Distributed flocking control of mobile robots by bounded feedback

Nguyen, Thang; Han, Thanh-Trung; La, Hung Manh

doi:10.1109/allerton.2016.7852281

Cited by 17 publications

(19 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 Initialize a vertex matrix M . 6 Initialize V start , the starting Vertex and set it to null. 7 Initialize a vertex array Q, of the dirt model where each vertex is a cell: 8 Vertex V i has a coordinate (x, y), a dirt level λ, a boolean visit that is true when V start is connected or pass through it and an array of vertex it is connected to.…”

Section: Path Planningmentioning

confidence: 99%

A Multi-Robotic System for Environmental Dirt Cleaning

Pham

La³

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

View full text Add to dashboard Cite

There is a lot of waste in an industrial environment that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automative cleaning robots could be used, the environment is often too big for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning system algorithm for multiple automatic iRobot Creates teaming to efficiently clean an environment. Moreover, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by spending more time on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result.

show abstract

Section: Path Planningmentioning

confidence: 99%

A Multi-Robotic System for Environmental Dirt Cleaning

Pham

La³

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

View full text Add to dashboard Cite

show abstract

“…The algorithm for the collision avoidance is built based on the repulsive vector field (14) combined with the relative velocity vector k c ik ðv i À v k Þ between robots k and i as follows…”

Section: Collision Avoidance Control Algorithmmentioning

confidence: 99%

“…Remark 3. The protocol for collision avoidance for the agents of the system in subsection Control architecture employs a repulsive force in equation (14). Here, the shapes of agents are simplified to be points or circles.…”

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

“…Note that in [8], constraints on the speeds of the mobile robot are not considered. The work in [2] focuses on the control design for the torque level while the control inputs in this paper are the angular and translational speeds. Furthermore, the obstacle avoidance is considered in this paper, which is different from [2].…”

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)

Self Cite

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

Distributed flocking control of mobile robots by bounded feedback

Cited by 17 publications

References 20 publications

A Multi-Robotic System for Environmental Dirt Cleaning

A Multi-Robotic System for Environmental Dirt Cleaning

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