Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control

Distributed estimation of stochastic multiagent systems for cooperative control with a virtual network

This article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods

Extracorporeal cardiopulmonary resuscitation for adult out-of-hospital cardiac arrest patients: time-dependent propensity score-sequential matching analysis from a nationwide population-based registry

Background There is inconclusive evidence regarding the effectiveness of extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest (OHCA) patients. We aimed to evaluate the association between ECPR and neurologic recovery in OHCA patients using time-dependent propensity score matching analysis. Methods Using a nationwide OHCA registry, adult medical OHCA patients who underwent CPR at the emergency department between 2013 and 2020 were included. The primary outcome was a good neurological recovery at discharge. Time-dependent propensity score matching was used to match patients who received ECPR to those at risk for ECPR within the same time interval. Risk ratios (RRs) and 95% confidence intervals (CIs) were estimated, and stratified analysis by the timing of ECPR was also performed. Results Among 118,391 eligible patients, 484 received ECPR. After 1:4 time-dependent propensity score matching, 458 patients in the ECPR group and 1832 patients in the no ECPR group were included in the matched cohort. In the matched cohort, ECPR was not associated with good neurological recovery (10.3% in ECPR and 6.9% in no ECPR; RR [95% CI] 1.28 [0.85–1.93]). In the stratified analyses according to the timing of matching, ECPR with a pump-on within 45min after emergency department arrival was associated with favourable neurological outcomes (RR [95% CI] 2.51 [1.33–4.75] in 1–30min, 1.81 [1.11–2.93] in 31–45min, 1.07 (0.56–2.04) in 46–60min, and 0.45 (0.11–1.91) in over 60min). Conclusions ECPR itself was not associated with good neurological recovery, but early ECPR was positively associated with good neurological recovery. Research on how to perform ECPR at an early stage and clinical trials to evaluate the effect of ECPR is warranted

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Department of Mechanical EngineeringAdvancements in electronics, software, and sensors have led to various developments in unmanned aerial vehicles (UAVs), ranging from sophisticated models to simpler ones with lightweight avionics. Researchers have explored the use of multiple UAVs in a distributed multi-agent system (MAS) to overcome the limitations of a single UAV, thereby extending their capabilities. Utilizing multiple UAVs in a distributed MAS provides advantages such as improved efficiency, robustness, flexibility, and reliability. To fully benefit from a distributed MAS, cooperation and coordination among agents through local interactions are crucial. Flocking, a coordination strategy where agents exchange information with neighboring agents, enables a small team of operators to control multiple UAVs as a unified and stable unit, ensuring safe flights without collisions and continuous communication. However, in order to safely and effectively achieve a flocking state in practical situations, the flocking controller needs to take into account uncertainties in the environment, limitations of the agents involved, and intricate mission requirements. Therefore, the main goal of this thesis is to develop distributed flocking control algorithms that are both safe and efficient, and can be successfully implemented in real-world scenarios. The algorithm incorporates dynamic characteristics, such as applying the concept of inactivity inspired by the natural world, to achieve effective swarming in difficult scenarios with constraints, particularly for fixed-wing aircraft. Furthermore, optimization of flocking control parameters performs a safe and efficient execution of complex missions, including target tracking through flocking flight. In addition, the proposed method introduces an optimal flocking control approach that explicitly guarantees collision avoidance and optimizes flocking control performance. Unlike model predictive control, which calculates futures for collision avoidance, the proposed method employs a control barrier function to ensure collision avoidance based on the current state, efficiently generating optimal control inputs. Additionally, a distributed estimator is proposed to estimate the position and speed information of non-neighboring agents by observing only the position and velocity information of its neighbors in stochastic environments. This estimator contributes to the development of a flocking control algorithm that can achieve desired final flocking state. The effectiveness and performance of the proposed algorithms in achieving safe and efficient flocking control in various scenarios are demonstrated through numerical simulations.clos

Nonlinear Disturbance Observer-Based Standoff Target Tracking for Small Fixed-Wing UAVs

Small fixed-wing UAVs have attracted significant attention thanks to its pragmatic features such as small size, light weight, and low cost. Although many researchers have extensively studied small fixed-wing UAVs, there still remains a room for improvement. One of the improvements is to deal with external disturbances such as wind as small fixed-wing UAVs are susceptible to such external forces. This makes it difficult for UAVs to follow desired paths or moving targets, which may lead to a mission failure. In this paper, we present nonlinear disturbance observer-based standoff tracking guidance for multiple small fixed-wing UAVs in the presence of wind. The nonlinear disturbance observer is used to estimate the wind disturbance and the Lyapunov guidance vector field technique is used to compensate the effect of the wind and follow a ground target. The numerical simulations are carried out and outdoor flight experiments are performed to demonstrate the performance of the proposed approach in a real-world environment

Decentralized multiple V-formation control in undirected time-varying network topologies

It is well known that the V-shape formation of fixed-wing aerial vehicles can contribute to saving fuel consumption from its induced drag reduction. Although it has been widely adopted on various flight missions, the conventional formation method of forming a single V-formation has issues on robustness and scalability if large number of agents are involved. In this paper, we propose a decentralized and scalable approach capable of generating separated multiple V-shape formations for the undirected time-varying networked agents. Using a variation of the multi-consensus algorithm, a flock of agents can be divided into the desired number of subgroups regardless of the subgroup network topology if the algebraic connectivity of the entire flock is maintained. Numerical simulation results are provided to show the feasibility of the proposed formation control

Decentralized hybrid flocking guidance for a swarm of small UAVs

Flocking is defined as flying in groups without colliding into each other through data exchange where each UAV applies a decentralized algorithm. In this paper, hybrid flocking control is proposed by using three types of guidance methods: vector field, Cucker-Smale model, and potential field. Typically, hybrid flocking control using several methods can lead to generating conflicting commands and thus degrading the performance of the mission. To address this issue, the adaptive CuckerSmale model is proposed. Besides, we use social learning particle swarm optimization to determine the optimal weightings between guidance methods. It is verified through numerical simulations that the optimal weighting for missions such as loitering and target tracking results in effective flocking

Using Lazy Agents to Improve the Flocking Efficiency of Multiple UAVs

A group of agents can form a flock using the augmented Cucker-Smale (C-S) model. The model autonomously aligns them to a common velocity and maintains a relative distance among the agents in a distributed manner by sharing the information among neighbors. This paper introduces the concept of inactiveness to the augmented C-S model for improving the flocking performance. It involves controlling the energy and convergence time required to form a stable flock. Inspired by the natural world where a few lazy (or inactive) workers are helpful to the group performance in social insect colonies. In this study, we analyzed different levels of inactiveness as a degree of control input effectiveness for multiple fixed-wing UAVs in the flocking algorithm. To find the appropriate inactiveness level for each flock member, the particle swarm optimization-based approach is used as the first step, based on the initial condition of the flock. However, as the significant computational burden may cause difficulties in implementing the optimization-based approach in real time, we also propose a heuristic adaptive inactiveness approach, which changes the inactivity level of selected agents adaptively according to their position and heading relative to the flock center. The performance of the proposed approaches using the concept of lazy (or inactive) agents is verified with numerical simulations by comparing them with the conventional flocking algorithm in various scenarios