Taxonomy of Conflict Detection and Resolution Approaches for Unmanned Aerial Vehicle in an Integrated Airspace

Jenie, Yazdi I.; Kampen, E. van; Ellerbroek, Joost; Hoekstra, Joris

doi:10.1109/tits.2016.2580219

Cited by 48 publications

(28 citation statements)

References 74 publications

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“…These approaches, for instance, may specify a set of conflict‐resolving rules (eg, right‐of‐way rules), types of surveillance (eg, aircraft positions are determined by central radar or based on information broadcasted by aircraft), and types of coordination (ie, whether aircraft can communicate with each other in case of conflict). We refer the interested reader to the surveys of Jenie et al on conflict detection and resolution approaches and of Pham et al on collision avoidance systems.…”

Section: Strategic Tactical and Operational Issues Linked To Drone mentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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Section: Strategic Tactical and Operational Issues Linked To Drone mentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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“…With dynamic obstacles, Conflict Detection and Resolution (CDR) methods become indispensable for safe air traffic. In UTM, and similar in ATM (Air Traffic Management), CDR methods refer to different levels of "redundancy" [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the design and specification of a complete UTM system will require many considerations [2], [1], some of which are not yet determined, such as requirements for communication, application of dynamic airspace configuration, and so on.…”

Section: Introductionmentioning

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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“…There are many approaches to handle the collision avoidance problem in different stages [12]. Many of those approaches rely on models for the dynamic of the environment and UAVs.…”

Section: Introductionmentioning

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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Taxonomy of Conflict Detection and Resolution Approaches for Unmanned Aerial Vehicle in an Integrated Airspace

Cited by 48 publications

References 74 publications

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

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