Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA

Author's accepted manuscriptAntibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH ≅ 7, optimal conditions for initial CIP concentration, Fe2+ concentration, [H2O2]/[Fe2+] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.acceptedVersio

Technical Report: A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints

This technical report is an extended version of the paper 'A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints' accepted to the 2013 IEEE International Conference on Robotics and Automation (ICRA). This paper considers the problem of finding the most informative path for a sensing robot under temporal logic constraints, a richer set of constraints than have previously been considered in information gathering. An algorithm for informative path planning is presented that leverages tools from information theory and formal control synthesis, and is proven to give a path that satisfies the given temporal logic constraints. The algorithm uses a receding horizon approach in order to provide a reactive, on-line solution while mitigating computational complexity. Statistics compiled from multiple simulation studies indicate that this algorithm performs better than a baseline exhaustive search approach.Comment: Extended version of paper accepted to 2013 IEEE International Conference on Robotics and Automation (ICRA

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks

The recent advancements in the Internet of Things (IoT) and Wireless Multimedia Sensor Networks (WMSN) made high-speed multimedia streaming, data processing, and essential analytics processes with minimal delay. Multimedia sensors used in WMSN-based surveillance applications are beneficial helpful in attaining accurate and elaborate details. However, it has become essential to design an effective and lightweight solution for data traffic management in WMSN owing to the massive quantities of data, generated by multimedia sensors. The development of Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to investigate, collect, store, and process multimedia streaming data for decision-making in real-time scenarios. In this aspect, the current study develops an Integrated AI technique for Video Surveillance in IoT-enabled WMSN, called IAIVS-WMSN. The proposed IAIVS-WMSN technique aims to design a practical scheme for object detection and data transmission in WMSN. The proposed IAIVS-WMSN approach encompasses three stages: object detection, image compression, and clustering. The Mask Regional Convolutional Neural Network (Mask RCNN) technique is primarily utilized for object detection in the target region. Besides, Neighbourhood Correlation Sequence-based Image Compression (NCSIC) technique is applied to reduce data transmission. Finally, Artificial Flora Algorithm (AFA)-based clustering technique is designed for the election of Cluster Heads (CHs) and construction clusters. The design of object detection with compression and clustering techniques for WMSN shows the novelty of the work. These three processes’ designs enable one to accomplish effective data transmission in IoT-enabled WMSN. The researchers conducted multiple simulations to highlight the supreme performance of the IAIVS-WMSN approach. The simulation outcomes inferred the enhanced performance of the IAIVS-WMSN algorithm to the existing approaches

Optimization of Machining Parameters in Turning Operation Using PSO and AIS Algorithms: A Survey

In recent manufacturing, the optimization of turning processes is one of important problems which aim to increase competitiveness and product quality. However, the choice of optimal machining parameters is difficult and complex. Traditionally, the selections is heavily relies on trial and error methods which is tedious and unreliable. Metaheuristics methods have been proposed over the last decade to overcome these problems. This paper presents a survey for optimizing the parameters of turning operation using Particle Swarm Optimization (PSO) and Artificial Immune System (AIS). This study deals with different machining performance in turning operation like surface roughness, material removal rate , tool wear , tool life, production cost, machining time and cutting temperature. Most papers in the field of turning parameters optimization are based on (PSO) algorithms, but only a few efforts that are using (AIS) algorithms. In addition, there is a gap of several machining operation parameters especially for cutting temperature optimization in turning operation using PSO and AIS

Asymmetric Hashing for Fast Ranking via Neural Network Measures

Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on item ranking tasks with generic searching/matching measures (including complex measures such as neural network measures). However, since these ANN approaches must go through the neural measures several times during ranking, the computation is not practical if the neural measure is a large network. On the other hand, fast item ranking using existing hashing-based approaches, such as Locality Sensitive Hashing (LSH), only works with a limited set of measures. Previous learning-to-hash approaches are also not suitable to solve the fast item ranking problem since they can take a significant amount of time and computation to train the hash functions. Hashing approaches, however, are attractive because they provide a principle and efficient way to retrieve candidate items. In this paper, we propose a simple and effective learning-to-hash approach for the fast item ranking problem that can be used for any type of measure, including neural network measures. Specifically, we solve this problem with an asymmetric hashing framework based on discrete inner product fitting. We learn a pair of related hash functions that map heterogeneous objects (e.g., users and items) into a common discrete space where the inner product of their binary codes reveals their true similarity defined via the original searching measure. The fast ranking problem is reduced to an ANN search via this asymmetric hashing scheme. Then, we propose a sampling strategy to efficiently select relevant and contrastive samples to train the hashing model. We empirically validate the proposed method against the existing state-of-the-art fast item ranking methods in several combinations of non-linear searching functions and prominent datasets

Improved Modified Chaotic Invasive Weed Optimization Approach to Solve Multi-Target Assignment for Humanoid Robot

The paper presents an improved modified chaotic invasive weed optimization (IMCIWO) approach for solving a multi-target assignment for humanoid robot navigation. MCIWO is improved by utilizing the Bezier curve for smoothing the path and replaces the conventional split lines. In order to efficiently determine subsequent locations of the robot from the present location on the provided terrain, such that the routes to be specifically generated for the robot are relatively small, with the shortest distance from the barriers that have been generated using the IMCIWO approach. The MCIWO approach designed the path based on obstacles and targets position which is further smoothened by the Bezier curve. Simulations are performed which is further validated by real-time experiments in WEBOT and NAO robot respectively. They show good effectiveness with each other with a deviation of under 5%. Ultimately, the superiority of the developed approach is examined with existing techniques for navigation, and findings are substantially improved