72 research outputs found
Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots
This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement
INTELLIGENT MANUFACTURING SYSTEMS β with robotics and artificial intelligence backgrounds
ΠΠ²Π°Ρ ΠΎΡΠ½ΠΎΠ²Π½ΠΈ ΡΡΠ±Π΅Π½ΠΈΠΊ ΠΎΠ±ΡΡ
Π²Π°ΡΠ° Π²ΠΈΡΠ΅Π΄Π΅ΡΠ΅Π½ΠΈΡΡΠΊΠ° ΠΈΡΠΊΡΡΡΠ²Π° Π°ΡΡΠΎΡΠ° ΠΎΡΡΠ²Π°ΡΠ΅Π½Π° ΠΊΠ°ΠΊΠΎ ΠΊΡΠΎΠ· ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡΡ Π΄ΠΎΠΊΡΠΎΡΡΠΊΠΈΡ
Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ°, ΠΌΠ°Π³ΠΈΡΡΠ°ΡΡΠΊΠΈΡ
ΠΈ ΠΌΠ°ΡΡΠ΅Ρ ΡΠ΅Π·Π°, ΠΊΠ°ΠΎ ΠΈ ΠΏΡΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π½Π°ΡΡΠ½ΠΎ-ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΊΠΈΡ
ΠΏΡΠΎΡΠ΅ΠΊΠ°ΡΠ° Ρ Π΄ΠΎΠΌΠ΅Π½Ρ ΡΠ°Π·Π²ΠΎΡΠ° ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°, ΡΠ°ΠΊΠΎ ΠΈ ΡΠΎΠΊΠΎΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π½ΠΎΠ³ ΠΏΡΠΎΡΠ΅ΡΠ° ΠΈ ΡΠ°Π΄Π° ΡΠ° Π±ΡΠΎΡΠ½ΠΈΠΌ ΡΡΡΠ΄Π΅Π½ΡΠΈΠΌΠ° Π½Π° ΠΎΠ±Π°Π²Π΅Π·Π½ΠΈΠΌ
ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠΈΠΌΠ° ΠΌΠ°ΡΡΠ΅Ρ Π°ΠΊΠ°Π΄Π΅ΠΌΡΠΊΠΈΡ
ΡΡΡΠ΄ΠΈΡΠ° ΠΠ°ΡΠ΅Π΄ΡΠ΅ Π·Π° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎ ΠΌΠ°ΡΠΈΠ½ΡΡΠ²ΠΎ ΠΏΠΎΠ΄ Π½Π°Π·ΠΈΠ²ΠΎΠΌ ΠΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΡΠΊΠΈ ΡΠΈΡΡΠ΅ΠΌΠΈ, ΠΠ½Π΄ΡΡΡΡΠΈΡΡΠΊΠΈ ΡΠΎΠ±ΠΎΡΠΈ ΠΈ ΠΠ΅ΡΠΎΠ΄Π΅ ΠΎΠ΄Π»ΡΡΠΈΠ²Π°ΡΠ°, Π° ΠΎΠ΄ 2020. Π³ΠΎΠ΄ΠΈΠ½Π΅ ΠΈ Π½Π° Π½ΠΎΠ²ΠΎΡΡΠΏΠΎΡΡΠ°Π²ΡΠ΅Π½ΠΎΠΌ Π‘ΡΡΠ΄ΠΈΡΡΠΊΠΎΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡ ΠΌΠ°ΡΡΠ΅Ρ Π°ΠΊΠ°Π΄Π΅ΠΌΡΠΊΠΈΡ
ΡΡΡΠ΄ΠΈΡΠ° ΠΠ½Π΄ΡΡΡΡΠΈΡΠ° 4.0, Ρ ΠΎΠΊΠ²ΠΈΡΡ ΠΎΠ±Π°Π²Π΅Π·Π½ΠΈΡ
ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ° Π ΠΎΠ±ΠΎΡΠΈΠΊΠ° ΠΈ Π²Π΅ΡΡΠ°ΡΠΊΠ° ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠ°, ΠΠ°ΡΠΈΠ½ΡΠΊΠΎ ΡΡΠ΅ΡΠ΅ ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈΡ
ΡΠΎΠ±ΠΎΡΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈ ΠΈΠ·Π±ΠΎΡΠ½ΠΎΠ³ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ° Π’Π΅ΡΠΌΠΈΠ½ΠΈΡΠ°ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈ ΠΏΡΠΎΡΠ΅ΡΠ°.
Π£ ΠΎΠ²ΠΎΠΌ ΠΊΠ°ΠΏΠΈΡΠ°Π»Π½ΠΎΠΌ ΡΡΠ±Π΅Π½ΠΈΠΊΡ, ΠΏΠΎΡΠ΅Π΄ Π΄Π΅ΡΠ°ΡΠ½ΠΎ ΠΎΠ±ΡΠ°ΡΠ΅Π½ΠΈΡ
Π½Π°ΡΡΠ°Π²Π½ΠΈΡ
ΡΠ΅Π»ΠΈΠ½Π° ΠΈ Π±ΡΠΈΠΆΡΠΈΠ²ΠΎ ΠΎΠ΄Π°Π±ΡΠ°Π½ΠΈΡ
ΠΏΡΠΈΠΌΠ΅ΡΠ° Π·Π° Π½Π°Π±ΡΠΎΡΠ°Π½Π΅ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ΅, Π΄Π°ΡΠ΅ ΡΡ ΠΈ ΠΎΠ΄Π³ΠΎΠ²Π°ΡΠ°ΡΡΡΠ΅ ΠΊΠΎΡΠΈΡΠ½Π΅ Π΄ΠΈΡΠΊΡΡΠΈΡΠ΅ Π°ΡΡΠΎΡΠ° Ρ Π΄ΠΎΠΌΠ΅Π½Ρ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎ ΠΎΡΠΈΡΠ΅Π½ΡΠΈΡΠ°Π½ΠΈΡ
Π½Π°ΠΏΡΠ΅Π΄Π½ΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ°, ΡΠΎΠ±ΠΎΡΠΈΠΊΠ΅ ΠΈ Π²Π΅ΡΡΠ°ΡΠΊΠ΅ ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠ΅, ΠΊΠ°ΠΎ ΠΈ Π±ΠΈΠΎΠ»ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΏΠΈΡΠΈΡΠ°Π½ΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌΠ° ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΡΠ΅.
ΠΡΡΠΎΡΠΈ ΠΎΡΠ΅ΠΊΡΡΡ Π΄Π°, ΠΎΡΠΈΠΌ ΡΡΡΠ΄Π΅Π½ΡΠΈΠΌΠ°, ΠΎΠ²Π° ΠΊΡΠΈΠ³Π° ΠΌΠΎΠΆΠ΅ ΠΊΠΎΡΠΈΡΠ½ΠΎ ΠΏΠΎΡΠ»ΡΠΆΠΈΡΠΈ ΠΌΠ°ΡΡΠ΅Ρ, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ Π΄ΠΈΠΏΠ»ΠΎΠΌΠΈΡΠ°Π½ΠΈΠΌ ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΈΠΌ ΠΈΠ½ΠΆΠ΅ΡΠ΅ΡΠΈΠΌΠ°, Π° ΠΏΠΎΡΠ΅Π±Π½ΠΎ Π΄ΠΎΠΊΡΠΎΡΠ°Π½Π΄ΠΈΠΌΠ° ΠΊΠΎΡΠΈ ΡΠ΅ Π±Π°Π²Π΅ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ΠΌ, ΡΠ°Π·Π²ΠΎΡΠ΅ΠΌ ΠΈ ΡΠ²ΠΎΡΠ΅ΡΠ΅ΠΌ ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠ° ΠΠ½Π΄ΡΡΡΡΠΈΡΠ° 4.0 Ρ ΡΠ°Π²ΡΠ΅ΠΌΠ΅Π½Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎ ΠΎΡΠΈΡΠ΅Π½ΡΠΈΡΠ°Π½Π΅ ΡΠ·Π². Π΄ΠΈΠ³ΠΈΡΠ°Π»Π½Π΅ ΡΠ°Π±ΡΠΈΠΊΠ΅.UNIVERZITET U BEOGRADU - MAΕ INSKI FAKULTET (COBISS.SR-ID - 39849737
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue βAdvances in Artificial Intelligence: Models, Optimization, and Machine Learningβ of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
Chaotic Sand Cat Swarm Optimization
In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm
combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of
the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSOβs core
search process to improve global search performance and convergence behavior. Thus, randomness
in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical
and dynamic properties. In addition to these advantages, low search consistency, local optimum trap,
inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO,
several chaotic maps are implemented for more efficient behavior in the exploration and exploitation
phases. Experiments are conducted on a wide variety of well-known test functions to increase the
reliability of the results, as well as real-world problems. In this study, the proposed algorithm was
applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses
compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This
extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results
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
Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement
Volume measurement plays an important role in the production and processing of food products. Various methods have been
proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction
comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction
have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs
volume measurements using random points. Monte Carlo method only requires information regarding whether random points
fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a
computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with
heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images.
Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from
binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the
water displacement method. In addition, the proposed method is more accurate and faster than the space carving method
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Inverse Kinematic Analysis of Robot Manipulators
An important part of industrial robot manipulators is to achieve desired position and orientation of end effector or tool so as to complete the pre-specified task. To achieve the above stated goal one should have the sound knowledge of inverse kinematic problem. The problem of getting inverse kinematic solution has been on the outline of various researchers and is deliberated as thorough researched and mature problem. There are many fields of applications of robot manipulators to execute the given tasks such as material handling, pick-n-place, planetary and undersea explorations, space manipulation, and hazardous field etc. Moreover, medical field robotics catches applications in rehabilitation and surgery that involve kinematic, dynamic and control operations. Therefore, industrial robot manipulators are required to have proper knowledge of its joint variables as well as understanding of kinematic parameters. The motion of the end effector or manipulator is controlled by their joint actuator and this produces the required motion in each joints. Therefore, the controller should always
supply an accurate value of joint variables analogous to the end effector position. Even though industrial robots are in the advanced stage, some of the basic problems in
kinematics are still unsolved and constitute an active focus for research. Among these unsolved problems, the direct kinematics problem for parallel mechanism and inverse kinematics for serial chains constitute a decent share of research domain. The forward kinematics of robot manipulator is simpler problem and it has unique or closed form solution. The forward kinematics can be given by the conversion of joint space to Cartesian space of the manipulator. On the other hand inverse kinematics can be determined by the conversion of Cartesian space to joint space. The inverse kinematic of the robot manipulator does not provide the closed form solution. Hence, industrial manipulator can achieve a desired task or end effector position in more than one
configuration. Therefore, to achieve exact solution of the joint variables has been the main concern to the researchers. A brief introduction of industrial robot manipulators, evolution and classification is
presented. The basic configurations of robot manipulator are demonstrated and their benefits and drawbacks are deliberated along with the applications. The difficulties to solve forward and inverse kinematics of robot manipulator are discussed and solution of inverse kinematic is introduced through conventional methods. In order to accomplish the desired objective of the work and attain the solution of inverse kinematic problem an efficient study of the existing tools and techniques has been done. A review of literature survey and various tools used to solve inverse kinematic problem on different aspects is discussed. The various approaches of inverse kinematic solution is categorized in four sections namely structural analysis of mechanism, conventional approaches, intelligence or soft computing approaches and optimization based
approaches. A portion of important and more significant literatures are thoroughly discussed and brief investigation is made on conclusions and gaps with respect to the inverse kinematic solution of industrial robot manipulators. Based on the survey of
tools and techniques used for the kinematic analysis the broad objective of the present research work is presented as; to carry out the kinematic analyses of different
configurations of industrial robot manipulators. The mathematical modelling of selected robot manipulator using existing tools and techniques has to be made for the comparative study of proposed method. On the other hand, development of new algorithm and their mathematical modelling for the solution of inverse kinematic
problem has to be made for the analysis of quality and efficiency of the obtained solutions. Therefore, the study of appropriate tools and techniques used for the solution of inverse kinematic problems and comparison with proposed method is considered. Moreover, recommendation of the appropriate method for the solution of inverse kinematic problem is presented in the work.
Apart from the forward kinematic analysis, the inverse kinematic analysis is quite complex, due to its non-linear formulations and having multiple solutions. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network (ANN) can be gainfully used to yield the desired results. Therefore, in the present work several
models of artificial neural network (ANN) are used for the solution of the inverse kinematic problem. This model of ANN does not rely on higher mathematical formulations and are adept to solve NP-hard, non-linear and higher degree of polynomial equations. Although intelligent approaches are not new in this field but
some selected models of ANN and their hybridization has been presented for the comparative evaluation of inverse kinematic. The hybridization scheme of ANN and an
investigation has been made on accuracies of adopted algorithms. On the other hand, any Optimization algorithms which are capable of solving various
multimodal functions can be implemented to solve the inverse kinematic problem. To overcome the problem of conventional tool and intelligent based method the optimization based approach can be implemented. In general, the optimization based approaches are more stable and often converge to the global solution. The major problem of ANN based approaches are its slow convergence and often stuck in local optimum point. Therefore, in present work different optimization based approaches are considered. The formulation of the objective function and associated constrained are
discussed thoroughly. The comparison of all adopted algorithms on the basis of number of solutions, mathematical operations and computational time has been presented. The thesis concludes the summary with contributions and scope of the future research work
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
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