A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

Monica, Stefania; Ferrari, Gianluigi

doi:10.1007/978-3-319-16549-3_8

Cited by 3 publications

(1 citation statement)

References 18 publications

(21 reference statements)

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“…However [16] validated that particle swarm optimization (PSO) could be used for slower real time applications and argued that it should be used if robustness were required. PSO has also been validated in several other experiments for localization, where it is found to be a good candidate for ill-conditioned problems [17], [18]. By localizing in real-time using PSO an update rate slightly above 1Hz have been achieved previously by Qin et al [17], which is significantly improved in this paper.…”

Section: B Positional Estimationsupporting

confidence: 51%

A low cost omnidirectional relative localization sensor for swarm applications

Kohlbacher

Eliasson

Acres

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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By enabling coordinated task execution and movement, robotic swarms can achieve efficient exploration of unknown environments. In this paper, we propose a relative localization sensor system using Ultra-wideband (UWB) radio technology for ranging. This system is lightweight and relatively indifferent to the types of surrounding environments. Infrastructure dependency such as the requirement of beacons at known locations is eliminated by making an array of sensors on a swarm agent. In this paper, a novel algorithm is implemented on hardware with limited resources and compared to a more traditional trilateration approach. Both utilize Particle Swarm Optimization (PSO) to be more robust against noise and to achieve similar accuracy. The experimental results show that the proposed algorithm runs up to ten times faster than the existing trilateration approach. The sensor array which forms the localization system weighs only 56g, and achieves around 0.5m RMSE with a 10Hz update rate. Experiments show that the accuracy can be further improved if the rotational bias observed in the UWB devices are compensated for.

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Section: B Positional Estimationsupporting

confidence: 51%