scholarly journals PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

2021 ◽  
Vol 11 (24) ◽  
pp. 11940
Author(s):  
Allisa J. Dalpe ◽  
May-Win L. Thein ◽  
Martin Renken
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Technology Development ◽  
New Technology ◽  
Evaluation Framework ◽  
Test Environment ◽  
Acceptable Risks ◽  
Fuzzy Logic Approach ◽  
Interval Type

Trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The authors introduce the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e., providing direct comparisons and quantitative evaluations between autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g., for surveying, surveillance, search and rescue). Three case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission.

scholarly journals Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

2021 ◽  
Vol 54 (3-4) ◽  
pp. 303-323
Author(s):  
Amjad J Humaidi ◽  
Huda T Najem ◽  
Ayad Q Al-Dujaili ◽  
Daniel A Pereira ◽  
Ibraheem Kasim Ibraheem ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Parallel Robot ◽  
Design Parameters ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Interval Type

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

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