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 recent decades, the trend of using zero-emission vehicles has been constantly evolving. This trend brings about not only the pressure to develop electric vehicles (EVs) or hybrid electric vehicles (HEVs) but also the demand for further developments in battery technologies and safe use of battery systems. Concerning the safe usage of battery systems, Battery Management Systems (BMS) play one of the most important roles. A BMS is used to monitor operating temperature and State of Charge (SoC), as well as protect the battery system against cell imbalance. The paper aims to present hardware and software designs of a BMS for unmanned EVs, which use Lithium multi-cell battery packs. For higher modularity, the designed BMS uses a distributed topology and contains a master module with more slave modules. Each slave module is in charge of monitoring and protecting a multi-cell battery pack. All information about the state of each battery pack is sent to the master module which saves and sends all data to the control station if required. Controlled Area Network (CAN) bus and Internet of Things technologies are designed for requirements from different applications for communications between slave modules and the master module, and between the master module and control station.

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.