Pheromone-based Swarming for Position-less MAVs

Unlike existing aerial swarm systems, we aim at developing algorithms which do not require global or relative positioning information concerning agents and their neighbors. This alleviates the need for sensors which require calibration, are expensive and heavy or unusable because of environmental constraints. Rather than positioning, MAVs rely only on simple sensors (magnetic compass, speed sensor and altitude sensor) and local communication with neighbors. Our endeavor is motivated by an application whereby Micro Air Vehicles (MAVs) must organize autonomously to establish a robust communication network between users located on ground. Such a system is aimed towards the rapid and easy deployment of communication networks in disaster areas

Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks

UAV networks consisting of reduced size, weight, and power (low SWaP) fixed-wing UAVs are used for civilian and military applications such as search and rescue, surveillance, and tracking. To carry out these operations efficiently, there is a need to develop scalable, decentralized autonomous UAV network architectures with high network connectivity. However, the area coverage and the network connectivity requirements exhibit a fundamental trade-off. In this paper, a connectivity-aware pheromone mobility (CAP) model is designed for search and rescue operations, which is capable of maintaining connectivity among UAVs in the network. We use stigmergy-based digital pheromone maps along with distance-based local connectivity information to autonomously coordinate the UAV movements, in order to improve its map coverage efficiency while maintaining high network connectivity

Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

Due to growing endurance, safety and non-invasivity, small drones can be increasingly experimented in unstructured environments. Their moderate computing power can be assimilated into swarm coordination algorithms, performing tasks in a scalable manner. For this purpose, it is challenging to investigate the use of biologically-inspired mechanisms. In this paper the focus is on the coordination aspects between small drones required to perform target search. We show how this objective can be better achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone senses a potential target, by releasing digital pheromone on its location. Multiple pheromone deposits are aggregated, increasing in intensity, but also diffused, to be propagated to neighborhood, and lastly evaporated, decreasing intensity in time. As a consequence, pheromone intensity creates a spatiotemporal attractive potential field coordinating a swarm of drones to visit a potential target. Flocking occurs when drones are spatially organized into groups, whose members have approximately the same heading, and attempt to remain in range between them, for each group. It is an emergent effect of individual rules based on alignment, separation and cohesion. In this paper, we present a novel and fully decentralized model for target search, and experiment it empirically using a multi-agent simulation platform. The different combination strategies are reviewed, describing their performance on a number of synthetic and real-world scenarios

Investigating Multiple Pheromones in Swarm Robots - A Case Study of Multi-Robot Deployment

Social insects are known as the experts in handling complex task in a collective smart way although their small brains contain only limited computation resources and sensory information. It is believed that pheromones play a vital role in shaping social insects' collective behaviours. One of the key points underlying the stigmergy is the combination of different pheromones in a specific task. In the swarm intelligence field, pheromone inspired studies usually focus one single pheromone at a time, so it is not clear how effectively multiple pheromones could be employed for a collective strategy in the real physical world. In this study, we investigate multiple pheromone-based deployment strategy for swarm robots inspired by social insects. The proposed deployment strategy uses two kinds of artificial pheromones; the attractive and the repellent pheromone that enables micro robots to be distributed in desired positions with high efficiency. The strategy is assessed systematically by both simulation and real robot experiments using a novel artificial pheromone platform ColCOSΦ. Results from the simulation and real robot experiments both demonstrate the effectiveness of the proposed strategy and reveal the role of multiple pheromones. The feasibility of the ColCOSΦ platform, and its potential for further robotic research on multiple pheromones are also verified. Our study of using different pheromones for one collective swarm robotics task may help or inspire biologists in real insects' research

UAV based distributed automatic target detection algorithm under realistic simulated environmental effects

Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air [1]. Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification, and tracking objects of interest. These trends have been paralleled by advances in both distributed detection [2], image/signal processing and data fusion techniques [3]. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, we investigate the effects of environmental conditions on target detection performance in a UAV network. We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to generate synthetic images. The automatic target detector is a cascade of classifiers based on Haar-like features. The detector\u27s performance is evaluated using simulated images that closely mimic data acquired in a UAV network under realistic camera and environmental conditions. In order to improve automatic target detection (ATD) performance in a swarmed UAV system, we propose and design several fusion techniques both at the image and score level and analyze both the case of a single observation and the case of multiple observations of the same target

Ant-based Swarming with Positionless Micro Air Vehicles for Communication Relay

Swarming without positioning information is interesting in application- oriented systems because it alleviates the need for sensors which are dependent on the environment, expensive in terms of energy, cost, size and weight, or unusable at useful ranges for real-life scenarios. This principle is applied to the development of a swarm of micro air vehicles (SMAVs) for the deployment of ad hoc wireless communication networks (SMAVNETs) between ground users in disaster areas. Rather than relying on positioning information, MAVs rely on local communication with immediate neighbors and proprioceptive sensors which provide heading, speed and altitude. To solve the challenging task of designing agent controllers to achieve the swarm behavior of the SMAVNET, inspiration is taken from army ants which are capable of laying and maintaining pheromone paths leading from their nest to food sources in nature. This is analogous to the deployment of communication pathways between multiple ground users. However, instead of being physically deposited in the air or on a map, pheromone is virtually deposited on the MAVs using local communication. This approach is investigated in 3D simulation in a simplified scenario with two ground users

Agent Environments for Multi-agent Systems – A Research Roadmap

Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems (E4MAS 2004-2006) emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled. © Springer International Publishing Switzerland 2015.Peer reviewe

Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

Sensor networks are being used to implement different types of sophisticated emerging applications, such as those aimed at supporting ambient intelligence and surveillance systems. This usage is enhanced by employing sensors with different characteristics in terms of sensing, computing and mobility capabilities, working cooperatively in the network. However, the design and deployment of these heterogeneous systems present several issues that have to be handled in order to meet the user expectations. The main problems are related to the nodes‘ interoperability and the overall resource allocation, both inter and intra nodes. The first problem requires a common platform that abstracts the nodes’ heterogeneity and provides a smooth communication, while the second is handled by cooperation mechanisms supported by the platform. Moreover, as the nodes are supposed to be heterogeneous, a customizable platform is required to support both resource rich and poorer nodes. This paper analyses surveillance systems based on a heterogeneous sensor network, which is composed by lowend ground sensor nodes and autonomous aerial robots, i.e. Unmanned Aerial Vehicles (UAVs), carrying different kinds of sensors. The approach proposed in this work tackles the two above mentioned problems by using a customizable hardware platform and a middleware to support interoperability. Experimental results are also provided

Investigation of Multi-Robots Food Foraging Efficiency with an Artificial Pheromone System

In nature, the pheromone released by social insects is crucial for communication, which has become a rich inspiration source of swarm robotics. By utilising the virtual pheromone in physical swarm robot system, we can coordinate individuals and simulate behaviours of social insects. This thesis aims to investigate two influences, i.e., the leader and the wind effects on multi-robots’ food foraging efficiency in an artificial pheromone system, wherein the pheromone is represented by light spots or trails on a TV screen. To investigate the leader effect, we remotely controlled a robot agent as a leader to guide other wandering agents to reach a food source with persistent virtual pheromone and then aggregate around it; the released pheromone by the leader could be sensed by other mates so that triggering following behaviour. We compare the aggregation efficiency with the scenarios without a leader robot agent. After that, we simulated wind effects on the virtual pheromone affecting its evaporation and diffusion. The experimental results demonstrate that without interacting with the leader, the aggregation efficiency is highly depending on start positions of follower agents within each experiment. The potential of using the leader interaction with the other robots can improve the swarm efficiency under the same experimental setting. Moreover, the experimenting results of wind effects on the artificial pheromone system and the food foraging simulation demonstrate the wind has the power to influence the food foraging efficiency, which cannot be ignored. This research indicates that the leader and the wind effects are important factors affecting the pheromone-based swarm efficiency