Self-organization in aggregating robot swarms: A DW-KNN topological approach

Khaldi, Belkacem; Harrou, Fouzi; Cherif, Foudil; Sun, Ying

doi:10.1016/j.biosystems.2018.01.005

Cited by 35 publications

(19 citation statements)

References 47 publications

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“…k NN is one of the simplest non-parametric classification methods with an easily interpretable output, low calculation times, and high predictive power. Furthermore, it is widely used in classification and regression problems for real applications [ 23 , 24 , 25 , 26 ]. The algorithm is based on the predictions for a new instance ( x ) by searching through the entire training set for the most similar instances of k (the neighbors) and summarizing the output variable for those k instances.…”

Section: Self-tuning Methods For Reliability In Lidar Sensors Netwomentioning

confidence: 99%

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

Castaño

Beruvides

Villalonga

et al. 2018

Sensors

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On-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the ‘Internet of Things’ (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds.

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Section: Self-tuning Methods For Reliability In Lidar Sensors Netwomentioning

confidence: 99%

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

Castaño

Beruvides

Villalonga

et al. 2018

Sensors

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“…With the VVC model, we specically used the RAB (range and bearing) communication device for inter-robots communications and two-wheeled actuators to independently control the forward speed of the robots right and left wheels. The total virtual viscoelastic force F i vvc exerted on a robot i is obtained using Equation (1). Then basing on that force, the desired forward speeds v ri and v l i of the right and the left wheels of the robot is given in Equation 2.…”

Section: Virtual Viscoelastic Control Modeling Approachmentioning

confidence: 99%

“…S WARM intelligence has been remarkably observed in many biological organisms such as social insects and order living animals [1]. With swarm intelligence, complex swarming behaviours can emerge from simple interactions within individuals of a swarm in addition to their interaction with their environment [2].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Robotic Swarm Systems Under Noisy Conditions Using an Effective Fault Detection Strategy

et al. 2019

Self Cite

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Fault detection in robotic swarm systems is imperative to guarantee their reliability, safety, and to maximize operating efficiency and avoid expensive maintenance. However, data from these systems are generally contaminated with noise, which masks important features in the data and degrades the fault detection capability. This paper introduces an effective fault detection approach against noise and uncertainties in data, which integrates the multiresolution representation of data using wavelets with the sensitivity to small changes of an exponentially weighted moving average scheme. Specifically, to monitor swarm robotics systems performing a virtual viscoelastic control model for circle formation task, the proposed scheme has been applied to the uncorrelated residuals form principal component analysis model. A simulated data from ARGoS simulator is used to evaluate the effectiveness of the proposed method. Also, we compared the performance of the proposed approach to that of the conventional principal component-based approach and found improved sensitivity to faults and robustness to noises. For all the fault types tested-abrupt faults, random walks, and complete stop faults-our approach resulted in a significant enhancement in fault detection compared with the conventional approach.

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“…The fourth, selected technique was a k-Nearest-Neighbours (kNN) clustering algorithm. K-NN is one of the simplest non-parametric classification methods with an easily interpretable output, low calculation times, and high predictive power (Aziz et al 2018;Khaldi et al 2018;Beruvides et al 2017;Penedo et al 2012). The setting parameters used was a Euclidean distance and the number of neighbours equal to 5.…”

Section: Cyber-physical Applicationmentioning

confidence: 99%

Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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Self-organization in aggregating robot swarms: A DW-KNN topological approach

Cited by 35 publications

References 47 publications

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

Monitoring Robotic Swarm Systems Under Noisy Conditions Using an Effective Fault Detection Strategy

Computational Intelligence for Simulating a LiDAR Sensor

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