Two-Layer Optimization to Cooperative Conflict Detection and Resolution for UAVs

Yang, Jian; Yin, Dong; Cheng, Qiao; Xie, Xu

doi:10.1109/itsc.2015.335

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…The graph G ( t ) = ( V , E ( t ) ) is used to determine the relevant multi-UAV conflict, where V = is the vertex set and E ( t ) = { ( j , i ) | c ij = 1 } is the corresponding edge set. 37 The multi-UAV conflict matrix ( CM ) is derived by the adjacency matrix of G(t). CM = {cm(i,j)=1 if e ij ( t ) ∈ E ( t ) , else cm(i,j) = 0| i , j ∈ n }.…”

Section: Problem Formulationmentioning

confidence: 99%

Online cooperative airspace conflict resolution of unmanned aerial vehicles by space mapping based iterative search method

Zhao

Wang

Yang

et al. 2022

Advances in Mechanical Engineering

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This paper presents an investigation into the locally centralized conflict resolution problem of unmanned aerial vehicles (UAVs), in which the headings are adjustable variables. Firstly, the geometric characteristics of the conflict resolution problem in two-dimensional space are studied, and the problem is reduced into a nonlinear programing model. Secondly, the nonlinear safe separation constraint is transferred into linear style in the sine value space using the space mapping method. The feasible region of two conflict-related UAVs is transformed into a half-space in the sine value space. The minimum adjustment vector and the maximum adjustable vector are obtained in this space. Thirdly, to search for the local optimal conflict-free solution, a two-layered optimization method is given. In the first layer, feasible conflict-free solutions are obtained by iterative search in virtue of the minimum adjustment vector and the maximum adjustable vector. In the second layer, the local optimal solution is generated using the sequential quadratic programing (SQP) method. The effectiveness of the proposed method is verified by numerical simulations. Compared with the existing geometric guidance theory based methods, the proposed method could effectively generate conflict-free solutions that lead to fewer detours, through which UAVs could coordinate online in highly crowded airspace.

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Section: Problem Formulationmentioning

confidence: 99%

Online cooperative airspace conflict resolution of unmanned aerial vehicles by space mapping based iterative search method

Zhao

Wang

Yang

et al. 2022

Advances in Mechanical Engineering

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“…In order to give an explicit description on the multi-UAV conflict scenario, we use the graph G(t) = (V, E(t)) to express the relationships among UAVs, where V = {1,., N} is the vertex set. 4 Each vertex denotes one UAV A i . E(t) = f(j, i)jcr ij = 1g is the corresponding edge set, which expresses the conflict relations.…”

Section: Conflict Detectionmentioning

confidence: 99%

“…As a result, it is inappropriate to directly apply CD&R method of manned aviation onto unmanned planes. 4 To improve the benefit of air traffic, the number of UAVs in the metropolis airspace is expected to be as large as possible. Therefore, an efficient CD&R system is a necessity.…”

Section: Introductionmentioning

confidence: 99%

Cooperative conflict detection and resolution of civil unmanned aerial vehicles in metropolis

Yang

Yin

Niu

et al. 2016

Advances in Mechanical Engineering

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Unmanned air vehicles have recently attracted attention of many researchers because of their potential civil applications. A systematic integration of unmanned air vehicles in non-segregated airspace is required that allows safe operation of unmanned air vehicles along with other manned aircrafts. One of the critical issues is conflict detection and resolution. This article proposes to solve unmanned air vehicles' conflict detection and resolution problem in metropolis airspace. First, the structure of metropolis airspace in the coming future is studied, and the airspace conflict problem between different unmanned air vehicles is analyzed by velocity obstacle theory. Second, a conflict detection and resolution framework in metropolis is proposed, and factors that have influences on conflict-free solutions are discussed. Third, the multi-unmanned air vehicle conflict resolution problem is formalized as a nonlinear optimization problem with the aim of minimizing overall conflict resolution consumption. The safe separation constraint is further discussed to improve the computation efficiency. When the speeds of conflict-involved unmanned air vehicles are equal, the nonlinear safe separation constraint is transformed into linear constraints. The problem is solved by mixed integer convex programming. When unmanned air vehicles are with unequal speeds, we propose to solve the nonlinear optimization problem by stochastic parallel gradient descent-based method. Our approaches are demonstrated in computational examples.

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“…Austin L. Smith [16] developed and implemented a collision avoidance algorithm based on an aggregate collision cone approach, ranging from a single platform capable of independently performing all collision avoidance functions to a diversity of collision avoidance commands that execute ground station calculations. Jian Yang [17] used a geometric method to describe the relationship between UAV conflicts, considering actual and potential conflicts, and formalized the CDR problem as a nonlinear optimization problem to minimize maneuvering costs. Furthermore, they designed a two-layer strategy consisting of Stochastic Parallel Gradient Descent (SPGD) and an interior-point algorithm to efficiently solve non-convex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Path Planning for Multiple UAVs with Incomplete Information

Xue

Zhu

et al. 2023

Electronics

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To address the dynamic path planning for multiple UAVs using incomplete information, this paper studies real-time conflict detection and intelligent resolution methods. When the UAVs execute the task under the condition of incomplete information, the mission strategy of different UAVs may conflict with each other due to the difference in target, departure place, time and other factors. Based on the multi-agent deep deterministic policy gradient algorithm (MADDPG), we designed new global reward and partial local reward functions for the UAVs’ path planning and named the improved algorithm as a complex memory driver-MADDPG (CMD-MADDPG). Thus, the trained UAVs can effectively and efficiently perform path planning tasks in conditions of incomplete information (each UAV does not know its reward function and so on). Finally, the simulation verifies that the proposed method can realize fast and accurate dynamic path planning for multiple UAVs.

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Two-Layer Optimization to Cooperative Conflict Detection and Resolution for UAVs

Cited by 8 publications

References 17 publications

Online cooperative airspace conflict resolution of unmanned aerial vehicles by space mapping based iterative search method

Online cooperative airspace conflict resolution of unmanned aerial vehicles by space mapping based iterative search method

Cooperative conflict detection and resolution of civil unmanned aerial vehicles in metropolis

Dynamic Path Planning for Multiple UAVs with Incomplete Information

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