scholarly journals Hybrid Approach to Implement Multi-robotic Navigation System Using Neural Network, Fuzzy Logic and Bio-inspired Optimization Methodologies

2021 ◽  
Author(s):  
Shahanaz Ayub ◽  
Navneet Singh ◽  
Md. Zair Hussain ◽  
Mohd Ashraf ◽  
Dinesh Kumar Singh ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Path Planning ◽  
Robot Navigation ◽  
Optimization Technique ◽  
Optimal Path ◽  
Hybrid Approach ◽  
Initial Point ◽  
Target Position ◽  
Multi Robot

Abstract Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi-robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta-heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi-robotics to travel from the initial point to such goal. The fundamental purpose of portable multi-robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio-inspired methods that are exploited to optimize hybrid neuro-fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the Particle Swarm optimization technique (PSO) in real-time scenarios. Several optimization approaches of bio-inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi-robot navigation accuracy in terms of navigation duration and length of the path.

Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm

2019 ◽  
Vol 7 (3) ◽  
pp. 112-119 ◽  
Author(s):  
Asita Kumar Rath ◽  
Dayal R. Parhi ◽  
Harish Chandra Das ◽  
Priyadarshi Biplab Kumar ◽  
Manoj Kumar Muni ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Path Planning ◽  
Humanoid Robot ◽  
Fuzzy Logic Controller ◽  
Target Location ◽  
Target Position ◽  
Content Type ◽  
Hybrid Controller ◽  
Fuzzy Genetic

Purpose Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues. Design/methodology/approach Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup. Findings By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors. Originality/value Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

Optimal path planning for robot navigation by the Hopfield net

1995 ◽  
Author(s):  
G. Castellano ◽  
Ettore Stella ◽  
Giovanni Attolico ◽  
Arcangelo Distante
Keyword(s):  
Path Planning ◽  
Robot Navigation ◽  
Optimal Path ◽  
Optimal Path Planning

scholarly journals Obstacle Avoidance Techniques for Robot Path Planning

2019 ◽  
Vol 12 (1) ◽  
pp. 56-65
Author(s):  
Ali N. Abdulnabi
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Robot Manipulator ◽  
Optimal Path ◽  
Initial Point ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Robot Arm ◽  
Starting Position ◽  
Elapsed Time

This paper presents a collision-free path planning approaches based on Bézier curve and A-star algorithm for robot manipulator system. The main problem of this work is to finding a feasible collision path planning from initial point to final point to transport the robot arm from the preliminary to the very last within the presence of obstacles, a sequence of joint angles alongside the path have to be determined. To solve this problem several algorithms have been presented among which it can be mention such as Bug algorithms, A-Star algorithms, potential field algorithms, Bézier curve algorithm and intelligent algorithms. In this paper obstacle avoidance algorithms were proposed Bézier and A-Star algorithms, through theoretical studies and simulations with several different cases, it's found verify the effectiveness of the methods suggested. It's founded the Bézier algorithm is smoothing accurate, and effective as compare with the A-star algorithm, but A-star is near to shortest and optimal path to free collision avoidance. The time taken and the elapsed time to traverse from its starting position and to reach the goal are recorded the tabulated results show that the elapsed time with different cases to traverse from the start location to destination using A-star Algorithm is much less as compared to the time taken by the robot using Bézier Algorithm to trace the same path. The robot used was the Lab-Volt of 5DOF Servo Robot System Model 5250 (RoboCIM5250)

Deep Learning for Three-Dimensional Volumetric Recovery of Cloud Fields

2021 ◽  
Author(s):  
Yael Sde-Chen ◽  
Yoav Y. Schechner ◽  
Vadim Holodovsky ◽  
Eshkol Eytan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Optimization Technique ◽  
Hybrid Approach ◽  
Three Dimensional ◽  
Physical Nature ◽  
Key Factor ◽  
Volumetric Reconstruction ◽  
Real World Datasets

<p>Clouds are a key factor in Earth's energy budget and thus significantly affect climate and weather predictions. These effects are dominated by shallow warm clouds (shown by Sherwood et al., 2014, Zelinka et al., 2020) which tend to be small and heterogenous. Therefore, remote sensing of clouds and three-dimensional (3D) volumetric reconstruction of their internal properties are of significant importance.</p><p>Recovery of the volumetric information of the clouds relies on 3D radiative transfer, that models 3D multiple scattering. This model is complex and nonlinear. Thus, inverting the model poses a major challenge and typically requires using a simplification. A common relaxation assumes that clouds are horizontally uniform and infinitely broad, leading to one-dimensional modeling. However, generally this assumption is invalid since clouds are naturally highly heterogeneous. A novel alternative is to perform cloud retrieval by developing tools of 3D scattering tomography. Then, multiple satellite images of the clouds are acquired from different points of view. For example, simultaneous multi-view radiometric images of clouds are proposed by the CloudCT project, funded by the ERC. Unfortunately, 3D scattering tomography require high computational resources. This results, in practice, in slow run times and prevents large scale analysis. Moreover, existing scattering tomography is based on iterative optimization, which is sensitive to initialization.</p><p>In this work we introduce a deep neural network for 3D volumetric reconstruction of clouds. In recent years, supervised learning using deep neural networks has led to remarkable results in various fields ranging from computer vision to medical imaging. However, these deep learning techniques have not been extensively studied in the context of volumetric atmospheric science and specifically cloud research.</p><p>We present a convolutional neural network (CNN) whose architecture is inspired by the physical nature of clouds. Due to the lack of real-world datasets, we train the network in a supervised manner using a physics-based simulator that generates realistic volumetric cloud fields. In addition, we propose a hybrid approach, which combines the proposed neural network with an iterative physics-based optimization technique.</p><p>We demonstrate the recovery performance of our proposed method in cloud fields. In a single cloud-scale, our resulting quality is comparable to state-of-the-art methods, while run time improves by orders of magnitude. In contrast to existing physics-based methods, our network offers scalability, which enables the reconstruction of wider cloud fields. Finally, we show that the hybrid approach leads to improved retrieval in a fast process.</p>

Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control

2017 ◽  
Vol 89 ◽  
pp. 95-109 ◽  
Author(s):  
Azzeddine Bakdi ◽  
Abdelfetah Hentout ◽  
Hakim Boutami ◽  
Abderraouf Maoudj ◽  
Ouarda Hachour ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Path Planning ◽  
Mobile Robots ◽  
Fuzzy Logic Control ◽  
Optimal Path ◽  
Adaptive Fuzzy ◽  
Optimal Path Planning ◽  
Logic Control ◽  
Adaptive Fuzzy Logic
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