Optimal flocking control for a mobile sensor network based a moving target tracking

La, Hung Manh; Nguyen, Trung Hien; Nguyen, Cong Huu; Nguyen, Hai Xuan

doi:10.1109/icsmc.2009.5346069

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…The solution is generated using similar techniques of mutation, selection and crossover as reported in [32]. We model the genetic algorithm as follows.…”

Section: B Robotic Inspection Path Planning Based On Genetic Algoritmentioning

confidence: 99%

A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

Lim

Sheng

2014

IEEE Trans. Automat. Sci. Eng.

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166

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One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and marks the location of cracks manually. However, the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a crack inspection system that uses a camera-equipped mobile robot to collect images on the bridge deck. In this method, the Laplacian of Gaussian (LoG) algorithm is used to detect cracks and a global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, a path planning algorithm based on the genetic algorithm is developed. The path planning algorithm finds a solution which minimizes the number of turns and the traveling distance. We validate our proposed system through both simulations and experiments.Note to Practitioners-This work addresses crack detection and mapping on a bridge deck using a robotic system. Several challenges including coordinate transformation, robot localization and complete coverage path planning for the proposed robot system are tackled. This paper focuses mainly on the overall framework for such a robotic inspection system, therefore some of the techniques for handling shadows, paints, patches on bridges are not addressed. In real-world applications, these issues should be carefully incorporated into the design of the image processing algorithm. Also, there may be vibration caused by the passing traffic, which should be dealt with as well. The positioning of the ROCIM system is critical to crack mapping, hence more accurate robot localization techniques fusing various sensors such as differential GPS, Inertial Measurement Unit (IMU), etc. should be developed. It is also worth noting that the depth and severity of the cracks can be measured by employing advanced nondestructive evaluation (NDE) sensors, such as impact echo and ultrasonic surface wave.

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“…The solution is generated using similar techniques of mutation, selection and crossover as reported in [32]. We model the genetic algorithm as follows.…”

Section: B Robotic Inspection Path Planning Based On Genetic Algoritmentioning

confidence: 99%

A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

Lim

Sheng

2014

IEEE Trans. Automat. Sci. Eng.

Self Cite

166

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“…GAs [29], [30] and the GA operators of crossover and mutation [31] have been tested on numerous problems. Closer to our research, GAs have been applied to early SLAM optimization problems [32], mobile localization using ultrasonic sensors [33] [34], and in deep reinforcement learning [35]. This provides good evidence for GA efficacy on localization problems, and our main contribution in this paper is a demonstration of smaller translation error when using a GA to tune LIMO parameter values compared to the stock LIMO algorithm [7].…”

Section: Introduction and Related Workmentioning

confidence: 81%

Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

Sehgal

Singandhupe

et al. 2019

Advances in Visual Computing

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Lidar-Monocular Visual Odometry (LIMO), a odometry estimation algorithm, combines camera and LIght Detection And Ranging sensor (LIDAR) for visual localization by tracking camera features as well as features from LIDAR measurements, and it estimates the motion using Bundle Adjustment based on robust key frames. For rejecting the outliers, LIMO uses semantic labelling and weights of the vegetation landmarks. A drawback of LIMO as well as many other odometry estimation algorithms is that it has many parameters that need to be manually adjusted according to the dynamic changes in the environment in order to decrease the translational errors. In this paper, we present and argue the use of Genetic Algorithm to optimize parameters with reference to LIMO and maximize LIMO's localization and motion estimation performance. We evaluate our approach on the well known KITTI odometry dataset and show that the genetic algorithm helps LIMO to reduce translation error in different datasets.

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“…Methods of ensuring agents flocking have been proposed and studied in the literature. [33][34][35][36][37] Inspired by the natural world of birds and fish flocking together, 38 flocking algorithms have been formed. The algorithms allow agents to flock in different patterns in a distributed manner that requires on communication between direct neighbors rather than the entire flock.…”

Section: Literature Reviewmentioning

confidence: 99%

Consensus, cooperative learning, and flocking for multiagent predator avoidance

Young

2020

International Journal of Advanced Robotic Systems

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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.

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Optimal flocking control for a mobile sensor network based a moving target tracking

Cited by 19 publications

References 23 publications

A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

Consensus, cooperative learning, and flocking for multiagent predator avoidance

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