Reciprocal Geometric Conflict Resolution on Unmanned Aerial Vehicles by Heading Control

Yang, Jian; Yin, Dong

doi:10.2514/1.g002607

Cited by 22 publications

(11 citation statements)

References 14 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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“…However, when the UAVs are outside the coordination set, since they are executing the single-agent level control law, collision avoidance is not guaranteed. In real applications, some collision avoidance algorithms such as [40] can be employed.…”

Section: And Entersmentioning

confidence: 99%

Coordinated Path Following Control of Fixed-wing Unmanned Aerial Vehicles

Chen

Cong

Wang

et al. 2019

Preprint

Self Cite

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In this paper, we investigate the problem of coordinated path following for fixed-wing UAVs with speed constraints in 2D plane. The objective is to steer a fleet of UAVs along the path(s) while achieving the desired sequenced inter-UAV arc distance. In contrast to the previous coordinated path following studies, we are able through our proposed hybrid control law to deal with the forward speed and the angular speed constraints of fixed-wing UAVs. More specifically, the hybrid control law makes all the UAVs work at two different levels: those UAVs whose path following errors are within an invariant set (i.e., the designed coordination set) work at the coordination level; and the other UAVs work at the single-agent level. At the coordination level, we prove that even with speed constraints, the proposed control law can make sure the path following errors reduce to zero, while the desired arc distances converge to the desired value. At the singleagent level, the convergence analysis for the path following error entering the coordination set is provided. We develop a hardwarein-the-loop simulation testbed of the multi-UAV system by using actual autopilots and the X-Plane simulator. The effectiveness of the proposed approach is corroborated with both MATLAB and the testbed.Index Terms-Coordinated path following, hybrid control, speed constraints, multi-UAV systems. 1 We have proved that the conventional coordinated path following control laws in [2] cannot solve the coordinated path following control problem for fixed-wing UAVs if the constraints are not properly considered.

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“…In recent years, many scholars have probed deep into collision avoidance approaches from different aspects [4]- [7]. Among them, the popular real-time collision avoidance algorithms include A * [8]- [9] and rapid-exploring random tree (RRT) [10]- [13], geometry-based method [14]- [17], artificial potential field (APF) [18]- [24]. The A * algorithm is a widely used heuristic search algorithm that introduces the global information to check every possible node in the shortest path, and estimates the distance of the current node to the destination, which reflects how possible it is for the current node to fall in the shortest path.…”

Section: Introductionmentioning

confidence: 99%

“…Jenie et al [16] put forward a real-time collision avoidance method based on conflict geometry, which is a tacticallevel collision avoidance strategy that prevents excessive deviation from the planned path. To solve conflicts in complex scenes, Yang et al [17] introduced spatial mapping to convert nonlinear safety distance to linear ones. However, these algorithms are not well suitable for reactive collision avoidance in dynamic airspace.…”

Section: Introductionmentioning

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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Reciprocal Geometric Conflict Resolution on Unmanned Aerial Vehicles by Heading Control

Cited by 22 publications

References 14 publications

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

Coordinated Path Following Control of Fixed-wing Unmanned Aerial Vehicles

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

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