LiDAR Equipped Robot Navigation on Behavior-based Formation Control for Gas Leak Localization

Nursyeha, Muhammad Agung; Rivai, Muhammad; Purwanto, Djoko; Tukadi,

doi:10.1109/isitia49792.2020.9163758

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…The robots moved together towards the gas leak source; therefore, a control was needed to maintain the shape of the formation. Behavior-based formation control was used in this study because it has an easy algorithm and clear task definitions [8]. In this control, each robot had the same task.…”

Section: Formation Controlmentioning

confidence: 99%

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Heuristic Approach on Robot Formation to Find a Gas Leak Source

2023

IJIES

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Robots have been widely used to overcome problems in the environment. In this case, mobile robots were used to find the source of gas leakage. Each robot was equipped with gas sensors in the form of a stereo-nose and Light Detection and Ranging (LiDAR). The robot formation in finding the gas leak source had the advantage of expanding the gas detection area, meaning the robots could not easily lose the pathway. However, detecting through formation required a longer execution time. To speed up the robots in finding the gas leak source's location, this study involved the heuristic model. This model allowed each robot to change positions in a formation. The experiment results showed that the robot group using the heuristic model was 62.2% faster than a single robot. This method was also 19.4% faster than the robot group with a fixed formation.

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Section: Formation Controlmentioning

confidence: 99%

“…The formation is an egression for tracking gas leak sources where there is a change in the plume. Formations make for wider gas detection than a single robot [8]. Formation behavior mimics group intelligence, especially in birds that fly in groups in a V formation.…”

mentioning

confidence: 99%

Heuristic Approach on Robot Formation to Find a Gas Leak Source

2023

IJIES

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“…e multiple mobile robots also enhance the fault tolerance and robustness of the system and strengthen the ability of robots' environment recognition. Comparing with the leader-follower method [5][6][7][8], behavior-based method [9][10][11],and virtual structure method [12,13], the model predictive control (MPC) has attracted attention because of its ability of improving the robustness of the system and having better dynamic control performance in solving the problem of consensus protocol in distributed formation control of multiple mobile robots. Also, MPC is convenient to establish the system model.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

Shang

Zhu

Pan

et al. 2021

Discrete Dynamics in Nature and Society

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In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. The robust performance objective minimization is applied to replace the quadratic performance objective minimization to establish the optimization problem, where the model uncertainty is considered in the distributed system. The control policy is derived by applying the data-driven strategy, and the future predictive value is obtained by employing the linear law in the historical data. Lyapunov theory is referred to analyze the stability of the mobile robot formation system. The effectiveness of the proposed method is proved by a set of simulation experiments.

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