In order to select the best controller for a Differential Drive Wheeled Mobile Robot (DDWMR), an energy consumption comparison relating to tracking accuracy is used as a very strict criterion. Therefore, this paper reviews some well-known controllers designed for the DDWMR. Furthermore, there are presented several experiments with the extensible open-source code programmed in Python. Such an extensible open-source code presentation could serve as a tool for simulating, comparing, and evaluating a set of different control algorithms. The kinematic and dynamic models of the DDWMR and control algorithms are implemented in this open-source code to determine a travel time, a distance between the robot’s position and a given path, a linear velocity, an angular velocity, a travel path length, and a total kinetic energy loss of the DDWMR. These simulation results are used to compare and evaluate the given control algorithms. Moreover, the simulation results also enable to answer the question of whether a significant increase in energy consumption is worth shortening the travel path by just a bit. Finally, this paper includes a direct link to the stored experiments which are runnable and could serve as a proof. Besides, users can also easily supplement with other controllers and different paths to evaluate robot tracking control algorithms.

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.

Unmanned aerial vehicles (UAVs) are increasingly attracting investment and development attention from many countries all over the world due to their great advantages. However, one of the biggest challenges for researchers is the problem of supplying energy to UAVs to ensure they can operate for a longer time. Especially in the case of rotary wings, they consume more energy than other UAV types as the motors need to spend a lot of energy to operate in order to overcome the gravity of the earth. The article aims to research power supply, energy consumption on UAVs, and a method of taking advantage of external energy sources to provide power for the operation of UAVs and discuss UAVs’ structure, categories, and control. Two experiments were conducted separately to evaluate the energy consumption of UAVs and the energy conversion from external energy sources to electrical energy. A test bench was designed to evaluate and determine the maximum efficiency using regenerative braking mode. The measuring device was manufactured to measure the necessary parameters to calculate the energy consumption and performance of the system. Experimental numerical results show that energy conversion from external sources is one of methods that can help increase the flight time of the UAV.

In the problem of designing a Fuzzy Logic Controller (FLC) for robots in general and a Differential Drive Robot (DDR) in particular, the determination of the parameters of Membership Functions (MFs) and Fuzzy Rules (FRs) is a difficult, complicated, and time-consuming problem because this is mainly based on heuristics and the knowledge of experts. Therefore, this paper provides a new method to efficiently design an FLC for the DDR by using the Genetic Algorithm (GA). Here, the GA is used to generate and optimize both the parameters of MFs and the FRs according to the minimum kinetic energy loss criterion. For this purpose, a program is created in Google Colab^® by using the Python language with the help of the “Pymoo” library to not only automatically generate all the suboptimal parameters of MFs and the suboptimal FRs but also the simulate and evaluate different used FLCs. This program is published as an open-source code so that all readers can browse, view, run, and modify the code themselves to design their FLC. The simulation results have shown that the designed FLC is much better than other used FLCs in terms of the minimum kinetic energy loss while other control performances are still good.

This paper aims to introduce a design of a COTS system that uses available components to monitor UAV’s accumulators. These accumulators can measure important parameters to analyze and prediction operation of an accumulator system. Besides, they can transmit the data to a computer with the help of the technology IoT. This COTS system will be administered by the microcontroller ESP-32, which connects the system and the computer.

This paper aims to present a simulating and setting parameter method for a quadcopter to evaluate and improve the effectiveness and stability of the system. The system is set up on a platform that could be moved freely on a vertical axis. Data from sensors and measuring equipment will be transmitted in real-time to the computer. Batteries or an external DC power supply are used to power the system.