Cooperative Multi-UAV Collision Avoidance Based on Distributed Dynamic Optimization and Causal Analysis

Lao, Mingrui; Tang, Jun

doi:10.3390/app7010083

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Dangerous areas, such as hostile radar detection regions, peaks, etc., are designated as no-fly zones. Bayesian rule is used to update whether a cell exists a target [26]. The update equations for the probability that a target exists but is not detected and that the target does not exist but is reported are written as follows:…”

Section: Probability Map Updatementioning

confidence: 99%

Coevolution Pigeon-Inspired Optimization with Cooperation-Competition Mechanism for Multi-UAV Cooperative Region Search

Luo

Shao

et al. 2019

Applied Sciences

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In this paper, a dynamic two-stage closed search (DTSCS) scheme for the unmanned aerial vehicle (UAV) cooperative region search is designed, which satisfies the range constraint (RC) and orientation constraint (OC). The closed trajectory is composed of two coupling stages, the search stage and the return stage. The position and orientation at the end of the search stage are the starting cell and orientation of the return stage. In the first stage, a coevolution pigeon-inspired optimization (CPIO) algorithm based on the cooperation-competition mechanism is proposed for multi-UAV cooperative search. In the return stage, inspired by region searching and trajectory tracking, a search tracking (ST) approach is presented to obtain the lowest-cost path under OC. The simulation results show that: (i) N p = 5 is the best prediction time step. (ii) CPIO algorithm performs better than the compared intelligent algorithms in region searching. (iii) ST has high tracking performance than other algorithms. (iv) The DTSCS scheme enables every UAV to make the best use of its fuel to cover more region and return to the airport within the RC, and the average range utilization of UAVs is 97% under the 3OC.

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Section: Probability Map Updatementioning

confidence: 99%

Coevolution Pigeon-Inspired Optimization with Cooperation-Competition Mechanism for Multi-UAV Cooperative Region Search

Luo

Shao

et al. 2019

Applied Sciences

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“…The dynamic system using a discrete time simulation has numerous applications. For example, a compartment dynamic model was simulated to describe the dynamic response and distribution characteristics of the gasifier [21], and trajectories of unmanned aerial vehicles were modeled for collision avoidance [22]. In this paper, the maneuvering behaviors of a UUV are modeled based on the DTSS, which is a generalized form of a Mealy system [18].…”

Section: Dynamic Systemmentioning

confidence: 99%

Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework

Seo

Kim

2017

Applied Sciences

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Simulation analysis has been performed for simulation experiments of all possible input combinations as a "what-if" analysis, which causes the simulation to be extremely time-consuming. To resolve this problem, this paper proposes a multi-fidelity modeling framework for enhancing simulation speed while minimizing simulation accuracy loss. A target system for this framework is a discrete event dynamic system. The dynamic property of the system facilitates the development of variable fidelity models for the target system due to its high computational cost; and the discrete event property allows for determining when to change the fidelity within a simulation scenario. For formal representation, the paper defines several key concepts such as an interest region, a fidelity change condition, and a selection model. These concepts are integrated into the framework to allow for the achievement of a condition-based disjunction of high-and low-fidelity simulations within a scenario. The proposed framework is applied to two case studies: unmanned underwater and urban transportation vehicles. The results show that simulation speed increases at least 1.21 times with a 5% accuracy loss. We expect that the proposed framework will resolve a computationally expensive problem in the simulation analysis of discrete event dynamic systems.

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“…The avoidance of obstacles by unmanned aerial vehicles is examined in [ 27 ]. In [ 28 ], the methods of conflict identification and resolution for a multi-UAV collision-avoidance system are investigated. Therefore, a modified technique is given for tentacle construction and collision avoidance for multiple UAVs operating in unstructured environments.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Choutri

Lagha

Meshoul

et al. 2022

Sensors

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Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

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Cooperative Multi-UAV Collision Avoidance Based on Distributed Dynamic Optimization and Causal Analysis

Cited by 13 publications

References 21 publications

Coevolution Pigeon-Inspired Optimization with Cooperation-Competition Mechanism for Multi-UAV Cooperative Region Search

Coevolution Pigeon-Inspired Optimization with Cooperation-Competition Mechanism for Multi-UAV Cooperative Region Search

Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

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