Immune Inspired Cooperative Mechanism with Refined Low-level Behaviors for Multi-Robot Shepherding

In this paper, immune systems and its relationships with multi-robot shepherding problems are discussed. The proposed algorithm is based on immune network theories that have many similarities with the multi-robot systems domain. The underlying immune inspired cooperative mechanism of the algorithm is simulated and evaluated. The paper also describes a refinement of the memory-based immune network that enhances a robot’s action-selection process. A refined model, which is based on the Immune Network T-cell-regulated—with Memory (INT-M) model, is applied to the dog-sheep scenario. The refinements involves the low-level behaviors of the robot dogs, namely shepherds’ formation and shepherds’ approach. These behaviors would make the shepherds to form a line behind the group of sheep and also obey a safety zone of each flock, thus achieving better control of the flock and minimize flock separation occurrences. Simulation experiments are conducted on the Player/Stage robotics platform

Flock Identification using Connected Components Labeling for Multi-Robot Shepherding

Shepherding is often used in robotics and applied to various domains such as military in Unmanned Aerial Vehicle (UAV) or Unmanned Ground Vehicle (UGV) combat scenarios, disaster rescue and even in manufacturing. Generally, robot shepherding refers to a task of a robot known as shepherd or sheep herder, who guards and takes care of flocks of sheep, to make sure that the flock is intact and protect them from predators. In order to make an accurate decision, the shepherd needs to identify the flock that needs to be managed. How does the shepherd can precisely identify a group of animals as a flock? How can one actually judge a flock of sheep, is a flock? How does the shepherd decide how to approach or to steer the flock? These are the questions that relates to flock identification. In this paper, a new method using connected components labeling is proposed to cater the problem of flock identification in multi-robot shepherding scenarios. The results shows that it is a feasible approach, and can be used when integrated with the Player/Stage robotics simulation platform

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

On the formation of terrestrial planets in hot-Jupiter systems

We present a series of calculations aimed at examining how an inner system of planetesimals/protoplanets, undergoing terrestrial planet formation, evolves under the influence of a giant planet undergoing inward type II migration through the region bounded between 5 - 0.1 AU. We find that > 60% of the solids disk survives by being scattered by the giant planet into external orbits. Planetesimals are scattered outward almost as efficiently as protoplanets, resulting in the regeneration of a solids disk where dynamical friction is strong and terrestrial planet formation is able to resume. A simulation extended for a few Myr after the migration of the giant planet halted at 0.1 AU, resulted in an apparently stable planet of ~ 2 Earth masses forming in the habitable zone. Migration-induced mixing of volatile-rich material from beyond the `snowline' into the inner disk regions means that terrestrial planets that form there are likely to be water-rich. We predict that hot--Jupiter systems are likely to harbor water-rich terrestrial planets in their habitable zones. These planets may be detected by future planet search missions.Comment: 18 pages, 13 figures. Higher resolution pdf available at http://www.users.globalnet.co.uk/~mfogg/fogg_nelson2.pd

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