Abstract
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.
Highlights
Mobile robot control has been widely used in several applications, such as navigation, obstacle avoidance, path planning, and cooperative transport
To verify the performance of navigation control, two different test environments were created for testing whether the robots successfully accomplished cooperative carrying and navigation control
Based on a control dynamicfor group differential evolution realize theenvironment, carrying control study proposed an based on a dynamic group differential evolution to realize the carrying
Summary
Mobile robot control has been widely used in several applications, such as navigation, obstacle avoidance, path planning, and cooperative transport. Adopted four sonar sensors and used the data from these sensors as input signals In this application, the speeds of the left and right wheel are the outputs of the fuzzy neural network controller. Some researchers [8,9] have used the type-1 fuzzy set to solve uncertain problems, the control performance in the real environment is not optimal. The traditional DE method has a disadvantage, in that it can become trapped in a local optimal solution To eliminate this disadvantage, an improved DE is proposed for solving mobile robot control problems. An efficient interval type-2 fuzzy neural controller (IT2FNC) based on dynamic group differential evolution (DGDE) was designed to implement the carrying control and wall-following mode (WFM) control for mobile robots. The experimental results demonstrated that the proposed DGDE learning algorithm is superior to other algorithms at using WFM control
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