Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs

Kownacki, Cezary; Ambroziak, Leszek

doi:10.1016/j.ast.2017.05.040

Cited by 43 publications

(34 citation statements)

References 18 publications

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“…As the simulation of the traditional APF formation obstacle avoidance fly path as shown in Figs 7,8,9,10,11,and 12, the uncertainty of navigation information leads to the formation's failure to avoid obstacles in time, which leads to collision. Compared with that, it can be seen from Figs 8, 13 that after augmenting the APF algorithm with navigation information, the potential risk caused by navigation quality uncertainty can be mitigated, and the path becomes smoother.…”

Section: Simulationsmentioning

confidence: 99%

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Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Liu

Chi

et al. 2020

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In recent years, multiple Unmanned Aerial Vehicle (UAV) formation flight has attracted worldwide research interest, for its potential benefits of scalability and flexibility. In complex urban environments, the successful operation of those UAVs requires the system to provide certain safety level. As one of the key requirements, collision avoidance improves the system's ability to accommodate operational environment variations, and to perform multiple tasks. To achieve this, artificial potential field (APF) has been recognized as one of the most suitable methods along with drone control. Although there has been substantial relevant work on the APF for single UAV in static environment, more efforts are desired to address formation maneuvers in complex environments such as urban. Most importantly, the traditional APF algorithms do not account for random errors in navigation solutions, which can bring potential risk to the UAV system. In response, this paper proposes a new APF algorithm that employs navigation information in complex urban environments, and the goal is to realize UAV formation collision avoidance. By augmenting the APF algorithm with UAV navigation information, the potential risk caused by navigation uncertainty can be mitigated, especially in the Global Navigation Satellite System (GNSS) challenged environment. The principle of the new APF approach is adaptively estimating the parameters of potential field force function, using the variance of navigation information and user-defined confidence probability. This new approach is applied in the synchronized UAV formation collision avoidance control. As a result, the UAVs can achieve fast position and attitude adjustment with high safety confidence. To verify the algorithm, quadrotors with emulated GNSS receivers are used to generate observation data. These data are incorporated into a complex urban environment simulation, where multiple sets of virtual obstacles are injected. Results show that the proposed method can achieve safe and efficient collision avoidance for cooperative formation flight in urban GNSS challenged environment.

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Section: Simulationsmentioning

confidence: 99%

“…This ensures the accuracy of position tracking and the flight stability of UAV formation. However, it did not consider obstacles in the environment [11]. Recently, Yong-shen et al [12] combined the virtual structure method with APF to achieve UAV formation flight.…”

Section: Introductionmentioning

confidence: 99%

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Liu

Chi

et al. 2020

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“…A wide range of works within this field has been conducted for many years throughout the world [1][2][3] as well as in Poland [4]. Some concepts require small flying objects to be located in a predetermined time and in precisely defined positions [5,6]. This can be achieved by launching airplanes sequentially at certain speeds and at specified intervals.…”

Section: Introductionmentioning

confidence: 99%

Design and Dynamics of Kinetic Launcher for Unmanned Aerial Vehicles

Kondratiuk

Ambroziak

2020

Applied Sciences

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Assisting in the starting procedure of Unmanned Aerial Vehicles (UAVs) is one of many very important areas of modern aviation research. Supported start-up saves fuel or electrical energy, increases operator safety and level of autonomy, extends the application area, and, in some applications, even enables the operator to shape the motion characteristics of the initial phase of a UAV’s flight. Currently used solutions, depending on an aircraft’s class, are based on the utilization of rubber, pneumatic or electromagnetic launchers. All of these launchers are used for the medium class of UAVs and all of them use the potential energy previously stored in stretched rubber, compressed air or electrical voltage. In this paper, authors propose the novel concept of a launcher powered through kinetic energy stored in a rotary wheel driven by an electric motor. Using the transmission systems of the drive and the controlled clutch and an electromagnetic brake, it is possible to precisely control the speed and acceleration of the launched object. Within the paper, the authors present and discuss the applied equations of dynamics, the results of a simulation that was carried out using the MATLAB/Simulink software and a conceptual CAD model of preliminary engineering solutions for the kinetic UAV launcher. The work is summarized in the conclusions section, which details the practical implementation of the device.

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“…In recent years, the unmanned aerial vehicle (UAV), as the embodiment of the agent, has attracted much attention due to increasingly high autonomy levels and the application value of the UAV in military and civilian fields [9]- [15]. According to Lanchester's laws, the number of combat units is a decisive factor for victory or defeat in warfare and is more important than the unit's capability.…”

Section: Introductionmentioning

confidence: 99%

Flocking Control of Fixed-Wing UAVs With Cooperative Obstacle Avoidance Capability

et al. 2019

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In recent years, with the development of the unmanned aerial vehicle (UAV) and battlefield environments, the UAV swarm has attracted significant research attention. To solve problems regarding poor state consensus among swarm individuals due to a small number of individuals easily falling into local minima upon encountering an obstacle, this paper proposes a flocking obstacle avoidance algorithm with local interaction of obstacle information. To make the UAV swarm follow the desired trajectory with better state consensus, we improved the flocking control algorithm of agents according to the characteristics and requirements of the UAV swarm. The obstacle avoidance algorithm for the UAV swarm is based on Olfati-Saber's multi-agent obstacle avoidance algorithm. The proposed method has individuals in the swarm communicate obstacle information with their neighbors, and we present a simple analysis of this method. The method improves the cooperative obstacle avoidance capability of the flocking control algorithm. The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles.

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Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs

Cited by 43 publications

References 18 publications

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

Design and Dynamics of Kinetic Launcher for Unmanned Aerial Vehicles

Flocking Control of Fixed-Wing UAVs With Cooperative Obstacle Avoidance Capability

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