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.

This research addresses the obstacle avoidance problem in wheeled mobile robots powered by renewable energy by considering all subsystems involved. A three-tier hierarchical controller is developed, integrating the technique of artificial potential fields. The proposed controller incorporates the dynamics of the three key subsystems typically found in a wheeled mobile robot: the mechanical structure, actuators, and power electronics. At the highest tier, input-output linearization is combined with artificial potential fields. The medium tier employs two controllers based on differential flatness theory, while the lowest tier incorporates sliding mode control and proportional-integral control. The effectiveness of the control strategy is experimentally validated using a differential drive-type wheeled mobile robot prototype, leveraging the TDK-Lambda G100-17 as a renewable energy emulator, along with the DS1104 board and Matlab-Simulink software. Experiments were conducted under two scenarios: a) the emulation of a commercial photovoltaic panel to simulate realistic operating conditions and b) the application of time-varying input voltages to replicate dynamic power source variations. The experimental results demonstrate the robustness of the proposed controller against sudden changes in system parameters, confirming its reliability and effectiveness.

This paper presents a novel micro-segmented genetic algorithm (μsGA) to identify the best solution for the locomotion of a quadruped robot designed on a rectangular ABS plastic platform. We compare our algorithm with three similar algorithms found in the specialized literature: a standard genetic algorithm (GA), a micro-genetic algorithm (μGA), and a micro artificial immune system (μAIS). The quadruped robot prototype guarantees the same conditions for each test. The platform was developed using 3D printing for the structure and can accommodate the mechanisms, sensors, servomechanisms as actuators. It also has an internal battery and a multicore embedded system (mES) to process and control the robot locomotion. This research proposes a μsGA that segments the individual into specific bytes. μGA techniques are applied to each segment to reduce the processing time; the same benefits as the GA are obtained, while the use of a computer and the high computational resources characteristic of the GA are avoided. This is the reason why some research in robot locomotion is limited to simulation. The results show that the performance of μsGA is better than the three other algorithms (GA, μGA and AIS). The processing time was reduced using a mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. This research solves the problem of continuous locomotion of a quadruped robot, and gives a feasible solution with real performance parameters using a μsGA bio-micro algorithm and a mES architecture.

Overhead cranes are industrial machines that can cause user accidents due to deficiencies in inspecting their various components. One of the activities consists of verifying that the load hook does not exceed the limit deformation. However, the lack of historical records and negligence in visual inspection is a problem to ensure a correct maintenance service. Therefore, the objective of the research was to develop a vision system capable of inspecting, recording, and verifying the hook parameters. The method involved designing a vision module on an integral crane scanner system. A five-state deformation experiment of a cargo hook was modeled with ANSYS and fabricated in 3D printing to validate the proposed module by measuring the parameters manually with a commercial and proprietary vision system. The comparison between the two systems was satisfactory in detecting the latch and the correct position. Finally, the proprietary system improved the resolution up to 0.0435 mm on the hook deformation. With the systems contribution, it will be possible to know the valuable and safe life of the crane hook by automatic constant inspection.