Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

Vedder, Benjamin; Eriksson, Henrik; Skarin, Daniel; Vinter, Jonny; Jönsson, Magnus

doi:10.1109/icuas.2015.7152291

Cited by 9 publications

(1 citation statement)

References 15 publications

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“…An impressive recent display of this in the real world was put into practice by Vásárhelyi et al ( 2018 ), who programmed a swarm of 30 MAVs to flock. The same concept can be applied to indoor environments if pre-fitted with, for example: external markers (Pestana et al, 2014 ), motion-tracking cameras (Kushleyev et al, 2013 ), antenna beacons (Ledergerber et al, 2015 ; Guo et al, 2016 ), or ultra sound beacons (Vedder et al, 2015 ). However, this dependency on external infrastructure limits the swarm to being operable only in areas that have been properly fitted to the task.…”

Section: Intra-swarm Relative Sensing and Collision Avoidancementioning

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: Intra-swarm Relative Sensing and Collision Avoidancementioning

confidence: 99%

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

et al. 2020

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Approach to UAV Swarm Control and Collision-Free Reconfiguration

Izhboldina

Lebedev

Shabanova

2020

Proceedings of 15th International Conference on Electromechanics and Robotics "Zavalishin's Readings"

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Three-dimensional relative localization and synchronized movement with wireless ranging

Pfeiffer

Munaro

et al. 2022

Swarm Intell

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Relative localization is a key capability for autonomous robot swarms, and it is a substantial challenge, especially for small flying robots, as they are extremely restricted in terms of sensors and processing while other robots may be located anywhere around them in threedimensional space. In this article, we generalize wireless ranging-based relative localization to three dimensions. In particular, we show that robots can localize others in three dimensions by ranging to each other and only exchanging body velocities and yaw rates. We perform a nonlinear observability analysis, investigating the observability of relative locations for different cases. Furthermore, we show both in simulation and with real-world experiments that the proposed method can be used for successfully achieving various swarm behaviours. In order to demonstrate the method's generality, we demonstrate it both on tiny quadrotors and lightweight flapping wing robots.

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Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

Cited by 9 publications

References 15 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

Approach to UAV Swarm Control and Collision-Free Reconfiguration

Three-dimensional relative localization and synchronized movement with wireless ranging

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