Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots

Robotica ◽  
2009 ◽  
Vol 27 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Amitava Chatterjee
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Extended Kalman Filtering ◽  
Extended Kalman Filters ◽  
De Algorithm ◽  
Localization And Mapping ◽  
Recent Method ◽  
Fuzzy Supervisor

SUMMARYThe present paper proposes a successful application of differential evolution (DE) optimized fuzzy logic supervisors (FLS) to improve the quality of solutions that extended Kalman filters (EKFs) can offer to solve simultaneous localization and mapping (SLAM) problems for mobile robots and autonomous vehicles. The utility of the proposed system can be readily appreciated in those situations where an incorrect knowledge of Q and R matrices of EKF can significantly degrade the SLAM performance. A fuzzy supervisor has been implemented to adapt the R matrix of the EKF online, in order to improve its performance. The free parameters of the fuzzy supervisor are suitably optimized by employing the DE algorithm, a comparatively recent method, popularly employed now-a-days for high-dimensional parallel direct search problems. The utility of the proposed system is aptly demonstrated by solving the SLAM problem for a mobile robot with several landmarks and with wrong knowledge of sensor statistics. The system could successfully demonstrate enhanced performance in comparison with usual EKF-based solutions for identical environment situations.

scholarly journals Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

2020 ◽  
Vol 13 (6) ◽  
pp. 168-178
Author(s):  
Pyae Cho ◽  
◽  
Thi Nyunt ◽  
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Clustering Algorithm ◽  
Population Based ◽  
Initial Population ◽  
Local Optima ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
Mutation Strategy

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

scholarly journals ALGORITMA DIFFERENTIAL EVOLUTION UNTUK PENJADWALAN FLOW SHOP BANYAK MESIN DENGAN MULTI OBYEKTIF

2012 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Stefanus Eko Wiratno ◽  
Nurdiansyah Rudi ◽  
Budi Santosa
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Computation Time ◽  
Parameter Determination ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Adaptive Parameters ◽  
Local Search Strategy ◽  
Reduce Computation Time

This research focuses on the development of Differential Evolution(DE) algorithmto solve m-machine flow shopscheduling problems with respect to both makespan and total flow time. Development of DE algorithm is done by modifyingthe adaptive parameter determination procedure in order to change the value of adaptive parameters in each generation,adding local search strategy to the algorithm in order to improve the quality of the resulting solutions, as ewell as modifyingthe crossover in order to reduce computation time. The result indicates that the proposed DE algorithm has proven to bebetter than the original DE algorithm, Genetic Algorithm (GA), and for certain cases it also out performs Multi-ObjectiveAnt Colony System Algorithm (MOCSA).

A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhen Zhou ◽  
Dongqing Wang ◽  
Boyang Xu
Keyword(s):  
Mobile Robots ◽  
Strong Nonlinearity ◽  
Forgetting Factor ◽  
Extended Kalman Filtering ◽  
Estimation Errors ◽  
Content Type ◽  
Application Range ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Filtering Effect

Purpose The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity. Design/methodology/approach A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations. Findings The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping. Originality/value The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

An Improved Differential Evolution and its Application in Function Optimization Problem

2011 ◽  
Vol 267 ◽  
pp. 632-634
Author(s):  
Jing Feng Yan ◽  
Chao Feng Guo
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Search Strategy ◽  
Optimization Problems ◽  
Function Optimization ◽  
Final Solution ◽  
De Algorithm ◽  
Ranking Strategy ◽  
The Stability

An Improved Differential evolution (IDE) is proposed in this paper. It has some new features: 1) using multi-parent search strategy and stochastic ranking strategy to maintain the diversity of the population; 2) a novel convex mutation to accelerate the convergence rate of the classical DE algorithm.; The algorithm of this paper is tested on 13 benchmark optimization problems with linear or/and nonlinear constraints and compared with other evolutionary algorithms. The experimental results demonstrate that the performance of IDE outperforms DE in terms of the quality of the final solution and the stability.

scholarly journals Use Improved Differential Evolution Algorithms to Handle the Inverse Kinetics Problem for Robots with Residual Degrees of Freedom

2021 ◽  
Author(s):  
Trung Nguyen ◽  
Tam Bui
Keyword(s):  
Differential Evolution ◽  
Execution Time ◽  
Degrees Of Freedom ◽  
Single Point ◽  
Differential Evolution Algorithm ◽  
Adaptive Strategy ◽  
De Algorithm ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms

In this study, the Self-adaptive strategy algorithm for controlling parameters in Differential Evolution algorithm (ISADE) improved from the Differential Evolution (DE) algorithm, as well as the upgraded version of the algorithms has been applied to solve the Inverse Kinetics (IK) problem for the redundant robot with 7 Degree of Freedom (DoF). The results were compared with 4 other algorithms of DE and Particle Swarm Optimization (PSO) as well as Pro-DE and Pro-PSO algorithms. These algorithms are tested in three different Scenarios for the motion trajectory of the end effector of in the workspace. In the first scenario, the IK results for a single point were obtained. 100 points randomly generated in the robot’s workspace was input parameters for Scenario 2, while Scenario 3 used 100 points located on a spline in the robot workspace. The algorithms were compared with each other based on the following criteria: execution time, endpoint distance error, number of generations required and especially quality of the joints’ variable found. The comparison results showed 2 main points: firstly, the ISADE algorithm gave much better results than the other DE and PSO algorithms based on the criteria of execution time, endpoint accuracy and generation number required. The second point is that when applying Pro-ISADE, Pro-DE and Pro-PSO algorithms, in addition to the ability to significantly improve the above parameters compared to the ISADE, DE and PSO algorithms, it also ensures the quality of solved joints’ values.

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Global Numerical Optimization

2013 ◽  
Vol 457-458 ◽  
pp. 1283-1287 ◽  
Author(s):  
Si Ling Feng ◽  
Qing Xin Zhu ◽  
Sheng Zhong ◽  
Xiu Jun Gong
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Global Numerical Optimization ◽  
De Algorithm ◽  
Mutation Operators ◽  
Wide Range ◽  
The Cost

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solution. Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we applied a hybridization of adaptive BBO with DE approach, namely ABBO/DE/GEN, for the global numerical optimization problems. ABBO/DE/GEN adaptively changes migration probability and mutation probability based on the relation between the cost of fitness function and average cost every generation, and the mutation operators of BBO were modified based on DE algorithm and the migration operators of BBO were modified based on number of iteration to improve performance. And hence it can generate the promising candidate solutions. To verify the performance of our proposed ABBO/DE/GEN, 9 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with BBO/DE/GEN approaches, ABBO/DE/GEN performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.

Deterministically guided differential evolution for constrained power dispatch with prohibited operating zones

2013 ◽  
Vol 62 (4) ◽  
pp. 593-603
Author(s):  
J. Jasper ◽  
T. Aruldoss Albert Victoire
Keyword(s):  
Differential Evolution ◽  
Stochastic Approach ◽  
Cost Functions ◽  
Neighborhood Search ◽  
New Approach ◽  
Prohibited Operating Zones ◽  
De Algorithm ◽  
Network Transmission ◽  
Optimum Solution

Abstract This paper presents a new approach to solve economic load dispatch (ELD) problem in thermal units with non-convex cost functions using differential evolution technique (DE). In practical ELD problem, the fuel cost function is highly non linear due to inclusion of real time constraints such as valve point loading, prohibited operating zones and network transmission losses. This makes the traditional methods fail in finding the optimum solution. The DE algorithm is an evolutionary algorithm with less stochastic approach to problem solving than classical evolutionary algorithms.DE have the potential of simple in structure, fast convergence property and quality of solution. This paper presents a combination of DE and variable neighborhood search (VNS) to improve the quality of solution and convergence speed. Differential evolution (DE) is first introduced to find the locality of the solution, and then VNS is applied to tune the solution. To validate the DE-VNS method, it is applied to four test systems with non-smooth cost functions. The effectiveness of the DE-VNS over other techniques is shown in general.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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