Building a Bridge with Flying Robots

Mirjan, Ammar; Augugliaro, Federico; D’Andrea, Raffaello; Gramazio, Fabio; Köhler, Matthias

doi:10.1007/978-3-319-26378-6_3

Cited by 35 publications

(16 citation statements)

References 9 publications

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“…Kushleyev et al (2013) showed a swarm of 20 micro quadrotors that could re-organize in several formations. Lindsey et al (2012), Augugliaro et al (2014), and Mirjan et al (2016) developed impressive collaborative construction schemes using a team of MAVs. Preiss et al (2017) showcased "Crazyswarm, " an indoor display of 49 small quadrotors flying together.…”

Section: The Challenge Of Local Sensing and Controlmentioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.

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Section: The Challenge Of Local Sensing and Controlmentioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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“…StringNet is a closed net of strings formed by the defenders as shown in Figure 3. The strings are realized as impenetrable and extendable line barriers (e.g., spring-loaded pulley and a rope or other similar mechanism; Mirjan et al, 2016) that prevent attackers from passing through them. The extendable string barrier allows free relative motion of the two defenders connected by the string.…”

Section: Herding a Single Swarm Of Attackersmentioning

confidence: 99%

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

2021

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This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

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“…Mirjan et al [17] employ an interesting technique of displaying their drone build a bridge at both a normal speed and a sped up speed (video at [1]). The video first shows the drones operating at a regular pace, and then speed up the video to 20x, and then slow down the video back to real time at the end.…”

Section: Display Film Ratementioning

confidence: 99%

Steps Towards Best Practices For Robot Videos

Rosen¹,

Tellex²,

Konidaris³

2020

Preprint

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There are unwritten guidelines for how to make robot videos that researchers learn from their advisors and pass onto their students. We believe that it is important for the community to collaboratively discuss and develop a standard set of best practices when making robot. We suggest a starting set of maxims for best robot video practices, and highlight positive examples from the community and negative examples only from videos made by the authors of this article. In addition, we offer a checklist that we hope can act as an document that can be given to robotic researchers to inform them of how to make robot videos that truthfully characterize what a robot can and can not do. We consider this a first draft, and are looking for feedback from the community as we refine and grow our maxims and checklist.

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Building a Bridge with Flying Robots

Cited by 35 publications

References 9 publications

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

Steps Towards Best Practices For Robot Videos

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