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Abstract
The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot’s independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab® and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research.
Highlights
Collecting Results parameters of the Differential Drive Wheeled Mobile Robot (DDWMR) used for the simulation in this study are taken
The parameters of the DDWMR used for the simulation in this study are taken from
Running the simulation with its default configuration and setting up the population size at 50, all the results of these experiments in 200th generation for the zigzag path are shown in Figures 13 and 14 and Tables 10 and 11
Summary
To fulfill its series of tasks in different places, the robot has to move from one place to another place in a known or an unknown environment. Of a mobile robot are still the crucial and fundamental problems for the robot operation. These control problems have been widely researched and published, such as the obstacle avoidance with minimum travel time [1], the go-to-goal control without obstacle avoidance [2,3], the leader following control [4], the trajectory tracking control [5,6,7], the wall-following control [8], the obstacle avoidance [9,10,11]. Several well-known controllers such as the controller proposed by Kanayama and Robins Mathew, the feedback-based controller for the circular path, the Lyapunov-based controller, the clever trigonometry-based controller, and the Dubins path-based controller, have been discussed in [12]
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