Control structure for a car-like robot using artificial neural networks and genetic algorithms

The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison workCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQNão te

Optimal Robotic Path Planning Using Intlligents Search Algorithms

This investigation investigates the application of Adjusted Fuzzy Molecule Swarm Optimization (FPSO) to the versatile robot route issue in arrange to decide the briefest conceivable course with the least time required to travel from a beginning area to a goal area in a deterrent working zone. MPSO is being created in this ponder to progress the capability of customized calculations for a worldwide course. The proposed calculations decipher the environment outline spoken to by the framework show and develop an idea or nearly ideal collision-free way. Reenactment tests appear the viability of the most recent organized calculation for portable robot course arranging. The programs are composed in MATLAB R2019a and run on 2.65 GHz Intel Center i5 and 7 GB Smash computers. Changes proposed in MPSO and cuckoo look calculation fundamentally point to resolve the untimely merging issue related to the beginning PSO. A mistake calculate is demonstrated within the MPSO to guarantee the meeting of the PSO. FPSO points to handle another issue which is the populace may incorporate a few infeasible ways; an updated strategy is tired the FPSO to fathom the issue of the infeasible street. The discoveries illustrate that this calculation has huge potential to fathom the course arranging with satisfactory comes about in terms of decreasing remove and time for execution

Geometry based Three-Dimensional Image Processing Method for Electronic Cluster Eye

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkIn recent years, much attention has been paid to the electronic cluster eye (eCley), a new type of artificial compound eyes, because of its small size, wide field of view (FOV) and sensitivity to motion objects. An eCley is composed of a certain number of optical channels organized as an array. Each optical channel spans a small and fixed field of view (FOV). To obtain a complete image with a full FOV, the images from all the optical channels are required to be fused together. The parallax from unparallel neighboring optical channels in eCley may lead to reconstructed image blurring and incorrectly estimated depth. To solve this problem, this paper proposes a geometry based three-dimensional image processing method (G3D) for eCley to obtain a complete focused image and dense depth map. In G3D, we derive the geometry relationship of optical channels in eCley to obtain the mathematical relation between the parallax and depth among unparallel neighboring optical channels. Based on the geometry relationship, all of the optical channels are used to estimate the depth map and reconstruct a focused image. Subsequently, by using an edge-aware interpolation method, we can further gain a sharply focused image and a depth map. The effectiveness of the proposed method is verified by the experimental results

Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots

Navigational path planning problems of the mobile robots have received considerable attention over the past few decades. The navigation problem of mobile robots are consisting of following three aspects i.e. locomotion, path planning and map building. Based on these three aspects path planning algorithm for a mobile robot is formulated, which is capable of finding an optimal collision free path from the start point to the target point in a given environment. The main objective of the dissertation is to investigate the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach. Firstly, three different standalone computational intelligence approaches based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) algorithm and Invasive Weed Optimization (IWO) are presented to address the problem of path planning in unknown environments. Next two different hybrid approaches are developed using CS-ANFIS and IWO-ANFIS to solve the mobile robot navigation problems. The performance of each intelligent navigational controller is demonstrated through simulation results using MATLAB. Experimental results are conducted in the laboratory, using real mobile robots to validate the versatility and effectiveness of the proposed navigation techniques. Comparison studies show, that there are good agreement between them. During the analysis of results, it is noticed that CS-ANFIS and IWO-ANFIS hybrid navigational controllers perform better compared to other discussed navigational controllers. The results obtained from the proposed navigation techniques are validated by comparison with the results from other intelligent techniques such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and other hybrid algorithms. By investigating the results, finally it is concluded that the proposed navigational methodologies are efficient and robust in the sense, that they can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial algorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based trajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the optimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in terms of detection efficiency and computational time

Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots

YesThis paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired velocity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional-integral-derivative controllers, where neural networks and experts’ knowledge are applied to tune parameters. Extensive experiments conducted on the simulated wheeled mobile robots show the effectiveness of this approach.The work of XW and GZ is supported by the National Natural Science Foundation of China (61170016, 61373047). The work of MG, FI and RL was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (project number: PN-II-ID-PCE-2011-3-0688)

A review: On path planning optimization criteria and mobile robot navigation

Mobile robots are growing more significant from time to time and have been applied to many fields such as agriculture, space, and even human life. It could improve mobile robot navigation efficiency, ensure path planning safety and smoothness, minimize time execution, etc. The main focus of mobile robots is to have the most optimal functions. An intelligent mobile robot is required to travel autonomously in various environments, static and dynamic. This paper article presents the optimization criteria for mobile robot path planning to figure out the most optimal mobile robot criteria to fulfill, including modeling analysis, path planning and implementation. Path length and path smoothness are the most parameters used in optimization in mobile robot path planning. Based on path planning, the mobile robot navigation is divided into three categories: global navigation, local navigation and personal navigation. Then, we review each category and finally summarize the categories in a map and discuss the future research strategies

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

A review: On path planning strategies for navigation of mobile robot

This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics