Underwater Robot Swarms: Challenges and Opportunities

doi:10.1201/b14908-21

Cited by 13 publications

(7 citation statements)

References 141 publications

(235 reference statements)

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“…Coordinated flight of multiple drones is another instance of reactive autonomy, but raises it additional challenges in sensing, communication and control 73 . Although very little is known about the sensing and control that underlies coordinated flight in insects, it has been shown that cohesion in flocks of starlings is the result of individual birds aligning their trajectories with a small number of their nearest neighbours defined in topological -not metric -space 74 .…”

Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,047

552

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Drones, a popular nickname for unmanned aerial vehicles and micro aerial vehicles, often conjure up images of unmanned aeroplanes that fly thousands of miles for espionage and to deploy munitions. However, over the past few years, an increasing number of public and private research laboratories have been working on small, human-friendly drones that one day may autonomously fly in confined spaces and in close proximity to people. The development of these small drones, which is the main focus of this Review, has been supported by the miniaturization and cost reduction of electronic components (microprocessors, sensors, batteries and wireless communication units), largely driven by the portable electronic device industry. These improvements have enabled the prototyping and commercialization of small (typically less than 1 kg) drones at smartphone prices.Small drones will have important socio-economic impacts (Fig. 1). Images from drones that are capable of flying a few metres above the ground will fill a gap between expensive, weather-dependent and lowresolution images provided by satellites and car-based images limited to human-level perspectives and the availability of accessible roads. Specialized flying cameras and cloud-based data analytics will allow farmers to continuously monitor the quality of crop growth. Such platforms will enable construction companies to measure work progress in real time. Drones will let mining companies obtain precise volumetric data of excavations. Energy and infrastructure companies will be able to exhaustively survey pipelines, roads and cables. Humanitarian organizations could immediately assess and adapt aid efforts in continuously changing refugee camps. Transportation drones that are capable of safely taking off and landing in the proximity of buildings and humans will allow developing countries -without a suitable road network -to rapidly deliver goods and to finally unleash the full potential of their e-commerce telecommunication infrastructure. Transportation drones will also help developed countries to improve the quality of service in congested or remote areas, and will enable rescue organizations to quickly deliver medical supplies in the field and on demand. Inspection drones that are capable of flying in confined spaces will help fire-fighting and emergency units to assess dangers faster and more safely, logistic companies to detect cracks in the inner and outer shells of ships, road maintenance companies to measure signs of wear and tear in bridges and tunnels, security companies to improve building safety by monitoring areas outside the range of surveillance cameras, and disaster mitigation agencies to inspect partially collapsed buildings where ground clutter is an obstacle for terrestrial robots. Coordinated teams of autonomous drones will enable missions that last longer than the flight time of a single drone by allowing some drones to temporarily leave the team for battery replacement. Drone teams will permit rescue organizations to quickly deploy dedicated commu...

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Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,047

552

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“…C OLLECTIVE motion of animal groups such as flocks of birds is an awe-inspiring natural phenomenon that has profound implications for the field of aerial swarm robotics [1], [2]. Animal groups in nature operate in a completely self-organized manner since the interactions between them are purely local.…”

Section: Introductionmentioning

confidence: 99%

Learning Vision-Based Flight in Drone Swarms by Imitation

Schilling

Lecoeur

Schiano

et al. 2019

IEEE Robot. Autom. Lett.

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Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers.

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“…Although single-UAV systems have already transitioned from research labs out into field experiments and numerous real-world applications, the same cannot be said of collective UAV systems (Zufferey, Hauert, Stirling, Leven, & Roberts, 2010). So far, experiments with simultaneous operation of two or more real UAVs have been reported in only a few cases (Beard, Mclain, Nelson, Kingston, & Johanson, 2006;Cole, Göktogan, & Sukkarieh, 2006;Teo, Jang, & Tomlin, 2005).…”

Section: Introductionmentioning

confidence: 99%

Dealing with midair collisions in dense collective aerial systems

Leven

Zufferey

Floreano

2011

Journal of Field Robotics

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The idea of creating collective aerial systems is appealing because several rather simple flying vehicles could join forces to cover a large area in little time in applications such as monitoring, mapping, search and rescue, or airborne communication relays. In most of these scenarios, a fleet of cooperating vehicles is dispatched to a confined airspace area and requested to fly close to a nominal altitude. Moreover, depending on the task each vehicle is assigned to, individual flight trajectories in this essentially two-dimensional space may interfere, resulting in disastrous collisions. This paper begins by introducing a probabilistic model to predict the rate of midair collisions that would occur if nothing is done to prevent them. In a second step, a control strategy for midair collision avoidance is proposed, which is interesting because it requires only local communication and information about flight altitudes. The proposed strategy is systematically analyzed in theory and simulation as well as in experiments with five physical aerial vehicles. A significant reduction in collision rates can be achieved. Statistically, values close to zero are possible when the swarm's density is below an applicationdependent threshold. Such low collision rates warrant an acceptable level of confidence in collision-free operation of a physical swarm. C 2011 Wiley Periodicals, Inc.

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Underwater Robot Swarms: Challenges and Opportunities

Cited by 13 publications

References 141 publications

Science, technology and the future of small autonomous drones

Science, technology and the future of small autonomous drones

Learning Vision-Based Flight in Drone Swarms by Imitation

Dealing with midair collisions in dense collective aerial systems

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