A new consensus theory-based method for formation control and obstacle avoidance of UAVs

Wu, Yu Cheng; Gou, Jinzhan; Hu, Xinting; Huang, Yanting

doi:10.1016/j.ast.2020.106332

Cited by 82 publications

(32 citation statements)

References 20 publications

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“…Previous works 5,8,[9][10][11][12][13]15 Previous works [4][5][6]14 Previous works 8,16,17 Previous works agents modify their reference trajectory according to the new leader trajectory while trying to keep their formation. Finally, various simulations are performed for the formation control of a UAV MAS to show its performance.…”

Section: Referencesmentioning

confidence: 99%

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Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Amirani

Bigdeli

Haeri

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, time-varying formation tracking problem for multi-agent systems is investigated. It is assumed the number of agents is changing and obstacles should be avoided along the trajectory. In addition, the tracking formation should be reached while the leader trajectory is unknown and communication data are noisy. To reach the desired formation tracking, first, a virtual leader is constructed using the local neighbouring data. Second, the reference signal is created based on the virtual leader and the leader data (which is available for some agents). The data fusion at this stage is done based on Kalman filtering to reduce the effect of communication noise. Third, an integral state feedback controller as a new consensus formation controller based on the relative position and velocity vectors of the agents and the estimated (virtual) leader are proposed to achieve the time-varying formation tracking. Next, closed-loop stability of the system under the proposed control law is investigated using the Lyapunov stability theorem. In order to implement the proposed control strategy, an extended state observer is designed to estimate the required velocity and accelerations. Finally, an obstacle avoidance strategy would be embedded to the controller via a repelling strategy. That is, once the leader identifies the obstacle and enters a repulsion region, its reference trajectory is modified to keep a safe distance from the obstacle. The agents modify their reference trajectory according to the new leader trajectory while trying to keep their formation. As an illustrative example, various simulations are performed for formation tracking of a unmanned aerial vehicle multi-agent system to evaluate its performance.

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Section: Referencesmentioning

confidence: 99%

“…Actually, two parallel Kalman filters are used for estimation. The estimated states of the virtual and the real leaders are augmented ignoring the mutual covariance of the estimation errors 17,18 as follows…”

Section: Data Fusion Of Leader and Virtual Leadermentioning

confidence: 99%

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Amirani

Bigdeli

Haeri

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The results assessed in the simulation using the dynamic model give improved circular error probable (CEP) results. Yu Wu et al [104] came up with a new consensus theory-based method for formation control and obstacle avoidance in UAVs.…”

Section: Applications To Aerial Vehiclesmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

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“…Wang et al designed a distributed formation controller based on sliding mode theory using nearby information [26]. Wu et al solved the obstacle avoidance problems in UAV formations by combining a particle swarm optimization algorithm and MPC [27]. Dubay et al investigated the problem of the collision avoidance of UAVs in the process of reaching consensus [28].…”

Section: Introductionmentioning

confidence: 99%

Design of a Multi-Constraint Formation Controller Based on Improved MPC and Consensus for Quadrotors

2022

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The formation flight of quadrotor unmanned aerial vehicles (UAVs) is a complex multi-constraint process. When designing a formation controller, the dynamic model of the UAV itself has modeling errors and uncertainties. Model predictive control (MPC) is one of the best control methods for solving the constrained problem. First, a mathematical model of the quadrotor considering disturbance and uncertainty is established using the Lagrange–Euler formulation and is divided into a rotational subsystem (RS) and a translational subsystem (TS). Here, an improved MPC (IMPC) strategy based on an error model is introduced for the control of UAVs. The tracking errors caused by synthesis disturbance can be eliminated because of the integrator embedded in the augmented model. In addition, by modifying the parameters of the cost function, not only can the degree of stability of the closed-loop subsystem be specified, but also numerical problems in the MPC calculation can be improved. The simulation results demonstrate the stability of the designed controller in formation maintenance and its robustness to external disturbances and uncertainties.

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A new consensus theory-based method for formation control and obstacle avoidance of UAVs

Cited by 82 publications

References 20 publications

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Design of a Multi-Constraint Formation Controller Based on Improved MPC and Consensus for Quadrotors

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