Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform

In this paper, a hierarchical multi-UAV simulation platform,called XTDrone, is designed for UAV swarms, which is completely open-source 4 . There are six layers in XTDrone: communication, simulator,low-level control, high-level control, coordination, and human interac-tion layers. XTDrone has three advantages. Firstly, the simulation speedcan be adjusted to match the computer performance, based on the lock-step mode. Thus, the simulations can be conducted on a work stationor on a personal laptop, for different purposes. Secondly, a simplifiedsimulator is also developed which enables quick algorithm designing sothat the approximated behavior of UAV swarms can be observed inadvance. Thirdly, XTDrone is based on ROS, Gazebo, and PX4, andhence the codes in simulations can be easily transplanted to embeddedsystems. Note that XTDrone can support various types of multi-UAVmissions, and we provide two important demos in this paper: one is aground-station-based multi-UAV cooperative search, and the other is adistributed UAV formation flight, including consensus-based formationcontrol, task assignment, and obstacle avoidance.Comment: 12 pages, 10 figures. And for the, see https://gitee.com/robin_shaun/XTDron

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour

EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model

Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of over-smoothing and under-reaching to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, mis-simplification, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines

Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors; the other agents utilize lowaccuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results corroborate the effectiveness of our proposed algorithm and verify our method can significantly improve the estimation accuracy.Comment: 15 pages 6 figure

Intelligent selection of NEO deflection strategies under uncertainty

This paper presents an Intelligent Decision Support System (IDSS) that can automatically assess the suitable robust deflection strategies to respond to an asteroid impact scenario. The input to the IDSS is the warning time, the orbital parameters and mass of the asteroid and the corresponding uncertainties. The output is the deflection strategies that are more likely to offer a successful deflection. Both aleatory and epistemic uncertainties on ephemerides and physical properties of the asteroid are considered. The training data set is produced by generating thousands of virtual impactors, sampled from the current distribution of Near Earth Objects (NEO). For each virtual impactor we perform a robust optimisation, under mixed aleatory/epistemic uncertainties, of the deflection scenario with different deflection strategies. The robust performance indices is considered by the deflection effectiveness, which is quantified by Probability of Collision post deflection. The IDSS is based on a combination Dempster-Shafer theory of evidence and a Random Forest classifier that is trained on the data set of virtual impactors and deflection scenarios. Five deflection strategies are modelled and included in the IDSS: Nuclear Explosion, Kinetic Impactor, Laser Ablation, Gravity Tractor and Ion Beam Shepherd. Simulation results suggest that the proposed decision support system can quickly provide robust decisions on which deflection strategies are to be chosen to respond to a NEO impact scenario. Once trained the IDSS does not require re-running expensive simulations to make decisions on which deflection strategies are to be used and is, therefore, suitable for the rapid pre-screening or reassessment of deflection options

Intelligent decision support system for planetary defense under mixed aleatory/epistemic uncertainties

This paper studies the application of Machine Learning techniques in Planetary Defense. To quickly respond to an asteroid impact scenario, an Intelligent Decision Support System is proposed to automatically decide if a deflection mission is necessary, and then select the most effective deflection strategy. This system consists of two sub-systems: the first one is named as Asteroid Impact Scenarios Identifier, and the second one is named as Asteroid Deflection Strategies Selector. The input to the Asteroid Impact Scenarios Identifier is the warning time, the orbital parameters and the diameter of the asteroid and the corresponding uncertainties. According to the Probability of Collision and the corresponding confidence, the output is the decision of action: the deflection is needed, no deflection is needed, or more measurements need to be obtained before making any decision. If the deflection is needed, the Asteroid Deflection Strategies Selector is activated to output the most efficient deflection strategy that offers the highest probability of success. The training dataset is produced by generating thousands of virtual impact scenarios, sampled from the real distribution of Near-Earth Objects. A robust optimization is performed, under mixed aleatory/epistemic uncertainties, with five different deflection strategies (Nuclear Explosion Device, Kinetic Impactor, Laser Ablation, Gravity Tractor and Ion Beam Shepherd). The robust performance indices are considered as the deflection effectiveness, which is quantified by the change of impact probability pre and post deflection. We demonstrate the capabilities of Random Forest, Deep Neural Networks and Convolutional Neural Networks at classifying impact scenarios and deflection strategies. Simulation results suggest that the proposed system can quickly provide decisions to respond to an asteroid impact scenario. Once trained, the Intelligent Decision Support System, does not require re-running expensive simulations and is, therefore, suitable for the rapid prescreening deflection options