Relative Distance Constraints Research Articles

Adaptive formation control architectures for a team of quadrotors with multiple performance and safety constraints

AbstractIn this work, we propose a novel adaptive formation control architecture for a group of quadrotor systems, under line‐of‐sight (LOS) distance and relative distance constraints as well as attitude constraints, where the constraint requirements can be both asymmetric and time‐varying in nature. The LOS distance constraint consideration ensures that each quadrotor is not deviating too far away from its desired flight trajectory. The LOS relative inter‐quadrotor distance constraint is to guarantee that the LOS distance between any two quadrotors in the formation is neither too large (which may result in the loss of communication between quadrotors, for example) nor too small (which may result in collision between quadrotors, for example). The attitude constraints make sure that the roll, pitch, and yaw angles of each quadrotor do not deviate too much from the desired profile. Universal barrier functions are adopted in the controller design and analysis, which is a generic framework that can address system with different types of constraints in a unified controller architecture. Furthermore, each quadrotor's mass and inertia are unknown, and the system dynamics are subjected to time‐varying external disturbances. Through rigorous analysis, an exponential convergence rate can be guaranteed on the distance and attitude tracking errors, while all constraints are satisfied during the operation. A simulation example further demonstrates the efficacy of the proposed control framework.

Weight Factor Graph Co-Location Method for UAV Formation Based on Navigation Performance Evaluation

At present, multiple unmanned aerial vehicles (UAVs) collaboration have attracted people’s more and more attention. The abundant relative distance information in UAV cooperative network can be employed to further improve UAV’s positioning accuracy. However, the traditional distance-based cooperative localization (CL) algorithm does not consider the performance of cooperative navigation information. Cooperative information with poor navigation performance will affect follower UAV’s state estimation accuracy. Thus, this paper proposes a weight factor graph CL method based on navigation performance evaluation. A novel navigation performance evaluation strategy based on Fisher information and relative entropy is proposed. The distance observations and cooperative nodes’ positions with higher estimation accuracy and contribution are selected to estimate follower UAV’s position. Considering the difference contributions of the selected cooperative information to follower UAV’s state estimation, we employ the information weight processing to achieve higher state estimation accuracy. Based on the selected cooperative information and weight coefficient, a multi-UAVs cooperative optimization graph model based on navigation performance is constructed. The global optimization cost function is constructed using relative distance constraints from the current time to historical time in the sliding window. The global optimal navigation solution of follower UAV is achieved using iterative weighted least squares method. Simulation and experiment results show that the proposed navigation performance evaluation strategy can effectively screen cooperative information with higher estimation accuracy and contribution for CL. Using the selected high-quality cooperative information, the system computational complexity is reduced without significantly affecting system positioning accuracy.

A Cooperative Localization Method for Leader–Follower Multiple UAVs Using Continuous Relative Ranging Information

Cooperative localization (CL) utilizing multiple unmanned aerial vehicles (UAVs) has become the current development trend and research hotspot. The traditional distance-based CL method typically uses principle of the spherical intersection to achieve UAV high-precision positioning. However, this distance-based CL strategy requires several leader UAVs at the known positions. Thus, a cooperative localization method for leader–follower multiple UAVs using continuous relative ranging information is proposed in this paper. Aiming at the UAV formation consists of a leader UAV node with the known position and at least a follower UAV that needs to be located. Using the Inertial short-time high-precision relative position constraint and the continuous relative distance constraints with the leader UAV nodes, the closed-form solution of follower UAV’s three-dimensional position can be obtained using spherical intersection principle. On this basis, we derive and construct follower UAV’s position observation equation. The developed strategy requires a single known position leader UAV node to realize high-precision CL, which can effectively solve the traditional distance-based collaborative location problem in case the number of leader UAVs is small. The simulation and experimental results demonstrate that the proposed method significantly improves follower UAV’s positioning accuracy, and neglects the limitation of the traditional distance-based CL method on the number of leader UAV nodes at known positions.

Distributed Formation Control Using Artificial Potentials and Neural Network for Constrained Multiagent Systems

In this brief, we focus on the study of formation tracking problem for a class of multiagent systems with nonlinear dynamics and external disturbances in the presence of relative distance constraints. A novel distributed formation control strategy is proposed based on an integration of radial basis function neural network (NN) with artificial potential field method. The relative distance constraints between arbitrary adjacent agents can be ensured by the artificial potential function. Based on the NN approximation property, it has been proposed to neutralize the nonlinear dynamics in agents. To account for the negative influence of the approximation error and external disturbances, a robustness term is employed. Finally, based on algebraic graph theory, matrix theory, and Barbalat’s lemma, some sufficient conditions are established to accomplish the asymptotical stability of the systems for a given communication graph. The study is with application to tethered space net robot. The simulation results are performed to illustrate the performance of the proposed strategy.

Unsupervised Transfer Learning via Relative Distance Comparisons

Primitive machine learning method such as Support Vector Machine (SVM) or k-Nearest Neighbor (k-NN) faces a major challenge when its training and test data is distributed with large-scale variations in lighting conditions, color, backgrounds, size, etc. The variation may be because training and testing data can come from related but some other domains. Considerable efforts have been made in the development of transfer learning methods. However, most current work focuses only on the following goals or objectives: i)Preservation of source domain discriminative information with Linear Discriminant Analysis (LDA); ii)Maximization of target domain variance; iii) Subspace alignment; iv) Minimization of marginal and conditional distribution by using the Maximum Mean Discrepancy (MMD) criterion;v)Preservation of original similarity of the data samples. Current approaches to preserve source domain discriminant information can easily misclassify the target domain samples which are distributed near the edge of the cluster. In order to overcome the limitations of existing transfer learning methods, we propose a novel Unsupervised Transfer Learning Via Relative Distance Comparisons (UTRDC) method. UTRDC optimizes all the aforementioned objectives jointly with a common projection vector matrix for both domains as well as uses the relative distance constraints for better inter-class separability and intra-class compactness. Furthermore, we extend our proposed method UTRDC to a kernelized version to deal with non-linear separable datasets. Extensive experimentation on two real-world problems datasets (PIE face and Office+Caltech) has proven that the proposed methods outperform several approaches to non-transfer learning and transfer learning.

Self-triggered adaptive control for multi-agent systems with timed constraints and connectivity maintenance

Abstract This paper presents a distributed control strategy for a multi-agent system commanded by a set of leaders that has to accomplish a high-level plan consisting of a sequence of tasks specified by a state-space region and a timed constraint. The agents are also subject to relative-distance constraints with its neighbors. The solution consists in an adaptive distributed mechanism to update the feedback gains for the leader agents, which is executed following a self-triggered algorithm. The results show how the proposed approach provides less conservative results than if feedback gains are held constant, and are illustrated with a simulation example.

Semi-supervised orthogonal discriminant analysis with relative distance : integration with a MOO approach

In discriminant analysis, trace ratio is an important criterion for minimizing the between-class similarity and maximizing the within-class similarity, simultaneously. In brief, we address the trace ratio problem associated with many semi-supervised discriminant analysis algorithms as they use the normal Euclidean distances between training data samples. Based on this problem, we propose a new semi-supervised orthogonal discriminant analysis technique with relative distance constraints called SSODARD. Different from the existing semi-supervised dimensionality reduction algorithms, our algorithm is more consistent in propagating the label information from the labeled data to the unlabeled data because of the use of relative distance function instead of normal Euclidean distance function. For finding this appropriate relative distance function, we use pairwise constraints generated from labeled data and satisfy them using Bregman projection. Since the projection is not orthogonal, we require an appropriate subset of constraints. In order to select such a subset of constraints, we further develop a framework called MO-SSODARD, which uses evolutionary algorithm while optimizing various validity indices simultaneously. The experimental results on various datasets show that our proposed approaches are superior than the state-of-the-art discriminant algorithms with respect to various validity indices.

A kernel semi-supervised distance metric learning with relative distance: Integration with a MOO approach

Metric learning, which aims to determine an appropriate distance function to measure the similarity and dissimilarity between data points accurately, is one of the most popular methods to enhance the performance of many machine learning methods such as K-means clustering and K nearest neighbor classifier algorithms. These algorithms may not perform well because of the use of normal Euclidean distance function that ignores any statistical regularities that might be estimated from a large training set of labeled examples. In many real-world applications, the Euclidean distance may not be fit to capture the intrinsic similarity and dissimilarity between the data points. Compared to existing metric learning algorithms, which use large amount of labeled data in the form of must-link (ML) and cannot-link constraints as side information where the granularity of true clustering is unknown, our proposed approach uses few labeled data in the form of relative-distance constraints such as equality constraints, Ceq, and inequality constraints, Cneq. For satisfying such constraints, we need to project the initial Euclidean distance matrix by using Bregman projection on the convex subset of constraints in such a way that all the constraints are satisfied. Since Bregman projection is not orthogonal, means while satisfying the current constraint previously satisfied constraints may get unsatisfied, we need to select a proper subset of constraints for learning better distance function. The multi-objective framework is utilized for selecting a good subset of constraints which can help in getting the proper labeling of the data set. The selected subset of constraints is used for adjusting the initial kernel-matrix. K-means clustering technique is applied to the adjusted kernel matrix to label the data set. In order to evaluate the quality of obtained labeling, different external and internal cluster validity indices are deployed. The values of these indices are simultaneously optimized using the search capability of MOO with the aim of selecting the appropriate subset of constraints. The proposed approach is evaluated on UCI Human Activity Recognition using Smartphone Dataset v1.0 along with nine other popular data sets. Results show that our approach outperforms the state of the art semi-supervised metric learning algorithms with respect to different internal and external cluster validity indices.

A New Transfer Learning Algorithm in Semi-Supervised Setting

Transfer Learning is an effective method of dealing with real-world problems where the training and test data are drawn from different distributions. Transfer learning methods use a labeled source domain to boost the task in a target domain that may be unsupervised or semi-supervised. However, the previous transfer learning algorithms use Euclidean distance or Mahalanobis distance formula to represent the relationships between instances and to try and capture the geometry of the manifold. In many real-world scenarios, this is not enough and these functions fail to capture the intrinsic geometry of the manifold that the data exists in. In this paper, we propose a transfer learning framework called Semi-Supervised Metric Transfer Learning with Relative Constraints (SSMTR), that uses distance metric learning with a set of relative distance constraints that capture the similarities and dissimilarities between the source and the target domains better. In SSMTR, instance weights are learned for different domains which are then used to reduce the domain shift while a Relative Distance metric is learned in parallel. We have developed SSMTR for classification problems as well, and have conducted extensive experiments on several real-world datasets; particularly, the PIE Face, Office-Caltech, and USPS-MNIST datasets to verify the accuracy of our proposed algorithm when compared to the current transfer learning algorithms.

Loosely Formation-Displaced Geostationary Orbit Optimization with Complex Hybrid Sail Propulsion

To explore the performance of hybrid sail and overcome the congestion of geostationary orbit, this work proposes a method intended to optimize the trajectories of the spacecraft formation and extend the concept of displaced geostationary orbit by loosening the relative distance and introducing a station-keeping box. The multispacecraft formation is a typical complex system with nonlinear dynamics, and the hybrid propulsion system introduces additional complexity. To solve this problem, suboptimal trajectories with constant relative distance constraints are first found with inverse methods, which were referred to as ideal displaced geostationary orbits. Then, the suboptimal trajectories are used as a first guess for a direct optimization algorithm based on Gauss pseudospectral algorithm, which loosens the relative distance constraints and allows the spacecraft to be placed anywhere inside the station-keeping box. The optimization results show that the loosely formation and station-keeping box can create more flexible trajectories and achieve higher efficiency of the hybrid sail propulsion system, which can save about 40% propellant consumption.

Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints

Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features)), which may suffer from poor matching speed and are not well represented in the literature. Here, we propose a novel Local Deep Hashing Matching (LDHM) method for matching of aerial images with large size and with lower complexity or fast matching speed. The basic idea of the proposed algorithm is to utilize the deep network model in the local area of the aerial images, and study the local features, as well as the hash function of the images. Firstly, according to the course overlap rate of aerial images, the algorithm extracts the local areas for matching to avoid the processing of redundant information. Secondly, a triplet network structure is proposed to mine the deep features of the patches of the local image, and the learned features are imported to the hash layer, thus obtaining the representation of a binary hash code. Thirdly, the constraints of the positive samples to the absolute distance are added on the basis of the triplet loss, a new objective function is constructed to optimize the parameters of the network and enhance the discriminating capabilities of image patch features. Finally, the obtained deep hash code of each image patch is used for the similarity comparison of the image patches in the Hamming space to complete the matching of aerial images. The proposed LDHM algorithm evaluates the UltraCam-D dataset and a set of actual aerial images, simulation result demonstrates that it may significantly outperform the state-of-the-art algorithm in terms of the efficiency and performance.

Communication-Free Multi-Agent Control Under Local Temporal Tasks and Relative-Distance Constraints

We propose a distributed control and coordination strategy for multi-agent systems where each agent has a local task specified as a Linear Temporal Logic (LTL) formula and at the same time is subject to relative-distance constraints with its neighboring agents. The local tasks capture the temporal requirements on individual agents' behaviors, while the relative-distance constraints impose requirements on the collective motion of the whole team. The proposed solution relies only on relative-state measurements among the neighboring agents without the need for explicit information exchange. It is guaranteed that the local tasks given as syntactically co-safe or general LTL formulas are fulfilled and the relative-distance constraints are satisfied at all time. The approach is demonstrated with computer simulations.

A novel coarse-to-fine method for registration of multispectral images

Due to non-linear intensity changes between multispectral images, the existed descriptors often yield low matching performance. In order to build reliable keypoint mappings on multispectral images, a novel coarse-to-fine method is designed using projective transformation and the information of edge overlap. The method consists of a coarse process and a fine-tuning process. In the coarse process, initial keypoint mappings are built with the descriptors associated with keypoints and the relative distance constraints are employed on them to remove outliers. In the fine-tuning process, the edge overlap information is utilized as similarity metric and an iterative framework is applied to search correct keypoint mappings. The performance of the proposed is investigated with keypoints extracted by speeded-up robust features. The experiment results show that the proposed method can build more reliable keypoint mappings on multispectral images than existed methods.

Optimal On–Off Cooperative Maneuvers for Long-Term Satellite Cluster Flight

When a group of satellites is equipped with a particularly simple propulsion system (e.g., cold-gas thrusters), constraints on the thrust level and total propellant mass renders cluster keeping extremely challenging. This is even more pronounced in disaggregated space architectures, in which a satellite is formed by clustering a number of heterogonous free-flying modules. The research described in this paper develops guidance laws aimed at keeping the relative distances between the cluster modules bounded for long mission lifetimes, typically more than a year, while using constant-magnitude low thrust, with a characteristic on–off profile. A cooperative guidance law capable of cluster establishment and maintenance under realistic environmental perturbations is developed. The guidance law is optimized for fuel consumption, subject to relative distance constraints. Some of the solutions found to the optimal guidance problem require only a single maneuver arc to keep the cluster within relatively close distances for an entire year.

Decentralized controllers for shape generation with robotic swarms

SUMMARYWe address the synthesis of controllers for a swarm of robots to generate a desired two-dimensional geometric pattern specified by a simple closed planar curve with local interactions for avoiding collisions or maintaining specified relative distance constraints. The controllers are decentralized in the sense that the robots do not need to exchange or know each other's state information. Instead, we assume that the robots have sensors allowing them to obtain information about relative positions of neighbors within a known range. We establish stability and convergence properties of the controllers for a certain class of simple closed curves. We illustrate our approach through simulations and consider extensions to more general planar curves.

NF-κB-inducible BCL-3 Expression Is an Autoregulatory Loop Controlling Nuclear p50/NF-κB1 Residence

NF-kappa B is a transcription factor whose nuclear residence is controlled by I kappa B family members. In the NF-kappa B-I kappa B autoregulatory loop, activated (nuclear) Rel A.NF-kappa B1 induces the resynthesis of I kappa B alpha recapturing nuclear Rel A back into the cytoplasm within 1 h of stimulation. In contrast, NF-kappa B1 subunits redistribute more slowly into the cytoplasm (from 6 to 12 h). Here we examine the role of inducible cytoplasmic BCL-3 expression in terminating nuclear NF-kappa B1. Although BCL-3 is a nuclear protein in B lymphocytes, surprisingly, BCL-3 is primarily a cytoplasmic protein in HepG2 cells. Cytoplasmic BCL-3 abundance is induced 6-12 h after tumor necrosis factor-alpha stimulation where it complexes with NF-kappa B1 homodimers. Moreover, BCL-3 mRNA and protein expression are induced by NF-kappa B-activating agents. Two observations are interpreted to indicate that bcl-3 is transactivated by NF-kappa B/Rel A: 1) expression of a dominant negative NF-kappa B inhibitor blocks tumor necrosis factor-alpha-induced BCL-3 expression and 2) expression of constitutively active Rel A is sufficient to induce BCL-3 expression. In gene transfer studies, we identify two high affinity NF-kappa B-binding sites, kappa B1 (located at -872 to -861 nucleotides) and kappa B2 (-106 to -96 nucleotides), and although both bind with high affinity to Rel A, only kappa B2 is required for NF-kappa B-dependent induction of the native BCL-3 promoter. Down-regulation of BCL-3 induction results in prolonged, enhanced NF-kappa B1 binding and increased NF-kappa B-dependent transcription. Together, these data suggest the presence of an NF-kappa B-BCL-3 autoregulatory loop important in terminating NF-kappa B1 action and that individual NF-kappa B isoforms are actively terminated through coordinate induction of inhibitory I kappa B molecules to restore cellular homeostasis.