bound should depend on practical conditions (on the initial positions and velocities of agents) only. For this purpose, we exhibit an i.i.d. process bounding the original system’s dynamics. We build upon previous work on multi-agent systems with switching communication networks. Though conservative, our approach provide conditions that can be verified a priori
Predictive protocol of flocks with small-world connection pattern
By introducing a predictive mechanism with small-world connections, we
propose a new motion protocol for self-driven flocks. The small-world
connections are implemented by randomly adding long-range interactions from the
leader to a few distant agents, namely pseudo-leaders. The leader can directly
affect the pseudo-leaders, thereby influencing all the other agents through
them efficiently. Moreover, these pseudo-leaders are able to predict the
leader's motion several steps ahead and use this information in decision making
towards coherent flocking with more stable formation. It is shown that drastic
improvement can be achieved in terms of both the consensus performance and the
communication cost. From the industrial engineering point of view, the current
protocol allows for a significant improvement in the cohesion and rigidity of
the formation at a fairly low cost of adding a few long-range links embedded
with predictive capabilities. Significantly, this work uncovers an important
feature of flocks that predictive capability and long-range links can
compensate for the insufficiency of each other. These conclusions are valid for
both the attractive/repulsive swarm model and the Vicsek model.Comment: 10 pages, 12 figure
Beyond Reynolds: A Constraint-Driven Approach to Cluster Flocking
In this paper, we present an original set of flocking rules using an
ecologically-inspired paradigm for control of multi-robot systems. We translate
these rules into a constraint-driven optimal control problem where the agents
minimize energy consumption subject to safety and task constraints. We prove
several properties about the feasible space of the optimal control problem and
show that velocity consensus is an optimal solution. We also motivate the
inclusion of slack variables in constraint-driven problems when the global
state is only partially observable by each agent. Finally, we analyze the case
where the communication topology is fixed and connected, and prove that our
proposed flocking rules achieve velocity consensus.Comment: 6 page
Declarative vs Rule-based Control for Flocking Dynamics
The popularity of rule-based flocking models, such as Reynolds' classic
flocking model, raises the question of whether more declarative flocking models
are possible. This question is motivated by the observation that declarative
models are generally simpler and easier to design, understand, and analyze than
operational models. We introduce a very simple control law for flocking based
on a cost function capturing cohesion (agents want to stay together) and
separation (agents do not want to get too close). We refer to it as {\textit
declarative flocking} (DF). We use model-predictive control (MPC) to define
controllers for DF in centralized and distributed settings. A thorough
performance comparison of our declarative flocking with Reynolds' model, and
with more recent flocking models that use MPC with a cost function based on
lattice structures, demonstrate that DF-MPC yields the best cohesion and least
fragmentation, and maintains a surprisingly good level of geometric regularity
while still producing natural flock shapes similar to those produced by
Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.Comment: 7 Page
Refining self-propelled particle models for collective behaviour
Swarming, schooling, flocking and herding are all names given to the wide variety of collective behaviours exhibited by groups of animals, bacteria and even individual cells. More generally, the term swarming describes the behaviour of an aggregate of agents (not necessarily biological) of similar size and shape which exhibit some emergent property such as directed migration or group cohesion. In this paper we review various individual-based models of collective behaviour and discuss their merits and drawbacks. We further analyse some one-dimensional models in the context of locust swarming. In specific models, in both one and two dimensions, we demonstrate how varying the parameters relating to how much attention individuals pay to their neighbours can dramatically change the behaviour of the group. We also introduce leader individuals to these models with the ability to guide the swarm to a greater or lesser degree as we vary the parameters of the model. We consider evolutionary scenarios for models with leaders in which individuals are allowed to evolve the degree of influence neighbouring individuals have on their subsequent motion
Flocking with Obstacle Avoidance
In this paper, we provide a dynamic graph theoretical framework for flocking in presence of multiple obstacles. In particular, we give formal definitions of nets and flocks as spatially induced graphs. We provide models of nets and flocks and discuss the realization/embedding issues related to structural nets and flocks. This allows task representation and execution for a network of agents called alpha-agents. We also consider flocking in the presence of multiple obstacles. This task is achieved by introducing two other types of agents called beta-agents and gamma-agents. This framework enables us to address split/rejoin and squeezing maneuvers for nets/flocks of dynamic agents that communicate with each other. The problems arising from switching topology of these networks of mobile agents make the analysis and design of the decision-making protocols for such networks rather challenging. We provide simulation results that demonstrate the effectiveness of our theoretical and computational tools
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