Distributed Range-Based Relative Localization of Robot Swarms

Cornejo, Alejandro; Nagpal, Radhika

doi:10.1007/978-3-319-16595-0_6

Cited by 40 publications

(27 citation statements)

References 30 publications

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“…σ Q v , σ Q ψ , and σ Q z were then tuned higher (to 0.5) to enhance the difference, while staying within the order of magnitude of the expected standard deviations of the measurements. This filter is limited by flip and rotation ambiguity as defined by Cornejo and Nagpal (2015). When the motion of R j perfectly matches the motion of R i , range-only measurements remain constant and are not informative for bearing estimation.…”

Section: Localization Via Fusion Of Range and On-board Statesmentioning

confidence: 99%

On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams

et al. 2018

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To avoid collisions, Micro Air Vehicles (MAVs) flying in teams require estimates of their relative locations, preferably with minimal mass and processing burden. We present a relative localization method where MAVs need only to communicate with each other using their wireless transceiver. The MAVs exchange on-board states (velocity, height, orientation) while the signal strength indicates range. Fusing these quantities provides a relative location estimate. We used this for collision avoidance in tight areas, testing with up to three AR.Drones in a 4 m × 4 m area and with two miniature drones (≈ 50 g) in a 2 m × 2 m area. The MAVs could localize each other and fly several minutes without collisions. In our implementation, MAVs communicated using Bluetooth antennas. The results were robust to the high noise and disturbances in signal strength. They could improve further by using transceivers with more accurate signal strength readings.

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Section: Localization Via Fusion Of Range and On-board Statesmentioning

confidence: 99%

On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams

et al. 2018

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“…The logic behind (15) is that even though the matrices U τ are "wrong", the product U τ l X (τ l ) is correct thanks to the anchors. With sufficiently many marginal estimates, there is a unique set of polynomial trajectories passing through them.…”

Section: B Unknown Spectral Factor (Practical Algorithm)mentioning

confidence: 99%

“…Localization of dynamic point sets from distance measurements finds applications whenever objects move. Robot swarms, for example, often must localize autonomously [15], especially in remote situations such as extraterrestrial exploration [16] or deep-water missions [17]. Related applications exist in environmental monitoring, for example for dynamic sensor networks composed of river-borne sensing nodes [18].…”

Section: Introductionmentioning

confidence: 99%

Kinetic Euclidean Distance Matrices

Tabaghi

Vetterli

2020

IEEE Trans. Signal Process.

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Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize points from a snapshot of distances. If the objects move, one expects to do better by modeling the motion. In this paper, we introduce Kinetic Euclidean Distance Matrices (KEDMs)-a new kind of time-dependent distance matrices that incorporate motion. The entries of KEDMs become functions of time, the squared time-varying distances. We study two smooth trajectory models-polynomial and bandlimited trajectoriesand show that these trajectories can be reconstructed from incomplete, noisy distance observations, scattered over multiple time instants. Our main contribution is a semidefinite relaxation (SDR), inspired by SDRs for static EDMs. Similarly to the static case, the SDR is followed by a spectral factorization step; however, because spectral factorization of polynomial matrices is more challenging than for constant matrices, we propose a new factorization method that uses anchor measurements.Extensive numerical experiments show that KEDMs and the new semidefinite relaxation accurately reconstruct trajectories from noisy, incomplete distance data and that, in fact, motion improves rather than degrades localization if properly modeled. This makes KEDMs a promising tool for problems in geometry of dynamic points sets.

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“…Displacement of robots during this time interval adds an accumulative noise to the system. One solution is to consider robots to be immobile intermittently and serve as beacons for the other mobile one [29].…”

Section: Cooperative Self-organized Localizationmentioning

confidence: 99%

“…Recently, Cornejo and Nagpal [29] proposed an algorithm for relative localization and local coordination of a swarm of simple robots capable of measuring their distance to their close-by neighbours. In their research, similar to this work, some robots move toward the goal and the others stay immobile, providing mobile robots with positional information.…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Self-Organized Coordinated Motion of a Swarm of Minimalist Robots

Shirazi

Jin

2017

IEEE Trans. Emerg. Top. Comput. Intell.

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Abstract-Minimalist robots are functionally highly restricted but well suited for swarm robotic applications because of their low costs and small sizes. Connectivity maintenance and collision avoidance are challenging in minimalist swarm robotic systems due to a short communication range and the lack of positional and directional sensing. In this paper, we introduce a selforganizing control strategy for collective flocking of a swarm of minimalist robots with an aim to improve swarm connectivity and to reduce the chance of collision between robots. Based on the relative positional information built up via collaborations, each robot determines a collision-free operational polygon. This scheduling scheme coordinates the motion of the robots by dividing them into one group of immobile and one group of mobile robots, such that each mobile robot is surrounded by immobile robots serving as beacons. In addition, we introduce a cohesive force into motion planning, which has been shown to play an important role in maintaining a swarm during flocking. A new quantitative metric is introduced for measuring the connectivity of a swarm of agents with local communications, thereby evaluating the performance of the proposed control scheme. We run extensive simulations using simulated Kilobts to examine the influence of different sources of noise and the size of swarms on the connectivity in the swarm and the speed of flocking. Finally, we implement the proposed algorithm on a swarm of real Kilobots to compare the flocking performance with and without the proposed control strategy for coordinated and collective motion.

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Distributed Range-Based Relative Localization of Robot Swarms

Cited by 40 publications

References 30 publications

On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams

On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams

Kinetic Euclidean Distance Matrices

A Strategy for Self-Organized Coordinated Motion of a Swarm of Minimalist Robots

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