A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles

Qiu, Huaxin; Duan, Haibin

doi:10.1016/j.ins.2018.06.061

Cited by 121 publications

(38 citation statements)

References 28 publications

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“…and using (11) in (10) yields (17). In order to avoid the random loss of measurement in calculating the Kalman gain K which introduces more errors, the first moments instead of measurements is used to determine the Kalman gain.…”

Section: ) Variational Bayesian Measurement Updatementioning

confidence: 99%

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A Novel Nonlinear Algorithm for Non-Gaussian Noises and Measurement Information Loss in Underwater Navigation

et al. 2020

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The ocean environment is complex and changeable because of all kinds of noise interferences, such as salt cliffs, ships around and other electromagnetic interferences, so the measurement information is prone to be lost. It is difficult to describe the complex noise and the acquisition probability of measurement information. In this paper, a continuous discrete variational Bayesian filter (CD VBF) is proposed to solve the problems of the heavy tailed noises and measurement random loss for state estimation. The variational Bayesian (VB) approach can effectively estimate the state vector, scale matrices, degree of freedom (DOF) parameters, Bernoulli random variables and the acquisition probability of measurement information. The performances of the proposed algorithm and traditional algorithms are tested in simulations and underwater experiments. The simulation results illustrate that the CD VBF has better localization accuracy and robustness. The experimental results demonstrate that the localization accuracy is improved by CD VBF and an optimal solution of iteration number is acquired.

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Section: ) Variational Bayesian Measurement Updatementioning

confidence: 99%

“…11) in(10) yields, and the logarithm of () q  is renewed below:This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

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confidence: 99%

A Novel Nonlinear Algorithm for Non-Gaussian Noises and Measurement Information Loss in Underwater Navigation

et al. 2020

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“…The collision avoidance in work [21] is formulated as a set of linear quadratic optimization problems, which are solved with an original geometric based formulation. To handle flocking control with obstacle avoidance, work [22] proposes a UAV distributed flocking control algorithm based on the modified multi-objective pigeon-inspired optimization (MPIO), which considers both the hard constraints and the soft ones. Our previous works [23,24] formulate the conflict avoidance problem as a nonlinear optimization problem, and then use different methods to solve such an optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

2019

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This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

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“…However, the basic PIO algorithm is easy to fall into the local optimal solution [ 3 ]. It is necessary to try to strengthen PIO’s global search capability [ 6 , 7 ]. The binary particle swarm optimization (BPSO) is developed by Kennedy and Eberhart [ 8 ] and used to optimize combinational problems.…”

Section: Introductionmentioning

confidence: 99%

Binary Pigeon-Inspired Optimization for Quadrotor Swarm Formation Control

Zhang

Duan

Chen

2020

Lecture Notes in Computer Science

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This paper proposes a binary pigeon-inspired optimization (BPIO) algorithm, for the quadrotor swarm formation control problem. The expected position is provided by the BPIO. Quadrotor moves to the position with control strategy, and the strategy is based on the proportional integral derivative (PID) control method. The BPIO algorithm which is based on pigeon-inspired optimization (PIO) algorithm can effectively solve the combination problem in the binary solution space. The BPIO keeps the fast convergence of the PIO, and can explore the space effectively at the same time. The parameters to be optimized are encoded with binary bits. A special fitness function is designed to avoid the happening of crash. The simulation experiment shows how the BPIO works. The results of simulation verify the feasibility and effectiveness of the BPIO to solve the swarm formation problem.

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A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles

Cited by 121 publications

References 28 publications

A Novel Nonlinear Algorithm for Non-Gaussian Noises and Measurement Information Loss in Underwater Navigation

A Novel Nonlinear Algorithm for Non-Gaussian Noises and Measurement Information Loss in Underwater Navigation

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

Binary Pigeon-Inspired Optimization for Quadrotor Swarm Formation Control

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