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At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that-according to the application and the robot's characteristics-it is recommended to use each of them, based on different conditions.

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The inspection and maintenance of transmission systems are necessary for their proper functioning. In this way, among the line's critical points are the insulator chains, which are responsible for providing insulation between conductors and structures. The accumulation of pollutants on the insulator surface can cause failures in the power system, leading to power supply interruptions. Currently, the cleaning of insulator chains is performed manually by operators who climb towers and use cloths, high-pressure washers, or even helicopters. The use of robots and drones is also under study, presenting challenges to be overcome. This paper presents the development of a drone-robot for cleaning insulator chains. The drone-robot was designed to identify insulators by camera and perform cleaning through a robotic module. This module is attached to the drone and carries a battery-powered portable washer, a reservoir for demineralized water, a depth camera, and an electronic control system. This paper includes a literature review on the state of the art related to strategies used for cleaning insulator chains. Based on this review, the justification for the construction of the proposed system is presented. The methodology used in the development of the drone-robot is then described. The system was validated in a controlled environment and in field experimental tests, with the ensuing discussions and conclusions formulated, along with suggestions for future work.

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Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot's prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.

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This paper presents a navigation strategy for a platoon of n non-holonomic mobile robots with a time-varying spacing policy between each pair of successive robots at the platoon, such that a safe trailing distance is maintained at any speed, avoiding the robots getting too close to each other. It is intended that all the vehicles in the formation follow the trajectory described by the leader robot, which is generated by bounded input velocities. To establish a chain formation among the vehicles, it is required that, for each pair of successive vehicles, the (i+1)-th one follows the trajectory executed by the former i-th one, with a delay of τ(t) units of time. An observer is proposed to estimate the trajectory, velocities, and positions of the i-th vehicle, delayed τ(t) units of time, consequently generating the desired path for the (i+1)-th vehicle, avoiding numerical approximations of the velocities, rendering robustness against noise and corrupted or missing data as well as to external disturbances. Besides the time-varying gap, a constant-time gap is used to get a secure trailing distance between each two successive robots. The presented platoon formation strategy is analyzed and proven by using Lyapunov theory, concluding asymptotic convergence for the posture tracking between the (i+1)-th robot and the virtual reference provided by the observer that corresponds to the i-th robot. The strategy is evaluated by numerical simulations and real-time experiments.

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Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot-robot and human-robot interactions. However, existing approaches are focused either on multi-robot navigation (robot-robot interaction) or social navigation with human presence (human-robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human-Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation.

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The aim of this work is a control scheme implementation to deform a nonrigid object in which deformation dynamics are modeled by the finite element method. The deformation of a soft object is highly difficult to model because of its non-linearity, time-dependency, and material-response characteristics. Thus, the control implementation for Differential Drive Mobile Robots (DDMR) to deform an elastic object, is a challenge. The proposed steps to solve it are: Position-control designed over DDMR kinematics. Alignment-control applied for DDMRs orientation. The desired shape of the object is achieved using two contact points as the control nodes. A centralized vision algorithm was employed in each stage to obtain positions. To show the usefulness of the proposed scheme, numerical simulation, and real-time implementation were carried out.

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This paper presents the design and implementation of COVID-Bot, an open-source robotic platform for sanitizing single plant environments such as offices, houses, apartments, among others. This development seeks to create a tool that contributes to the global fight against the COVID-19 pandemic, from a low-cost and easy-to-replicate robot, which disinfects surfaces through type C ultraviolet radiation. The platform is based on a differential robotic base, an RGB-D camera, a tracking camera, three UV-C lamps, and an embedded computer running the ROS-based control software. In addition, this paper presents the description of the hardware used, the software implemented, and the tests carried out to corroborate the operation of the integrated system. These tests demonstrated that the system is adequate to autonomously cover a one-floor apartment, based on the theoretical radiation distance of the used lamps.

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This paper is concerned with path-tracking control of a wheeled mobile robot. This robot is equipped with two permanent magnet brushed DC-motors which are fed by two inverter-DC/DC Buck power converter systems as power amplifiers. By taking into account the dynamics of all the subsystems we present, for the first time, a formal stability proof for this control problem. Our control scheme is simple, in the sense that it is composed by four internal classical proportional-integral loops and one external classical proportional-derivative loop for path-tracking purposes. This is the third paper of a series of papers devoted to control different nonlinear systems, which proves that the proposed methodology is a rather general approach for controlling electromechanical systems when actuated by power electronic converters.

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In order to solve the trajectory tracking task in a wheeled mobile robot (WMR), a dynamic three-level controller is presented in this paper. The controller considers the mechanical structure, actuators, and power stage subsystems. Such a controller is designed as follows: At the high level is a dynamic control for the WMR (differential drive type). At the medium level is a PI current control for the actuators (DC motors). Lastly, at the low level is a differential flatness-based control for the power stage (DC/DC Buck power converters). The feasibility, robustness, and performance in closed-loop of the proposed controller are validated on a DDWMR prototype through Matlab-Simulink, the real-time interface ControlDesk, and a DS1104 board. The obtained results are experimentally assessed with a hierarchical tracking controller, recently reported in literature, that was also designed on the basis of the mechanical structure, actuators, and power stage subsystems. Although both controllers are robust when parametric disturbances are taken into account, the dynamic three-level tracking controller presented in this paper is better than the hierarchical tracking controller reported in literature.

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This work deals with the development of a nonlinear Periodic Event-Triggered Control strategy employed to the consensus of a multi-vehicle autonomous system based on (3,0) mobile robots. First, the existence of the Control Lyapunov Function (CLF) applicable to the consensus problem is proven. This is subsequently used to develop event and feedback functions. The Periodic Event-Triggered Control ensures trajectories boundedness and convergence to consensus while a specific sampling period is provided. Also, the formation problem is addressed as an extension of the presented work. Experimental results show the performance of the proposed control strategy which reduces 99.78% the number of control updates compared to a continuous control law, resulting in energy saving for the information transfer from central control to the mobile robots.