Formation Control and Obstacle Avoidance of Multiple Rectangular Agents With Limited Communication Ranges

Nguyen, Thang; La, Hung Manh; Le, Tuan; Jafari, Mohammad

doi:10.1109/tcns.2016.2542978

Cited by 84 publications

(41 citation statements)

References 39 publications

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“…Remark 3.2: Similarly to [4], we use an interactive potential function to avoid collisions among agents and obstacles. In this paper, the control laws (11) and (12) are employed when the collision region between agent i and other agents or obstacle is not active. In contrast, when the collision region is detected, the control laws (27) and (28) are in use.…”

Section: Obstacle Avoidancementioning

confidence: 99%

Distributed formation control of nonholonomic mobile robots by bounded feedback in the presence of obstacles

Nguyen

2017

2017 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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The problem of distributed formation control of nonholonomic mobile robots is addressed in this paper, in which the robots are designed to track a formation. Collision avoidance among agents is guaranteed using a control law based on a repulsive force. In an uncertain environment where obstacles exist, the construction of a repulsive force and rotational direction enables agents to avoid and pass the obstacles. The control inputs of each robot are designed to be bounded. Numerical simulations with different formations are implemented to demonstrate the efficacy of the proposed scheme.

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Section: Obstacle Avoidancementioning

confidence: 99%

Distributed formation control of nonholonomic mobile robots by bounded feedback in the presence of obstacles

Nguyen

2017

2017 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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“…Collision is an inherent problem in unmanned aerial vehicle (UAV) systems [1][2][3][4][5][6]. Path planning is important in performing different tasks.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

2019

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The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

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“…The more advantages and disadvantages, as well as the distinctions among these three structures can be found in [8][9][10]. Two methods are considered for the formation control of multiagent systems, namely, the centralized [11] and decentralized controllers [12,13], according to the interaction topology of sensing graph. In the centralized controller, each agent is assumed to have access to all the states collected from the central controller.…”

Section: Introductionmentioning

confidence: 99%

“…Geometrically, the obstacles can be viewed as static agents, which steer all the dynamic agents away from the static agents (obstacles). The obstacles can be modeled as a single particle/point [25], elliptical shapes, and rectangular shapes, in general [8,13]. In [13], the distributed control strategy based on an artificial potential function is proposed to address the formation control and obstacle avoidance with the rectangular agents, which solved the obstacle avoidance problem.…”

Section: Introductionmentioning

confidence: 99%

“…The obstacles can be modeled as a single particle/point [25], elliptical shapes, and rectangular shapes, in general [8,13]. In [13], the distributed control strategy based on an artificial potential function is proposed to address the formation control and obstacle avoidance with the rectangular agents, which solved the obstacle avoidance problem. In [8], the authors investigated the formation control of a group of robots under virtual and behavioral structures, in which a rotational potential field is employed to drive the autonomous mobile agent away from the obstacle without getting stuck in the local minimum points.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Formation Control Scheme with Obstacle Avoidance for Multiagent Systems

Yang

Fan

2019

Mathematical Problems in Engineering

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This study considers the problem of formation control for second-order multiagent systems. We propose a distributed nonlinear formation controller where the control input of each follower can be expressed as a product of a nonlinear term that relies on the distance errors under the leader–follower structure. In the leader–follower structure, a small number of agents are assumed to be the leaders, and they are responsible for steering a group of agents to the specific destination, while the rest of the agents are called followers. The stability of the proposed control laws is demonstrated by utilizing the Lyapunov function candidate. To solve the obstacle avoidance problem, the artificial potential approach is employed, and the agents can avoid each possible obstacle successfully without getting stuck in any local minimum point. The control problem of multiagent systems in the presence of unknown constant disturbances is also considered. To attenuate such disturbances, the integral term is introduced, and the static error is eliminated through the proposed PI controller, which makes the system stable; the adaptive controller is designed to reduce the effect of time-varying disturbances. Finally, numerical simulation results are presented to support the obtained theoretical results.

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Formation Control and Obstacle Avoidance of Multiple Rectangular Agents With Limited Communication Ranges

Cited by 84 publications

References 39 publications

Distributed formation control of nonholonomic mobile robots by bounded feedback in the presence of obstacles

Distributed formation control of nonholonomic mobile robots by bounded feedback in the presence of obstacles

Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

A Distributed Formation Control Scheme with Obstacle Avoidance for Multiagent Systems

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