Steering herds away from dangers in dynamic environments

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed

Fly with me : algorithms and methods for influencing a flock

As robots become more affordable, they will begin to exist in the world in greater quantities. Some of these robots will likely be designed to act as components in specific teams. These teams could work on tasks that are too large or complex for a single robot - or that are merely more efficiently accomplished by a team - such as surveillance in a large building or product delivery to packers in a warehouse. Multiagent systems research studies how these teams are formed and how they work together. Ad hoc teamwork, a newer area of multiagent systems research, studies how new robots can join these pre-existing teams and assist the team in accomplishing its goal. This dissertation extends and applies research in ad hoc teamwork towards the general area of flocking, which is an emergent swarm behavior. In particular, the work in this dissertation considers how ad hoc agents - called influencing agents in this dissertation - can join a flock, be recognized by the rest of the flock as part of the flock, influence the flock towards particular behaviors through their own behavior, and then separate from the flock. Specifically, the primary research question addressed in this dissertation is How can influencing agents be utilized in various types of flocks to influence the flock towards a particular behavior? In order to address this research question, this dissertation makes six main types of contributions. First, this dissertation formalizes the problem of using influencing agents to influence a flock. Second, this dissertation contributes and analyzes algorithms for influencing a flock to a desired orientation. Third, this dissertation presents methods for determining how to best add influencing agents to a flock. Fourth, this dissertation provides methods by which influencing agents can join and then leave a flock in motion. Fifth, this dissertation evaluates some of the influencing agent algorithms on a robot platform. Sixth, although the majority of this dissertation assumes the influencing agents will join a flock that behaves similarly to European starlings, this dissertation also provides insight into when and how its algorithms are generalizable to other types of flocks as well as to general teamwork and coordination research. All of the methods presented in this dissertation are empirically evaluated using a simulator that can support large flocks.Computer Science

Constructing Geometries for Group Control: Methods for Reasoning about Social Behaviors

Social behaviors in groups has been the subjects of hundreds of studies in a variety of research disciplines, including biology, physics, and robotics. In particular, flocking behaviors (commonly exhibited by birds and fish) are widely considered archetypical social behavioris and are due, in part, to the local interactions among the individuals and the environment. Despite a large number of investigations and a significant fraction of these providing algorithmic descriptions of flocking models, incompleteness and imprecision are readily identifiable in these algorithms, algorithmic input, and validation of the models. This has led to a limited understanding of the group level behaviors. Through two case-studies and a detailed meta-study of the literature, this dissertation shows that study of the individual behaviors are not adequate for understanding the behaviors displayed by the group. To highlight the limitations in only studying the individuals, this dissertation introduces a set of tools, that together, unify many of the existing microscopic approaches. A meta-study of the literature using these tools reveal that there are many small differences and ambiguities in the flocking scenarios being studied by different researchers and domains; unfortunately, these differences are of considerable significance. To address this issue, this dissertation exploits the predictable nature of the group’s behaviors in order to control the given group and thus hope to gain a fuller understanding of the collective. From the current literature, it is clear the environment is an important determinant in the resulting collective behaviors. This dissertation presents a method for reasoning about the effects the geometry of an environment has on individuals that exhibit collective behaviors in order to control them. This work formalizes the problem of controlling such groups by means of changing the environment in which the group operates and shows this problem to be PSPACE-Hard. A general methodology and basic framework is presented to address this problem. The proposed approach is general in that it is agnostic to the individual’s behaviors and geometric representations of the environment; allowing for a large variety in groups, desired behaviors, and environmental constraints to be considered. The results from both the simulations and over 80 robot trials show (1) the solution can automatically generate environments for reliably controlling various groups and (2) the solution can apply to other application domains; such as multi-agent formation planning for shepherding and piloting applications

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Computational Intelligence for Cooperative Swarm Control

Over the last few decades, swarm intelligence (SI) has shown significant benefits in many practical applications. Real-world applications of swarm intelligence include disaster response and wildlife conservation. Swarm robots can collaborate to search for survivors, locate victims, and assess damage in hazardous environments during an earthquake or natural disaster. They can coordinate their movements and share data in real-time to increase their efficiency and effectiveness while guiding the survivors. In addition to tracking animal movements and behaviour, robots can guide animals to or away from specific areas. Sheep herding is a significant source of income in Australia that could be significantly enhanced if the human shepherd could be supported by single or multiple robots. Although the shepherding framework has become a popular SI mechanism, where a leading agent (sheepdog) controls a swarm of agents (sheep) to complete a task, controlling a swarm of agents is still not a trivial task, especially in the presence of some practical constraints. For example, most of the existing shepherding literature assumes that each swarm member has an unlimited sensing range to recognise all other members’ locations. However, this is not practical for physical systems. In addition, current approaches do not consider shepherding as a distributed system where an agent, namely a central unit, may observe the environment and commu- nicate with the shepherd to guide the swarm. However, this brings another hurdle when noisy communication channels between the central unit and the shepherd af- fect the success of the mission. Also, the literature lacks shepherding models that can cope with dynamic communication systems. Therefore, this thesis aims to design a multi-agent learning system for effective shepherding control systems in a partially observable environment under communication constraints. To achieve this goal, the thesis first introduces a new methodology to guide agents whose sensing range is limited. In this thesis, the sheep are modelled as an induced network to represent the sheep’s sensing range and propose a geometric method for finding a shepherd-impacted subset of sheep. The proposed swarm optimal herding point uses a particle swarm optimiser and a clustering mechanism to find the sheepdog’s near-optimal herding location while considering flock cohesion. Then, an improved version of the algorithm (named swarm optimal modified centroid push) is proposed to estimate the sheepdog’s intermediate waypoints to the herding point considering the sheep cohesion. The approaches outperform existing shepherding methods in reducing task time and increasing the success rate for herding. Next, to improve shepherding in noisy communication channels, this thesis pro- poses a collaborative learning-based method to enhance communication between the central unit and the herding agent. The proposed independent pre-training collab- orative learning technique decreases the transmission mean square error by half in 10% of the training time compared to existing approaches. The algorithm is then ex- tended so that the sheepdog can read the modulated herding points from the central unit. The results demonstrate the efficiency of the new technique in time-varying noisy channels. Finally, the central unit is modelled as a mobile agent to lower the time-varying noise caused by the sheepdog’s motion during the task. So, I propose a Q-learning- based incremental search to increase transmission success between the shepherd and the central unit. In addition, two unique reward functions are presented to ensure swarm guidance success with minimal energy consumption. The results demonstrate an increase in the success rate for shepherding