Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent

This work considers the problem of passively monitoring multiple moving targets with a single unmanned aerial vehicle (UAV) agent equipped with a direction-finding radar. This is in general a challenging problem due to the unobservability of the target states, and the highly non-linear measurement process. In addition to these challenges, in this work we also consider: a) environments with multiple obstacles where the targets need to be tracked as they manoeuvre through the obstacles, and b) multiple false-alarm measurements caused by the cluttered environment. To address these challenges we first design a model predictive guidance controller which is used to plan hypothetical target trajectories over a rolling finite planning horizon. We then formulate a joint estimation and control problem where the trajectory of the UAV agent is optimized to achieve optimal multi-target monitoring

IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL

We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications. In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition. Specifically, each agent plans inspections and repairs for a specific system component, aiming to minimise maintenance costs while cooperating to minimise system failure risk. With IMP-MARL, we release several environments including one related to offshore wind structural systems, in an effort to meet today's needs to improve management strategies to support sustainable and reliable energy systems. Supported by IMP practical engineering environments featuring up to 100 agents, we conduct a benchmark campaign, where the scalability and performance of state-of-the-art cooperative MARL methods are compared against expert-based heuristic policies. The results reveal that centralised training with decentralised execution methods scale better with the number of agents than fully centralised or decentralised RL approaches, while also outperforming expert-based heuristic policies in most IMP environments. Based on our findings, we additionally outline remaining cooperation and scalability challenges that future MARL methods should still address. Through IMP-MARL, we encourage the implementation of new environments and the further development of MARL methods

Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning

In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface area of an object of interest. The proposed approach integrates ray-tracing into the coverage planning process, thus allowing the agent to identify which parts of the scene are visible at any point in time. The problem of integrated ray-tracing and coverage planning control is first formulated as a constrained optimal control problem (OCP), which aims at determining the agent's optimal control inputs over a finite planning horizon, that minimize the coverage time. Efficiently solving the resulting OCP is however very challenging due to non-convex and non-linear visibility constraints. To overcome this limitation, the problem is converted into a Markov decision process (MDP) which is then solved using reinforcement learning. In particular, we show that a controller which follows an optimal control law can be learned using off-policy temporal-difference control (i.e., Q-learning). Extensive numerical experiments demonstrate the effectiveness of the proposed approach for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09 December 2022, Cancun, Mexic

Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.Comment: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 202

Geomiso TNL: A Software for Non-Linear Static T-Spline-Based Isogeometric Analysis of Complex Multi-Patch Structures

A new software, Geomiso TNL, is proposed to facilitate the use of isogeometric analysis and 3D design with NURBS and T-splines. Its dual nature eliminates geometric errors by merging geometric design with mesh generation into a single procedure. It is based on the isogeometric method, the powerful generalization of the traditional finite element method. This paper presents four sample applications in non-linear solid and structural mechanics. This software is seen to handle these situations remarkably well, as the numerical examples exhibit significantly improved accuracy of the results, such as displacement, strain and stress fields, and reduced computational cost when compared with finite element analysis. It is argued that Geomiso TNL is a new, more efficient, alternative to finite element software packages and possesses several advantages

IL-4 receptor engagement in human neutrophils impairs their migration and extracellular trap formation

Background Type 2 immunity serves to resist parasitic helminths, venoms, and toxins, but the role and regulation of neutrophils during type 2 immune responses are controversial. Helminth models suggested a contribution of neutrophils to type 2 immunity, whereas neutrophils are associated with increased disease severity during type 2 inflammatory disorders, such as asthma. Objective We sought to evaluate the effect of the prototypic type 2 cytokines IL-4 and IL-13 on human neutrophils. Methods Human neutrophils from peripheral blood were assessed without or with IL-4 or IL-13 for (1) expression of IL-4 receptor subunits, (2) neutrophil extracellular trap (NET) formation, (3) migration toward CXCL8 in vitro and in humanized mice, and (4) CXCR1, CXCR2, and CXCR4 expression, as well as (5) in nonallergic versus allergic subjects. Results Human neutrophils expressed both types of IL-4 receptors, and their stimulation through IL-4 or IL-13 diminished their ability to form NETs and migrate toward CXCL8 in vitro. Likewise, in vivo chemotaxis in NOD-scid-Il2rg−/− mice was reduced in IL-4–stimulated human neutrophils compared with control values. These effects were accompanied by downregulation of the CXCL8-binding chemokine receptors CXCR1 and CXCR2 on human neutrophils on IL-4 or IL-13 stimulation in vitro. Ex vivo analysis of neutrophils from allergic patients or exposure of neutrophils from nonallergic subjects to allergic donor serum in vitro impaired their NET formation and migration toward CXCL8, thereby mirroring IL-4/IL-13–stimulated neutrophils. Conclusion IL-4 receptor signaling in human neutrophils affects several neutrophil effector functions, which bears important implications for immunity in type 2 inflammatory disorders