Research on the Collision Avoidance Algorithm for Fixed-Wing UAVs Based on Maneuver Coordination and Planned Trajectories Prediction

Wan, Yu; Tang, Jun; Lao, Songyang

doi:10.3390/app9040798

Cited by 13 publications

(11 citation statements)

References 23 publications

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“…Before delving into the design of a communication system for conflict management, it is important to understand which kind of information must be exchanged for the UAVs to maintain a safe separation distance between each other. The fact that collision avoidance is an important topic in robotics resulted in a high interest of the research community [34]- [39]. A common algorithm for detecting potential collisions is velocity obstacles [40].…”

Section: Collision Avoidance Algorithmsmentioning

confidence: 99%

Avoiding Collisions at Any (Low) Cost: ADS-B Like Position Broadcast for UAVs

2020

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Unmanned Aerial Vehicles (UAVs), a.k.a. drones, are increasingly used for different tasks. With more drones in the sky, the risk of accidents rises, sparking the need for conflict management solutions. Aircraft use a system called Automatic Dependent System-Broadcast (ADS-B) to continuously broadcast their position and speed but this system is not suitable for small drones because of its cost, complexity and capacity limitations. Broadband technologies such as Wi-Fi beacons are more suited for such dense scenarios, and they also offer the benefit of wide availability and low cost. The main challenges for Wi-Fi are (a) the multichannel nature of the technology makes transmitter and receiver coordination difficult, and (b) standard chipsets are not designed for frequent broadcast transmission and reception. In this paper, we propose and analyse a multichannel position broadcast solution that is robust against jamming and achieves a reliable location update within 125 ms. In addition, we implement the protocol on inexpensive embedded Wi-Fi modules and analyse the hardware limitations of such devices. Our conclusions are that, even on the simplest Wi-Fi chipsets, our protocol can be implemented to achieve a realistic location broadcast solution that still perfectly mimics simulation and analytical results on the lab bench and still can achieve approximately 4 message/s throughput at a distance of 900 m on flying UAVs.

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Section: Collision Avoidance Algorithmsmentioning

confidence: 99%

Avoiding Collisions at Any (Low) Cost: ADS-B Like Position Broadcast for UAVs

2020

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“…There is one paper focused on UAV collision avoidance technology [11]. Y. Wan, J. Tang, and S. Lao present a collision avoidance (CA) algorithm for a cooperative fixed-wing UAV based on the maneuver coordination and planned trajectory prediction by generating and predicting three trajectory candidates [11].…”

Section: Advanced Uav Technologiesmentioning

confidence: 99%

Special Issue on Unmanned Aerial Vehicles (UAVs)

Jung

2020

Applied Sciences

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This editorial paper was a special issue of Applied Sciences belonging to the section of mechanical engineering in MDPI journal and summarized the collected manuscripts regarding the unmanned aerial vehicles (UAVs) related technologies, including communication, control, collision avoidance, modeling, path planning, human-machine interface (HMI), artificial intelligence (AI), etc. Chronologically, this special issue was started to be coordinated at the end of Oct 2018, prepared for a month and opened to collect manuscripts from the middle of Nov 2018 until the end of Dec 2019. During almost a year, 26 papers were published online out of 50 submitted papers which results in 52% acceptance rate.

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“…In paper [29], the space-conflict sphere is a sphere centered at each individual aircraft. The new space-conflict sphere for a swarm is a sphere of a radius SD centered at the leader aircraft that embraces all the space-conflict spheres of the agents of the swarm.…”

Section: Conflict-detection Modelmentioning

confidence: 99%

Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

2019

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In this paper, we study the problem of conflict detection and resolution for unmanned aerial vehicle (UAV) swarms. Specially, we propose a distributed conflict detection and resolution method for multi-UAVs in formation based on consensus algorithm and strategy coordination. When encountering threat swarms, the UAVs in one swarm act as one unit and are together treated as one control object. Each swarm in conflict selects three candidate collision avoidance maneuvers from the preset strategy pool, generates the corresponding planned trajectories with an uncertainty trajectory modeling, and then broadcasts and shares them. All of the swarms in conflict coordinate and determine an optimal combination of strategies. When a collision is imminent, the primary strategy is activated. Each swarm adopts a ''leader-follower'' strategy, where the leader UAV is regarded as the controller and flies independently, and the others follow the leader UAV. During motion, a decentralized consensus algorithm is adopted for agents to converge to their positions for the desired formation and to maintain a stable geometric configuration. A temporal and spatially integrated conflict-detection model is improved, especially for UAV swarms. A token-allocation strategy is especially improved for distributed coordination to resolve the partial knowledge of the airspace condition. Damping of the coordination is proposed to address data dropouts and transmission delays. Two typical scenarios are conducted to test the methodology proposed in this paper. The simulation result demonstrates the effectiveness and rationality of the proposed methodology.

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Research on the Collision Avoidance Algorithm for Fixed-Wing UAVs Based on Maneuver Coordination and Planned Trajectories Prediction

Cited by 13 publications

References 23 publications

Avoiding Collisions at Any (Low) Cost: ADS-B Like Position Broadcast for UAVs

Avoiding Collisions at Any (Low) Cost: ADS-B Like Position Broadcast for UAVs

Special Issue on Unmanned Aerial Vehicles (UAVs)

Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

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