Collision Avoidance Method for Self-Organizing Unmanned Aerial Vehicle Flights

Huang, Yang; Tang, Jun; Lao, Songyang

doi:10.1109/access.2019.2925633

Cited by 14 publications

(17 citation statements)

References 25 publications

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“…We use the algorithm presented in [43] for comparison because no other analogous computations (i.e. simulations of realistic drone flocks) have been carried out (and, thus, it has rapidly become a standard reference in the field [64][65][66]). We shall use the expression DroneFlock'18 to denote this algorithm in the present paper.…”

Section: Moving In Confined Environmentsmentioning

confidence: 99%

Adaptive leadership overcomes persistence–responsivity trade-off in flocking

Balázs

Vásárhelyi

Vicsek

2020

J. R. Soc. Interface.

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The living world is full of cohesive collectives that have evolved to move together with high efficiency. Schools of fish or flocks of birds maintain their global direction despite significant noise perturbing the individuals, yet they are capable of performing abrupt collective turns when relevant agitation alters the state of a few members. Ruling local fluctuations out of global movement leads to persistence and requires overdamped interaction dynamics, while propagating swift turns throughout the group leads to responsivity and requires underdamped interaction dynamics. In this paper we show a way to avoid this conflict by introducing a time-dependent leadership hierarchy that adapts locally to will : agents’ intention of changing direction. Integrating our new concept of will -based inter-agent behaviour highly enhances the responsivity of standard collective motion models, thus enables breaking out of their former limit, the persistence-responsivity trade-off. We also show that the increased responsivity to environmental cues scales well with growing flock size. Our solution relies on active communication or advanced cognition for the perception of will . The incorporation of these into collective motion is a plausible hypothesis in higher order species, while it is a realizable feature for artificial robots, as demonstrated by our swarm of 52 drones.

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Section: Moving In Confined Environmentsmentioning

confidence: 99%

Adaptive leadership overcomes persistence–responsivity trade-off in flocking

Balázs

Vásárhelyi

Vicsek

2020

J. R. Soc. Interface.

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“…Recently, Huang et al (2019) carried out an analysis of the collision avoidance method for fixed-wing UAVs. The authors proposed a flocking optimization algorithm based on a fitness function to evaluate the flight effect.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #1 of "An adaptive weighting mechanism for Reynolds rules-based flocking control scheme (v0.1)"

2021

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Cooperative navigation for fleets of robots conventionally adopts algorithms based onReynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the overall performance could not be guaranteed due to unexpected conditions not covered in experiments. This paper proposes a novel control scheme for a swarm of Unmanned Aerial Vehicles (UAVs) that also employs the original Reynolds rules but adopts an adaptive weight allocation mechanism based on the current context than being fixed at the beginning. The simulation results show that our proposed scheme has better performance than the conventional Reynolds-based ones in terms of the flock compactness and the reduction in the number of crashed swarm members due to collision. The analytical results of behavioral rules' impact also validate the proposed weighting mechanism's effectiveness leading to improved performance.

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“…In the above equations, we clarified the rules of Reynolds through simple formulas. Therefore, we utilized the formulas in [32,34,35] as follows:…”

Section: Flocking and Boids Modementioning

confidence: 99%

Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

2021

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This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

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Collision Avoidance Method for Self-Organizing Unmanned Aerial Vehicle Flights

Cited by 14 publications

References 25 publications

Adaptive leadership overcomes persistence–responsivity trade-off in flocking

Adaptive leadership overcomes persistence–responsivity trade-off in flocking

Peer Review #1 of "An adaptive weighting mechanism for Reynolds rules-based flocking control scheme (v0.1)"

Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

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