Collision avoidance control for formation flying of multiple spacecraft using artificial potential field

Hwang, Jiyoon; Lee, Jin-Ah; Park, Chandeok

doi:10.1016/j.asr.2021.12.015

Cited by 42 publications

(7 citation statements)

References 29 publications

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“…Prior research by Wang C, Chen D, and others highlighted the effectiveness of potential fields in debris rendezvous and obstacle avoidance in intricate space scenarios [4], while other studies confirmed the method's applicability in aerospace dynamics [5] [6]. Issues related to potential local minima in artificial potential fields have been discussed extensively in literature [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial potential field-based method for multi-spacecraft loose formation control

Guan,

Zhang,

Chen

et al. 2024

J. Phys.: Conf. Ser.

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This study presents a control strategy based on artificial potential fields to maintain loose formation flying of satellites without a fixed formation, ensuring collision avoidance and stability under various conditions. The approach employs artificial potential fields for obstacle evasion and maintaining relative distances between satellites. Comprehensive simulations validate this method, with Monte Carlo techniques confirming control stability despite variations in initial conditions. The strategy effectively manages the integration of external satellites, maintaining optimal formation even as operational demands increase.

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Section: Introductionmentioning

confidence: 99%

Artificial potential field-based method for multi-spacecraft loose formation control

Guan,

Zhang,

Chen

et al. 2024

J. Phys.: Conf. Ser.

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“…An algorithm is introduced in [22] for designing/controlling trajectories of multiple spacecrafts in formation while avoiding collision. The work describes a potential field for formation, which consists of structural and repulsive potentials to allow multiple agents to maintain a regular polygonal shape.…”

Section: Introductionmentioning

confidence: 99%

Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments

Souza

Lima

Morais

et al. 2022

Sensors

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Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.

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“…In robot obstacle avoidance, the artificial potential field method combined with the D* algorithm is used to reach the dynamic target and avoid the dynamic obstacles [19,20]. In UAV obstacle avoidance, a rotational potential field is designed to be added to the artificial potential field to solve the local minima [21]. In order to study the working characteristics of complex snake-like robots in water, the IB-LBM method is combined with the improved artificial potential (AF), which can effectively eliminate the local minimum of the artificial potential field method [22].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning Algorithm for Driverless Vehicles

Pei

Zhang

2022

Mathematics

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In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.

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Collision avoidance control for formation flying of multiple spacecraft using artificial potential field

Cited by 42 publications

References 29 publications

Artificial potential field-based method for multi-spacecraft loose formation control

Artificial potential field-based method for multi-spacecraft loose formation control

Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments

Research on Path Planning Algorithm for Driverless Vehicles

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