Navigating A Mobile Robot Using Switching Distributed Sensor Networks

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation- and communication-constrained setting.We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. The architecture has three major advantages over existing approaches: First, in each task only one DSN is active for sensing and estimating the robot state, and for different tasks the robot can switch the active DSN by taking resource saving and system performance into account; Second, the robot only needs to communicate with one active sensor at each time to obtain its state information from the active DSN; Third, no online optimization is required. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. Under proper conditions, we prove that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences respectively, which play an essential role in the event-triggered condition. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robots localization and navigation are provided to validate the proposed architecture.

Remote State Estimation of Multiple Systems over Multiple Markov Fading Channels

We consider remote state estimation of multiple discrete-time linear time-invariant (LTI) systems over multiple wireless time-varying communication channels. Each system state is measured by a sensor, and the measurements from sensors are sent to a remote estimator over the shared wireless channels in a scheduled manner. We answer the following open problem: what is the fundamental requirement on the multi-sensor-multi-channel system to guarantee the existence of a sensor scheduling policy that can stabilize the remote estimation system? To tackle the problem, we propose a novel policy construction method, and develop a new analytical approach by applying the asymptotic theory of spectral radii of products of non-negative matrices. A necessary and sufficient stability condition is derived in terms of the LTI system parameters and the channel statistics, which is more effective than existing sufficient conditions available in the literature. Explicit scheduling policies with stability guarantees are presented as well. We further extend the analytical framework to cover remote estimation with four alternative network setups and obtain corresponding necessary and sufficient stability conditions.

Resolving the Complex Evolution of a Supermassive Black Hole Triplet in a Cosmological Simulation

We present here a self-consistent cosmological zoom-in simulation of a triple supermassive black hole (SMBH) system forming in a complex multiple galaxy merger. The simulation is run with an updated version of our code KETJU, which is able to follow the motion of SMBHs down to separations of tens of Schwarzschild radii while simultaneously modeling the large-scale astrophysical processes in the surrounding galaxies, such as gas cooling, star formation, and stellar and AGN feedback. Our simulation produces initially a SMBH binary system for which the hardening process is interrupted by the late arrival of a third SMBH. The KETJU code is able to accurately model the complex behavior occurring in such a triple SMBH system, including the ejection of one SMBH to a kiloparsec-scale orbit in the galaxy due to strong three-body interactions as well as Lidov-Kozai oscillations suppressed by relativistic precession when the SMBHs are in a hierarchical configuration. One pair of SMBHs merges $sim 3,mathrm{Gyr}$ after the initial galaxy merger, while the remaining binary is at a parsec-scale separation when the simulation ends at redshift $z=0$. We also show that KETJU can capture the effects of the SMBH binaries and triplets on the surrounding stellar population, which can affect the binary merger timescales as the stellar density in the system evolves. Our results demonstrate the importance of dynamically resolving the complex behavior of multiple SMBHs in galactic mergers, as such systems cannot be readily modeled using simple orbit-averaged semi-analytic models.

Robust satisfiability check and online control synthesis for uncertain systems under signal temporal logic specifications

This paper studies the robust satisfiability check and online control synthesis problems for uncertain discrete-time systems subject to signal temporal logic (STL) specifications. Different from existing techniques, this work proposes an approach based on STL, reachability analysis, and temporal logic trees. Firstly, a real-time version of STL semantics and a tube-based temporal logic tree are proposed. We show that such a tree can be constructed from every STL formula. Secondly, using the tube-based temporal logic tree, a sufficient condition is obtained for the robust satisfiability check of the uncertain system. When the underlying system is deterministic, a necessary and sufficient condition for satisfiability is obtained. Thirdly, an online control synthesis algorithm is designed. It is shown that when the STL formula is robustly satisfiable and the initial state of the system belongs to the initial root node of the tube-based temporal logic tree, it is guaranteed that the trajectory generated by the controller satisfies the STL formula. The effectiveness of the proposed approach is verified by an automated car overtaking example.

Fast Quantized Average Consensus over Static and Dynamic Directed Graphs

In this paper we study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. Specifically, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (i.e., the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). The main idea of the proposed algorithm is that each node (i) models its initial state as two quantized fractions which have numerators equal to the nodes initial state and denominators equal to one, and (ii) transmits one fraction randomly while it keeps the other stored. Then, every time it receives one or more fractions, it averages their numerators with the numerator of the fraction it stored, and then transmits them to randomly selected out-neighbors. We characterize the properties of the proposed distributed algorithm and show that its execution, on any static and strongly connected digraph, allows each agent to reach in finite time a fixed state that is equal (within one quantisation level) to the average of the initial states. We extend the operation of the algorithm to achieve finite-time convergence in the presence of a dynamic directed communication topology subject to some connectivity conditions. Finally, we provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithm. We compare against state-of-the-art quantized average consensus algorithms and show that our algorithms convergence speed significantly outperforms most existing protocols.

Distributed Event-Triggered Algorithms for Finite-Time Privacy-Preserving Quantized Average Consensus

In this paper, we consider the problem of privacy preservation in the average consensus problem when communication among nodes is quantized. More specifically, we consider a setting where some nodes in the network are curious but not malicious and they try to identify the initial states of other nodes based on the data they receive during their operation (without interfering in the computation in any other way), while some nodes in the network want to ensure that their initial states cannot be inferred exactly by the curious nodes. We propose two privacy-preserving event-triggered quantized average consensus algorithms that can be followed by any node wishing to maintain its privacy and not reveal the initial state it contributes to the average computation. Every node in the network (including the curious nodes) is allowed to execute a privacy-preserving algorithm or its underlying average consensus algorithm. Under certain topological conditions, both algorithms allow the nodes who adopt privacypreserving protocols to preserve the privacy of their initial quantized states and at the same time to obtain, after a finite number of steps, the exact average of the initial states.

Distributed control under compromised measurements:Resilient estimation, attack detection, and vehicle platooning

We study how to design a secure observer-based distributed controller such that a group of vehicles can achieve accurate state estimates and formation control even if the measurements of a subset of vehicle sensors are compromised by a malicious attacker. We propose an architecture consisting of a resilient observer, an attack detector, and an observer-based distributed controller. The distributed detector is able to update three sets of vehicle sensors: the ones surely under attack, surely attack-free, and suspected to be under attack. The adaptive observer saturates the measurement innovation through a preset static or time-varying threshold, such that the potentially compromised measurements have limited influence on the estimation. Essential properties of the proposed architecture include: 1) The detector is fault-free, and the attacked and attack-free vehicle sensors can be identified in finite time; 2) The observer guarantees both real-time error bounds and asymptotic error bounds, with tighter bounds when more attacked or attack-free vehicle sensors are identified by the detector; 3) The distributed controller ensures closed-loop stability. The effectiveness of the proposed methods is evaluated through simulations by an application to vehicle platooning.

Secure Platooning of Autonomous Vehicles Under Attacked GPS Data

In this paper, we study how to secure the platooning of autonomous vehicles when an unknown vehicle is under attack and bounded system uncertainties exist. For the attacked vehicle, its position and speed measurements from GPS can be manipulated arbitrarily by a malicious attacker. First, to find out which vehicle is under attack, two detectors are proposed by using the relative measurements (by camera or radar) and the local innovation obtained through measurements from neighboring vehicles. Then, based on the results of the detectors, we design a local state observer for each vehicle by applying a saturation method to the measurement innovation. Moreover, based on the neighbor state estimates provided by the observer, a distributed controller is proposed to achieve the consensus in vehicle speed and keep fixed desired distance between two neighboring vehicles. The estimation error by the observer and the platooning error by the controller are shown to be asymptotically upper bounded under certain conditions. The effectiveness of the proposed methods is also evaluated in numerical simulations.

The GRIFFIN project -- Formation of star clusters with individual massive stars in a simulated dwarf galaxy starburst

We describe a population of young star clusters (SCs) formed in a hydrodynamical simulation of a gas-rich dwarf galaxy merger resolved with individual massive stars at sub-parsec spatial resolution. The simulation is part of the GRIFFIN (Galaxy Realizations Including Feedback From INdividual massive stars) project. The star formation environment during the simulation spans seven orders of magnitude in gas surface density and thermal pressure, and the global star formation rate surface density ($Sigma_mathrm{SFR}$) varies by more than three orders of magnitude during the simulation. Young SCs more massive than $M_{mathrm{*,cl}}sim 10^{2.5},M_{odot}$ form along a mass function with a power-law index $alphasim-1.7$ ($alphasim-2$ for $M_{mathrm{*,cl}}gtrsim10^{3},M_{odot}$) at all merger phases, while the normalization and the highest SC masses (up to $sim 10^6 M_{odot}$) correlate with $Sigma_mathrm{SFR}$. The cluster formation efficiency varies from $Gammasim20%$ in early merger phases to $Gammasim80%$ at the peak of the starburst and is compared to observations and model predictions. The massive SCs ($gtrsim10^4,M_{odot}$) have sizes and mean surface densities similar to observed young massive SCs. Simulated lower mass clusters appear slightly more concentrated than observed. All SCs form on timescales of a few Myr and lose their gas rapidly resulting in typical stellar age spreads between $sigmasim0.1-2$ Myr ($1sigma$), consistent with observations. The age spreads increase with cluster mass, with the most massive cluster ($sim10^6, M_{odot}$) reaching a spread of $5, mathrm{Myr}$ once its hierarchical formation finishes. Our study shows that it is now feasible to investigate the SC population of entire galaxies with novel high-resolution numerical simulations.

Performance Characterization Using AoI in a Single-loop Networked Control System

The joint design of control and communication scheduling in a Networked Control System (NCS) is known to be a hard problem. Several research works have successfully designed optimal sampling and/or control strategies under simplified communication models, where transmission delays/times are negligible or fixed. However, considering sophisticated communication models, with random transmission times, result in highly coupled and difficult-to-solve optimal design problems due to the parameter inter-dependencies between estimation/control and communication layers. To tackle this problem, in this work, we investigate the applicability of Age-of-Information (AoI) for solving control/estimation problems in an NCS under i.i.d. transmission times. Our motivation for this investigation stems from the following facts: 1) recent results indicate that AoI can be tackled under relatively sophisticated communication models, and 2) a lower AoI in an NCS may result in a lower estimation/control cost. We study a joint optimization of sampling and scheduling for a single-loop stochastic LTI networked system with the objective of minimizing the time-average squared norm of the estimation error. We first show that under mild assumptions on information structure the optimal control policy can be designed independently from the sampling and scheduling policies. We then derive a key result that minimizing the estimation error is equivalent to minimizing a function of AoI when the sampling decisions are independent of the state of the LTI system. Noting that minimizing the function of AoI is a stochastic combinatorial optimization problem and is hard to solve, we resort to heuristic algorithms obtained by extending existing algorithms in the AoI literature. We also identify a class of LTI system dynamics for which minimizing the estimation error is equivalent to minimizing the expected AoI.