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

Yang, Jian; Yin, Dong; Niu, Yifeng; Zhu, Lei

doi:10.1177/1687814016651195

Cited by 12 publications

(6 citation statements)

References 31 publications

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“…A suggested range of the band is between 200 and 500 feet [28]. In such a narrow flight space, the UAVs should try to avoid collision by changing the heading or speed, rather than the height [26], [29]. As a result, this paper attempts to realize collision avoidance by changing both the heading and speed on the horizontal plan.…”

Section: Dapf Collision-free Path Generationmentioning

confidence: 99%

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

Zhang

Nie

2019

IEEE Access

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The key to the integration of unmanned aircrafts in the national airspace is to prevent them from colliding with the other traffics in the airspace. However, it is a great challenge to generate a safe, stable and robust collision-free path for unmanned aircraft system (UAS), a.k.a. unmanned aircraft vehicle (UAV), in real time, if the airspace is highly dynamics and heterogenous (i.e. thronged with aircrafts with different motion states). Based on the dynamic artificial potential field (DAPF) algorithm, this paper provides advisories on how to generate a real-time reactive collision-free path for unmanned aircraft vehicles flying in a dynamic airspace, aiming to ensure the flight safety and minimize the impact on surrounding traffic. Firstly, the safety distance was defined as a variable threshold, which scaled adaptively according to the relative motion states of the surrounding obstacles and the performance of the own UAV. Moreover, the forces of the potential field were improved, such that their magnitudes could be adjusted automatically according to the threat levels of the surrounding obstacles. The threat level of an obstacle depends on the relative position, speed and flight trend between the UAV and the obstacle. In addition, the repulsive force along the relative position of the traditional artificial potential field (APF) was retained, and a steering force was added to change the flight direction of the UAV, aiming to speed up the collision avoidance. Furthermore, the attractive force was modified to help the UAV return to the planned path quickly and stably. After that, the capacity were determined to ensure the feasible and practical path planning for the UAV. Finally, the proposed UAV path-planning method was proved effective, safe, stable and adaptive through simulations. INDEX TERMS Unmanned aircraft system (UAS), unmanned aircraft vehicle (UAV), path planning, collision avoidance, moving obstacle, artificial potential field (APF).

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Section: Dapf Collision-free Path Generationmentioning

confidence: 99%

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

Zhang

Nie

2019

IEEE Access

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“…[2] introduce a multi-layered architecture based on a taxonomy for CDR, but they mainly address the conception of the In-Flight phase [15], which is assessed through Monte Carlo simulations. Also, the conception and evaluation of In-Flight CDR methods with simulations based on real world scenarios is discussed in [10], [11]. [16] introduce 4DT (3D plus time Trajectories) into UAV trajectory modeling, and they focus on the uncertainties and technical errors in UAV navigation.…”

Section: Related Work a Conflict Detection And Resolution Methomentioning

confidence: 99%

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

Geraldes

Gonçalves

et al. 2019

IEEE Access

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The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and ''batch'' processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case. INDEX TERMS Unmanned aircraft system traffic management (UTM), pre-flight conflict detection and resolution (CDR), multi-agent path finding (MAPF), air traffic complexity metrics.

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“…(1) Repulsive force between a UAV and a noncooperative obstacle. The reachable region of an obstacle after the time horizon Ks can be computed by formula (18). Meanwhile, the position of the UAV in Ks can be determined based on its current position and velocity.…”

Section: Repulsive Forcementioning

confidence: 99%

“…Many reviews have been completed on relevant issues in the past two years [5][6][7][8]. Popular collision avoidance algorithms include geometric method [9][10][11], sampling-based method [12][13][14], numerical optimization [15][16][17][18][19], and artificial potential field (APF) [20][21][22][23][24][25][26][27][28]. The geometric method considers the geometric representation of the collision scene in the search for collision avoidance maneuvers.…”

Section: Introductionmentioning

confidence: 99%

Automatic Separation Management Between Multiple Unmanned Aircraft Vehicles in Uncertain Dynamic Airspace Based on Trajectory Prediction

Du¹,

Wang²,

Zhang³

et al. 2019

RIA

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In an uncertain dynamic airspace, the future trajectories of noncooperative aircrafts (obstacles) are very uncertain. Multiple unmanned aircraft vehicles (UAVs) must avoid colliding into noncooperative obstacles and keep a safe distance between each other. This poses a huge challenge to the unmanned aircraft system traffic management (UTM). To cope with the challenge, this paper puts forward an automatic separation management method for a formation of multiple UAVs based on trajectory prediction in 3D dynamic airspace. Firstly, the uncertain trajectories of each obstacle in a time horizon were predicted based on the reachable set, producing an ellipsoidal reachable region. Next, a safe and efficient double-layer separation management strategy was proposed based on the improved artificial potential field (APF) method, considering the safe distance between cooperative UAVs and that between cooperative and noncooperative UAVs. The distance-based traditional APF method was adopted to manage the conflicts according to the reachable region of each obstacle, maximizing the safety between the UAV and the obstacle. The APF method based on relative motion state was employed to manage the conflicts, and adaptively adjust the dynamic safe separation between the UAVs. Experimental results prove that our method can effectively prevent the conflicts between a UAV and other UAVs in the formation or noncooperative obstacle. Besides, the collision-free trajectory generated by our method is smooth and stable, and close to the planned trajectory. The proposed method provides a solid guarantee to UAV flight safety, while minimizing the impacts on nearby aircrafts. The research findings shed new light on the UTM of highly uncertain low-altitude airspaces.

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

Cited by 12 publications

References 31 publications

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

Automatic Separation Management Between Multiple Unmanned Aircraft Vehicles in Uncertain Dynamic Airspace Based on Trajectory Prediction

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