44 research outputs found
Contextually aware intelligent control agents for heterogeneous swarms
An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.</p
Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution
Shepherding involves herding a swarm of agents (\emph{sheep}) by another a
control agent (\emph{sheepdog}) towards a goal. Multiple approaches have been
documented in the literature to model this behaviour. In this paper, we present
a modification to a well-known shepherding approach, and show, via simulation,
that this modification improves shepherding efficacy. We then argue that given
complexity arising from obstacles laden environments, path planning approaches
could further enhance this model. To validate this hypothesis, we present a
2-stage evolutionary-based path planning algorithm for shepherding a swarm of
agents in 2D environments. In the first stage, the algorithm attempts to find
the best path for the sheepdog to move from its initial location to a strategic
driving location behind the sheep. In the second stage, it calculates and
optimises a path for the sheep. It does so by using \emph{way points} on that
path as the sequential sub-goals for the sheepdog to aim towards. The proposed
algorithm is evaluated in obstacle laden environments via simulation with
further improvements achieved
Contextually Aware Intelligent Control Agents for Heterogeneous Swarms
An emerging challenge in swarm shepherding research is to design effective
and efficient artificial intelligence algorithms that maintain a
low-computational ceiling while increasing the swarm's abilities to operate in
diverse contexts. We propose a methodology to design a context-aware
swarm-control intelligent agent. The intelligent control agent (shepherd) first
uses swarm metrics to recognise the type of swarm it interacts with to then
select a suitable parameterisation from its behavioural library for that
particular swarm type. The design principle of our methodology is to increase
the situation awareness (i.e. information contents) of the control agent
without sacrificing the low-computational cost necessary for efficient swarm
control. We demonstrate successful shepherding in both homogeneous and
heterogeneous swarms.Comment: 37 pages, 3 figures, 11 table
Shepherding Heterogeneous Flocks: Overview and Prospect
The problem of guiding a flock of several autonomous agents using repulsion
force exerted by a smaller number of agents is called the shepherding problem
and has been attracting attention due to its potential engineering
applications. Although several works propose methodologies for achieving the
shepherding task in this context, most assume that sheep agents have the same
dynamics, which only sometimes holds in reality. The objective of this
discussion paper is to overview a recent research trend addressing the gap
mentioned above between the commonly placed uniformity assumption and the
reality. Specifically, we first introduce recent guidance methods for
heterogeneous flocks and then describe the prospects of the shepherding problem
for heterogeneous flocks
Shepherding Control for Separating a Single Agent from a Swarm
In this paper, we consider the swarm-control problem of spatially separating
a specified target agent within the swarm from all the other agents, while
maintaining the connectivity among the other agents. We specifically aim to
achieve the separation by designing the movement algorithm of an external
agent, called a shepherd, which exerts repulsive forces on the agents in the
swarm. This problem has potential applications in the context of the
manipulation of the swarm of micro- and nano-particles. We first formulate the
separation problem, where the swarm agents (called sheep) are modeled by the
Boid model. We then analytically study the special case of two-sheep swarms. By
leveraging the analysis, we then propose a potential function-based movement
algorithm of the shepherd to achieve separation while maintaining the
connectivity within the remaining swarm. We demonstrate the effectiveness of
the proposed algorithm with numerical simulations.Comment: 6 pages, 6 figure
Biologically inspired herding of animal groups by robots
A single sheepdog can bring together and manoeuvre hundreds of sheep from one location to another. Engineers and ecologists are fascinated by this sheepdog herding because of the potential it provides for ‘bio-herding’: a biologically inspired herding of animal groups by robots. Although many herding algorithms have been proposed, most are studied via simulation.There are a variety of ecological problems where management of wild animal groups is currently impossible, dangerous and/or costly for humans to manage directly, and which may benefit from bio-herding solutions.Unmanned aerial vehicles (UAVs) now deliver significant benefits to the economy and society. Here, we suggest the use of UAVs for bio-herding. Given their mobility and speed, UAVs can be used in a wide range of environments and interact with animal groups at sea, over the land and in the air.We present a potential roadmap for achieving bio-herding using a pair of UAVs. In our framework, one UAV performs ‘surveillance’ of animal groups, informing the movement of a second UAV that herds them. We highlight the promise and flexibility of a paired UAV approach while emphasising its practical and ethical challenges. We start by describing the types of experiments and data required to understand individual and collective responses to UAVs. Next, we describe how to develop appropriate herding algorithms. Finally, we describe the integration of bio-herding algorithms into software and hardware architecture