45 research outputs found
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
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
Mobile robot controller using novel hybrid system
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration
Control of Real Mobile Robot Using Artificial Intelligence Technique
An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently
Genetic Programming to Optimise 3D Trajectories
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTrajectory optimisation is a method of finding the optimal route connecting a start and
end point. The suitability of a trajectory depends on non-intersection with any obstacles
as well as predefined performance metrics. In the context of UAVs, the goal is to minimise
the cost of the route, in terms of energy or time, while avoiding restricted flight zones.
Artificial intelligence techniques including evolutionary computation have been applied to
trajectory optimisation with various degrees of success. This thesis explores the use of
genetic programming (GP) to optimise trajectories in 3D space, by encoding 3D geographic
trajectories as syntax trees representing a curve. A comprehensive review of the relevant
literature is presented, covering the theory and techniques of GP, as well as the principles
and challenges of 3D trajectory optimisation. The main contribution of this work is the
development and implementation of a novel GP algorithm using function trees to encode
3D geographical trajectories. The trajectories are validated and evaluated using a realworld
dataset and multiple objectives. The results demonstrate the effectiveness of the
proposed algorithm, which outperforms existing methods in terms of speed, automaticity,
and robustness. Finally, insights and recommendations for future research in this area are
provided, highlighting the potential for GP to be applied to other complex optimisation
problems in engineering and science
Climbing and Walking Robots
Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study
Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques
The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques
An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control
Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification
On the development and enhancement of artificial intelligence algorithms for swarm robots in real world applications
Swarm robotics is an area where using artificial intelligence (AI) can show a great deal of
improvement. Obstacle avoidance, object detection, mapping and navigation are some
the major algorithms required for successful execution of various tasks in the field of
robotics. There is a challenge in applying these algorithms in a manner that swarm
robots can use effectively. These five areas can be further researched to provide a
platform for real world applications. This research aims to tackle the challenges involved
in applying the aforementioned algorithms to swarm robotics and comparing the results
with single robot systems. These techniques can be optimized by leveraging the
advantage of swarm robots communication and scalability. The proposed algorithms
were tested and validated using swarm robots along with profiling and simulations. For
obstacle avoidance, two algorithms were devoloped. The first used a novel and modified
force field method and the second used artificial neural networks (ANN). The results
showed that the modified force field method performed better for static environments
while ANNs worked better for dynamic environments. For object detection, the proposed
algorithm uses an image classifier developed using ANN. The image classifier was
trained to identify blocks of various colours using a convolutional neural network
technique. This algorithm was then applied to swarm robotics using two proposed
methods and results showed that multiple robots viewing objects from different angles
provided better results as compared to single robot systems. This was validated with a
97% accuracy. In two dimension (2D) mapping, the proposed algorithm was developed
using simultaneous localization and mapping (SLAM). The results showed that a single
robot can require upto 3.5x more time for covering a given area compared to a swarm
size of ten robots. This research shows a great deal of contribution in applying swarm
robotics for surveilance purposes by showcasing the ability for swarm robotics to
coordinate and execute the required task in an efficient time frame. The proposed
three-dimension (3D) mapping algorithm used octomaps and occupancy grids to map out
an image taken from a camera mounted on swarm robots. The images were obtained
from various angles using multiple swarm robots. AI algorithms with a focus on swarm
robotics are developed and enhanced for real world applications including fire-fighting,
surveillance, fault analysis and construction. Results showed that swarm robots were
able to complete a given task by up to six times faster as compared to a single robot. The
overall contribution of this research lays a platform for further applications by
showcasing the effectiveness of robotic algorithms in a swarm robot environment.Heriot-Watt University Fee Scholarshi
Design and analysis of Intelligent Navigational controller for Mobile Robot
Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create
collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner