Flocking of mobile robots by bounded feedback

Han, Thanh-Trung; La, Hung Manh; Dinh, Bach Hoang

doi:10.1109/coase.2016.7743469

Cited by 9 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[14][15][16] Movement control of multiagent systems can come in the form of cooperatively doing path planning as in the literature. [17][18][19] Alternatively, there are means of control through flocking in varying formations [20][21][22] to achieve a variety of tasks. 23,24 Reinforcement learning has been implemented cooperatively in a variety of ways for multiagent environments, such as a GridWorld 25,26 and box pushing.…”

Section: Motivationmentioning

confidence: 99%

Consensus, cooperative learning, and flocking for multiagent predator avoidance

Young

2020

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this article, a hybrid multiagent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learns to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multiagent coordination tasks, but this article focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state-space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments, resulting in an efficient cooperative learning behavior. In the future, the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multiagent tasks.

show abstract

Section: Motivationmentioning

confidence: 99%

Consensus, cooperative learning, and flocking for multiagent predator avoidance

Young

2020

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…We summarize the above analysis in the following theorem. detection range as in Figure 4, the control law in equation (17) will drive robot i to escape the obstacle.…”

Section: Obstacle Avoidance Control Algorithmmentioning

confidence: 99%

“…In control law (17), the rotational force field (see Figure 3) is added to combine with the repulsive force (see Figure 2) to drive robot i to quickly escape its neighboring obstacle. While the potential force field is used to enable it to avoid collision with its neighboring obstacle, the rotational force field is used to solve the local minimum problems; for instance, the robot meets a trapping point, at which the repulsive force of the obstacles and the attractive force of the target are balanced.…”

Section: Obstacle Avoidance Control Algorithmmentioning

confidence: 99%

“…Autonomous robots of a multiagent system can be underwater vehicles, 1,2 unmanned aerial vehicles, 3,4 mobile sensor networks, [5][6][7][8][9][10][11][12] rectangular agents, 13 and nonholonomic mobile robots. [14][15][16][17][18] Centralized control protocols have been constructed based on the common assumption that the information of all agents is available or the multiagent system possesses all-to-all communication. The drawbacks of the centralized communication control architect are inflexibility and large computational costs for each controller for each agent especially when the number of robots is large.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Formation control for autonomous robots with collision and obstacle avoidance using a rotational and repulsive force–based approach

Dang

Nguyen

et al. 2019

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

In this article, we address a formation control problem for a group of autonomous robots to track a moving target in the presence of obstacles. In the proposed method, desired formations, which consist of virtual nodes arranged in specific shapes, are first generated. Then, autonomous robots are driven toward these virtual nodes without collisions with each other using a novel control scheme, which is based on artificial force fields. The convergence analysis is shown based on Lyapunov's stability. The novelty of the proposed approach lies in a new combination of rotational force field and repulsive force field to design a mechanism so that robots can avoid and escape complex obstacle shapes. The effectiveness of the proposed method is illustrated with numerical examples using V-shape and circular shape formations.

show abstract

“…Research on the collective behavior of an autonomous mobile robot system has been extensively conducted for many years. Recent development of flocking control for mobile robots with bounded feedback is designed based on a centralized approach [1] and later extended to a decentralized approach [2]. In many applications, the employment of a single complicated robot system can be replaced by invoking a coordination of a multi-agent system with much simpler configurations, whose advantages can be scalability, flexible deployment, cheaper cost, reliability, etc.…”

Section: Introductionmentioning

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)

View full text Add to dashboard Cite

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.

show abstract

Flocking of mobile robots by bounded feedback

Cited by 9 publications

References 19 publications

Consensus, cooperative learning, and flocking for multiagent predator avoidance

Consensus, cooperative learning, and flocking for multiagent predator avoidance

Formation control for autonomous robots with collision and obstacle avoidance using a rotational and repulsive force–based approach

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

Contact Info

Product

Resources

About