Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements

Shalaby, Mohammed; Cossette, Charles Champagne; Forbes, James Richard; Ny, Jérôme Le

doi:10.1109/lra.2021.3067253

Cited by 47 publications

(22 citation statements)

References 26 publications

(36 reference statements)

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“…In order to ensure observability of the anchor positions relative to the robot, three tags are placed on the robot [1], [21]. When Anchor i is within communication range with the robot, the proposed ranging protocol is shown in Figure 3.…”

Section: Ranging Protocolmentioning

confidence: 99%

“…Firstly, a ranging protocol is proposed to allow a 3-tag agent to synchronize the clocks of its tags and receive 3 range measurements from a fixed anchor with only one two-way ranging (TWR) transaction. This therefore allows necessary additional information for localization [21] without compromising the frequency in which range measurements are recorded. Secondly, a novel UWB-based T&R framework is presented for a vehicle moving in an environment with spaced-out fixed anchors at unknown locations over a large area.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-Wideband Teach and Repeat

Ayman¹,

Cossette²,

Ny³

et al. 2022

Preprint

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Autonomously retracing a manually-taught path is desirable for many applications, and Teach and Repeat (T&R) algorithms present an approach that is suitable for long-range autonomy. In this paper, ultra-wideband (UWB) ranging-based T&R is proposed for vehicles with limited resources. By fixing single UWB transceivers at far-apart unknown locations in an indoor environment, a robot with 3 UWB transceivers builds a locally consistent map during the teach pass by fusing the range measurements under a custom ranging protocol with an on-board IMU and height measurements. The robot then uses information from the teach pass to retrace the same trajectory autonomously. The proposed ranging protocol and T&R algorithm are validated in simulation, where it is shown that the robot can successfully retrace the taught trajectory with sub-metre tracking error.

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Section: Ranging Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Teach and Repeat

Ayman¹,

Cossette²,

Ny³

et al. 2022

Preprint

Self Cite

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“…External UWB position system based on fixed anchors can estimate positions of agents in the common reference frame [18], but the requirement of the previous deployment of anchors means the framework can hardly be adopted in the unknown environment [8]. Efforts have been made to design an anchorfree UWB position scheme, such as UWB-IMU coupled approach [22], [23]. However, due to the single-dimensional distance measurement, the accuracy of relative localization relying barely on UWB is not satisfactory [22], [23].…”

Section: Related Workmentioning

confidence: 99%

“…Efforts have been made to design an anchorfree UWB position scheme, such as UWB-IMU coupled approach [22], [23]. However, due to the single-dimensional distance measurement, the accuracy of relative localization relying barely on UWB is not satisfactory [22], [23]. To achieve better estimation, UWB measurements are fused together with measurements from other sensors, such as VIO [18], wheel encoder [24], and optical flow [25], [26].…”

Section: Related Workmentioning

confidence: 99%

Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization

Zhang¹,

Chen²,

Li³

et al. 2021

Preprint

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Relative localization is a prerequisite for the cooperation of aerial swarms. The vision-based approach has been investigated owing to its scalability and independence on communication. However, the limited field of view (FOV) inherently restricts the performance of vision-based relative localization. Inspired by bird flocks in nature, this letter proposes a novel distributed active vision-based relative localization framework for formation control in aerial swarms. Aiming at improving observation quality and formation accuracy, we devise graphbased attention planning (GAP) to determine the active observation scheme in the swarm. Then active detection results are fused with onboard measurements from UWB and VIO to obtain real-time relative positions, which further improve the formation control performance. Real-world experiments show that the proposed active vision system enables the swarm agents to achieve agile flocking movements with an acceleration of 4 m/s 2 in circular formation tasks. A 45.3% improvement of formation accuracy has been achieved compared with the fixed vision system.

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“…For example, cameras are often used in addition to UWB [3], but vision requires substantial computational capabilities, which imposes a lower bound on the size of the agent. In [4], the presence of some agents with two UWB tags makes the relative positions observable. The observability analysis provided by [5] concludes that when velocity information is available in addition to single-distance measurements, the relative position is observable provided that a persistency of excitation condition is satisfied, meaning that the agents must be in continuous relative motion.…”

mentioning

confidence: 99%

Relative Position Estimation Between Two UWB Devices With IMUs

Cossette

Shalaby

Saussié

et al. 2021

IEEE Robot. Autom. Lett.

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For a team of robots to work collaboratively, it is crucial that each robot have the ability to determine the position of their neighbors, relative to themselves, in order to execute tasks autonomously. This letter presents an algorithm for determining the three-dimensional relative position between two mobile robots, each using nothing more than a single ultrawideband transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding window filter estimates the relative position at selected keypoints by combining the distance measurements with acceleration estimates, which each agent computes using an on-board attitude estimator. The algorithm is appropriate for real-time implementation, and has been tested in simulation and experiment, where it comfortably outperforms standard estimators. A positioning accuracy of less than 1 meter is achieved with inexpensive sensors. Index Terms-Localization; Range Sensing; Multi-Robot Systems I. INTRODUCTIONT HE ability to determine the relative position between two robots, or agents, is needed in many applications. For instance, multi-robot tasks such as collaborative exploration and mapping, search and rescue, and formation control, each require relative position information. Access to inter-robot distance measurements is becoming increasingly accessible due to technologies such as ultra-wideband radio (UWB). UWB in particular is attractive due to its low cost, low power, high accuracy, and ability to function in GPS-denied environments. Such advantages have even warranted the presence of UWB transceivers in smartphones and smartwatches, which could be used to provide position information of one user relative to another or, more generally, any other UWB transceiver. However, determining a full three-dimensional relative position estimate from a single distance measurement is impossible. There is an infinite set of relative positions that will produce the same distance measurement. Hence, to realize an observable relative positioning solution, more information is required.

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Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements

Cited by 47 publications

References 26 publications

Ultra-Wideband Teach and Repeat

Ultra-Wideband Teach and Repeat

Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization

Relative Position Estimation Between Two UWB Devices With IMUs

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