Two-Layered Mechanism of Online Unmanned Aerial Vehicles Conflict Detection and Resolution

Yang, Jian; Yin, Dong; Cheng, Qiao

doi:10.1109/tits.2017.2771465

Cited by 19 publications

(17 citation statements)

References 19 publications

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“…[18] apply a speed change method to solve conflicts but are limited by frontal conflicts and uncertainties. In a similar way, [19], [20] use speed and heading changes restricted to 2D; hence they do not consider any altitude change. In the AgentFly project, [21], [22] propose decentralized algorithms for collision avoidance based on game theory, but they do not consider a realistic UTM setting with task allocation or static obstacles.…”

Section: B Conflict Detection and Resolution (Cdr)mentioning

confidence: 99%

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Geraldes

Gonçalves

et al. 2019

IEEE Trans. Veh. Technol.

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In future UAV-based services, UAV (Unmanned Aerial Vehicle) fleets will be managed by several independent flight operation service providers in shared low-altitude airspace. Therefore, Conflict Detection and Resolution (CDR) methods that can solve conflicts-possible collisions between UAVs of different service providers-are a key element of the Unmanned Aircraft System Traffic Management (UTM) system. As our CDR method, we introduce an adaptation of ORCA, which is a state-of-the-art collision avoidance algorithm hitherto mainly used in a limited theoretical scope, to realistic UAV operations. Our approach, called Adapted ORCA, addresses practical considerations that are inherent to the deployment of UAVs in shared airspace, such as navigation inaccuracies, communication overhead, and flight phases. We validate our approach through simulations. First, by empirically tuning the ORCA parameters look-ahead time window and deconfliction distance, we are able to minimize the ORCA generated deviations from the nominal flight path. Second, by simulating realistic UAV traffic for delivery, we can determine a value for separation distance between UAVs that uses airspace efficiently.

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Section: B Conflict Detection and Resolution (Cdr)mentioning

confidence: 99%

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Geraldes

Gonçalves

et al. 2019

IEEE Trans. Veh. Technol.

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“…To handle flocking control with obstacle avoidance, work [22] proposes a UAV distributed flocking control algorithm based on the modified multi-objective pigeon-inspired optimization (MPIO), which considers both the hard constraints and the soft ones. Our previous works [23,24] formulate the conflict avoidance problem as a nonlinear optimization problem, and then use different methods to solve such an optimization problem. The work in [23] proposes a two-layered mechanism to guarantee safe separation, which finds the optimal heading change solutions with the vectorized stochastic parallel gradient descent-based method, and finds the optimal speed change solutions with a mixed integer linear programming model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Our previous works [23,24] formulate the conflict avoidance problem as a nonlinear optimization problem, and then use different methods to solve such an optimization problem. The work in [23] proposes a two-layered mechanism to guarantee safe separation, which finds the optimal heading change solutions with the vectorized stochastic parallel gradient descent-based method, and finds the optimal speed change solutions with a mixed integer linear programming model. The work in [24] uses the stochastic parallel gradient descent (SPGD) method to find the feasible initial solutions, and then uses the Sequential quadratic programming (SQP) algorithm to compute the local optimal solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

2019

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This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

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“…The objective of CDR is to optimize the flights of UAVs while keeping the safe separation among relevant UAVs in a time interval [ 0 , τ ] , where τ is the look-ahead time window. 5 The CDR methods could be classified into three levels according to τ . 6 The long-term CDR considers the safe separation among aircraft strategically in several hours, which is often accomplished before departure.…”

Section: Introductionmentioning

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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Two-Layered Mechanism of Online Unmanned Aerial Vehicles Conflict Detection and Resolution

Cited by 19 publications

References 19 publications

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

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

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