scholarly journals Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 298 ◽  
Author(s):  
Jyun-Yu Jhang ◽  
Cheng-Jian Lin ◽  
Kuu-Young Young
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Target Location ◽  
Learning Strategy ◽  
Control Method ◽  
Behavioral Control ◽  
Differential Evolution Algorithm ◽  
Experimental Results ◽  
Unknown Environment ◽  
Navigation Control

This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes—wall-following mode (WFM) and toward-goal mode (TGM)—based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.

scholarly journals Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4181 ◽  
Author(s):  
Chun-Hui Lin ◽  
Shyh-Hau Wang ◽  
Cheng-Jian Lin
Keyword(s):  
Mobile Robots ◽  
Control Method ◽  
Fuzzy Controller ◽  
Bee Colony ◽  
Load Carrying ◽  
Navigation Control ◽  
Interval Type ◽  
Navigation Method ◽  
Neural Fuzzy

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

Effects of Multi-Robot Team Formations on Distributed Area Coverage

2019 ◽  
pp. 1192-1219
Author(s):  
Prithviraj Dasgupta ◽  
Taylor Whipple ◽  
Ke Cheng
Keyword(s):  
Mobile Robots ◽  
Area Coverage ◽  
Experimental Results ◽  
Wheel Slip ◽  
Robot System ◽  
Multiple Mobile Robots ◽  
Unknown Environment ◽  
Robot Teams ◽  
And Performance ◽  
Multi Robot

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.

Effects of Multi-Robot Team Formations on Distributed Area Coverage

2011 ◽  
Vol 2 (1) ◽  
pp. 44-69 ◽  
Author(s):  
Prithviraj Dasgupta ◽  
Taylor Whipple ◽  
Ke Cheng
Keyword(s):  
Mobile Robots ◽  
Area Coverage ◽  
Experimental Results ◽  
Wheel Slip ◽  
Robot System ◽  
Multiple Mobile Robots ◽  
Unknown Environment ◽  
Robot Teams ◽  
And Performance ◽  
Multi Robot

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.

Localization and Follow-the-Leader Control of a Heterogeneous Group of Mobile Robots

2005 ◽  
Author(s):  
Jiangyang Huang ◽  
Shane M. Farritor ◽  
Ala’ Qadi ◽  
Steve Goddard
Keyword(s):  
Statistical Analysis ◽  
Mobile Robots ◽  
Control Method ◽  
Planetary Exploration ◽  
Highway Safety ◽  
Experimental Results ◽  
Work Zones ◽  
Localization Method ◽  
Control Traffic ◽  
Safety Applications

This paper investigates the control and localization of a heterogeneous (different sensor, mechanical, computational capabilities) group of mobile robots. The group considered here has several inexpensive sensor-limited and computationally-limited robots which follow a leader robot in a desired formation over long distances. This situation is similar to a search, de-mining, or planetary exploration situation where there are several deployable/disposable robots led by a more sophisticated leader. Specifically, the robots in this paper are designed for highway safety applications where they automatically deploy and maneuver safety barrels commonly used to control traffic in highway work zones. Complex sensing and computation are performed by the leader while the followers perform simple operations under the leader’s guidance. This architecture allows followers to be simple, inexpensive and have minimal sensors. Theoretical and statistical analysis of a tracking-based localization method is provided. A simple follow-the-leader control method is also presented. Experimental results of localization and follow-the-leader formation-motion are included.

scholarly journals Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 120
Author(s):  
Javier Muñoz ◽  
Blanca López ◽  
Fernando Quevedo ◽  
Concepción A. Monje ◽  
Santiago Garrido ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Target Location ◽  
Differential Evolution Algorithm ◽  
Maximum Probability ◽  
Sweep Angle ◽  
Data Set ◽  
Probabilistic Distribution ◽  
Sweep Direction ◽  
Gaussian Probability Distribution ◽  
Evolution Algorithm

In this paper, we propose a coverage method for the search of lost target or debris on the ocean surface. The OSCAR data set is used to determine the marine currents and the differential evolution genetic filter is used to optimize the sweep direction of the lawnmower coverage and get the sweep angle for the maximum probability of containment. The position of the target is determined by a particle filter, where the particles are moved by the ocean currents and the final probabilistic distribution is obtained by fitting the particle positions to a Gaussian probability distribution. The differential evolution algorithm is then used to optimize the sweep direction that covers the highest probability of containment cells before the less probable ones. The algorithm is tested with a variety of parameters of the differential evolution algorithm and compared to other popular optimization algorithms.

A New Differential Evolution Algorithm with Random Parameters

2014 ◽  
Vol 556-562 ◽  
pp. 3614-3621
Author(s):  
Shao Rong Huang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Iteration Process ◽  
Experimental Results ◽  
Random Numbers ◽  
Optimal Selection ◽  
Random Parameters ◽  
Evolution Algorithm ◽  
Selection Of

Differential evolution algorithm’s performance often depends heavily on the parameter settings. Based on analyzing the influence of the parameters setting in the experiment, the effects and the optimal selection of those major parameters on DE are analyzed, and some conclusions are derived. A new differential evolution algorithm which the scale constant (F) and crossover constant (CR) are generated as random numbers within a certain range in each iteration process is proposed. The experimental results shows that the new algorithm is simple, easy to realize and can get higher precision and better stability.

Effects of Multi-Robot Team Formations on Distributed Area Coverage

2013 ◽  
pp. 260-286
Author(s):  
Prithviraj Dasgupta ◽  
Taylor Whipple ◽  
Ke Cheng
Keyword(s):  
Mobile Robots ◽  
Area Coverage ◽  
Experimental Results ◽  
Wheel Slip ◽  
Robot System ◽  
Multiple Mobile Robots ◽  
Unknown Environment ◽  
Robot Teams ◽  
And Performance ◽  
Multi Robot

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.

scholarly journals An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Tae Jong Choi ◽  
Chang Wook Ahn ◽  
Jinung An
Keyword(s):  
Differential Evolution ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Cauchy Distribution ◽  
Parameter Control ◽  
Control Parameters ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Parameter Values ◽  
Adaptive Parameter Control

Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals’ parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

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