Abstract
Robotic applications, such as educational programs, are well-known. Nonetheless, there are challenges to be implemented in other settings, e.g., mine detection, agriculture support, and tasks for industry 4.0. The main challenge consists of robotic operations supported by autonomous decision using sensed-based features extraction. A prototype of a robot assembled using mechanical parts of a LEGO MINDSTORMS Robotic Kit EV3 and a Raspberry Pi controlled through servo algorithms of 2D and 2D1/2 vision approaches was implemented to tackle this challenge. This design is supported by simulations based on image, position, and a hybrid scheme for visual servo controllers. Practical implementation is operated using navigation guided by running up image-based visual servo control algorithms embedded in a Raspberry Pi that uses a control criterion based on error evolution to compute the difference between a target and sensed image. Images are collected by a camera installed on a mobile robotic platform manually and automatically operated and controlled using the Raspberry Pi. An Android application to watch the images by video streaming is shown here, using a smartphone and a video related to the implemented robot’s operation. This kind of robot might be used to complete field reactive tasks in the settings mentioned above, since the detection and control approaches allow self-contained guidance.
Highlights
IntroductionRobots are one of the most promising devices to be potentially used in industry, agriculture [1,2], medicine [3], education [4,5] and some other fields
Simulations for Image–Based Visual Servo Control (IBVS), PBVS, and Hybrid controllers are run up using these parameters and the codes reported in [32]: Xc = 0 cm; Yc = 2 cm; Xp = 5 cm; Yp = 5 cm
Thisrobotic roboticsystem systemwas wasdesigned designedby byrunning running up upcontrol controlIBVS, IBVS,PBVS, PBVS,and andhybrid hybridalgorithms, algorithms,applied appliedtotoobtain obtainthe theinverse inversekinematic kinematicofof the therobot
Summary
Robots are one of the most promising devices to be potentially used in industry, agriculture [1,2], medicine [3], education [4,5] and some other fields. Robots reach up to places inaccessible to humans, perceiving their surroundings, and collecting information to make decisions by themselves through decision support systems based on artificial intelligence techniques to adapt their movements depending on the requirements. Different configurations of integrated robots applying mobile or fixed cameras or sensors are reported in literature [3,6,7] These are well-known as visual servoing, which is used to obtain the necessary data of the surroundings from a camera through images locally or remotely collected, by using various kinds of controllers such as task function [8–10], predictive control [11,12], rational systems and LMIs (Linear Matrix Inequalities) [13], applying Kalman filters [6], etc. The robot’s devices should have an adequate processing capability to process the image and run up as quickly as possible the control algorithms
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