3-D Distributed Localization With Mixed Local Relative Measurements

Xu, Fang; Li, Xiaolei; Xie, Lihua

doi:10.1109/tsp.2020.3029399

Cited by 19 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9a-c depict the simulation results of periodic switching, while Figure 9d-f show the results of random switching. Among them, (a), (d), and (c), (f) represent the corresponding error estimation curves under the methods in paper [30] and paper [31], respectively. (b) and (e) depict the error curves corresponding to the method proposed in this article.…”

Section: Results Of Rl Fusion Estimation Simulationsmentioning

confidence: 99%

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors

Guo,

Gan,

2024

Sensors

View full text Add to dashboard Cite

Relative localization (RL) and circumnavigation is a highly challenging problem that is crucial for the safe flight of multi-UAVs (multiple unmanned aerial vehicles). Most methods depend on some external infrastructure for positioning. However, in some complex environments such as forests, it is difficult to set up such infrastructures. In this paper, an approach to infrastructure-free RL estimations of multi-UAVs is investigated for circumnavigating a slowly drifting UGV0 (unmanned ground vehicle 0), where UGV0 serves as the RL and circumnavigation target. Firstly, a discrete-time direct RL estimator is proposed to ascertain the coordinates of each UAV relative to the UGV0 based on intelligent sensing. Secondly, an RL fusion estimation method is proposed to obtain the final estimate of UGV0. Thirdly, an integrated estimation control scheme is also proposed for the application of the RL fusion estimation method to circumnavigation. The convergence and the performance are analyzed. The simulation results validate the effectiveness of the proposed algorithm for RL fusion estimations and of the integrated scheme.

show abstract

Section: Results Of Rl Fusion Estimation Simulationsmentioning

confidence: 99%

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors

Guo,

Gan,

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The displacement parameters h ij0 , • • • , h ij d in (8) describe the time-varying geometric relationship among the real-time positions of agent i and its d+1 neighbors j 0 , • • • , j d , which can be calculated by relative positions, bearings, distances, angles, ratio-of-distances, or their combination among agent i and its d+1 neighbors j 0 , • • • , j d (the details can be found in Appendix and [24]). From (8), it has…”

Section: Displacement Constraint and Design Of Target Formation A Dis...mentioning

confidence: 99%

“…where h ii (t) = j∈Ni h ij (t), and the displacement parameters h ij (t), j ∈ N i can be calculated by the relative positions, bearings, distances, angles, ratio-of-distances, or their combination among agent i and its d + 1 neighbors j 0 , • • • , j d ∈ N i (the details can be found in Appendix and [24]). Under Assumption 1, we can conclude from (3) and Lemma 1 that h ii (t) ̸ = 0 if its d+1 neighbors j 0 , • • • , j d ∈ N i reach their target positions, and then (36) can be rewritten as…”

Section: Maneuvering Mode and Maintaining Modementioning

confidence: 99%

“…If the multi-agent system is equipped with mixed types of measurements, i.e., some agents can measure only distances, while others can measure only local relative positions, local bearings, angles, or ratio-of-distances, the displacement constraint can also be obtained. The method of how to obtain the displacement constraint based on mixed measurements can be found in Section IV-B of [24]. Other measurements such as acoustic waves [31] and measured absolute outputs [32] of the agents will be considered in the near future.…”

Section: F Mixed-measurement-based Displacement Constraintmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Formation Maneuver Control of Multiagent Systems Over Directed Graphs

Xie

2022

IEEE Trans. Cybern.

Self Cite

View full text Add to dashboard Cite

This paper studies the problem of integrated distributed network localization and formation maneuver control. We develop an integrated relative-measurement-based scheme, which only uses relative positions, distances, bearings, angles, ratio-of-distances, or their combination to achieve distributed network localization and formation maneuver control in R d (d ≥ 2). By exploring the localizability and invariance of the target formation, the scale, rotation, and translation of the formation can be controlled simultaneously by only tuning the leaders' positions, i.e., the followers do not need to know parameters of the scale, rotation, and translation of the target formation. The proposed method can globally drive the formation errors to zero in finite time over multi-layer d+1-rooted graphs. A simulation example is given to illustrate the theoretical results.

show abstract

“…In each round, four vectors are obtained by synchronization, so it converges within 4Hn rounds when A is non-singular. In actual applications, there may exist measurement noise so that A and b are influenced and the constructed linear system may be unsolvable [9]. Let A and b denote the noisy matrices as:…”

Section: Analysis Of Dcgmentioning

confidence: 99%

DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving

Ping¹,

Wang²,

Li³

2021

Preprint

View full text Add to dashboard Cite

Distributed algorithms to solve linear equations in multi-agent networks have attracted great research attention and many iteration-based distributed algorithms have been developed. The convergence speed is a key factor to be considered for distributed algorithms, and it is shown dependent on the spectral radius of the iteration matrix. However, the iteration matrix is determined by the network structure and is hardly pre-tuned, making the iterative-based distributed algorithms may converge very slowly when the spectral radius is close to 1. In contrast, in centralized optimization, the Conjugate Gradient (CG) is a widely adopted idea to speed up the convergence of the centralized solvers, which can guarantee convergence in fixed steps. In this paper, we propose a general distributed implementation of CG, called DCG. DCG only needs local communication and local computation, while inheriting the characteristic of fast convergence. DCG guarantees to converge in 4Hn rounds, where H is the maximum hop number of the network and n is the number of nodes. We present the applications of DCG in solving the least square problem and network localization problem. The results show the convergence speed of DCG is three orders of magnitude faster than the widely used Richardson iteration method.

show abstract

3-D Distributed Localization With Mixed Local Relative Measurements

Cited by 19 publications

References 21 publications

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors

Distributed Formation Maneuver Control of Multiagent Systems Over Directed Graphs

DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving

Contact Info

Product

Resources

About