Development of a prototype robot for transportation within industrial environments

This paper describes the design and development of an autonomous robot for the Robot@Factory league at “Festival Nacional de Robótica 2016”, held in Bragança, Portugal. This paper describes all the hardware and software components developed for a localization and performance of the robot according to the rules. The challenge consists of a table setup that recreates an industrial environment where a robot has to successfully transport boxes from an initial warehouse to the final warehouse. The destination to which the robot has to carry each box, depends on the state of the box, i.e., depending on the box LED color, even though in some cases the robot has to leave the box temporarily in the called processing machines (which are intermediate stations). The most significant innovation feature of this robot prototype consists of the possibility of carrying up to three boxes simultaneously while being able to select which box to drop. This project was developed with great success, since the team managed to reach the 3rd place in the competition.(undefined)info:eu-repo/semantics/publishedVersio

Autonomous 4DOF robotic manipulator prototype for industrial environment and human cooperation

This paper describes the design and development of an autonomous robotic manipulator with four degrees of freedom. The manipulator is named RACHIE - "Robotic Arm for Collaboration with Humans in Industrial Environment". The idea was to create a smaller version of the industrial manipulators available on the market. The mechanical and electronic components are presented as well as the software algorithms implemented on the robot. The manipulator has as its primary goal the detection and organization of cans by color and defects. The robot can detect a human operator so it can deliver defective cans by collaborating with him/her on an industrial environment. To be able to perform such task, the robot has implemented a machine learning algorithm, a Haar feature-based cascade classifier, on its vision system to detect cans and humans. On the handler motion, direct and inverse kinematics were calculated and implemented, and its equations are described in this paper. This robot presents high reliability and robustness in the task assigned. It is low-cost as it is a small version of commercial ones, making it optimized for smaller tasks

Q-learning for autonomous mobile robot obstacle avoidance

An approach to the problem of autonomous mobile robot obstacle avoidance using Reinforcement Learning, more precisely Q-Learning, is presented in this paper. Reinforcement Learning in Robotics has been a challenging topic for the past few years. The ability to equip a robot with a powerful enough tool to allow an autonomous discovery of an optimal behavior through trial-and-error interactions with its environment has been a reason for numerous deep research projects. In this paper, two different Q-Learning approaches are presented as well as an extensive hyperparameter study. These algorithms were developed for a simplistically simulated Bot'n Roll ONE A (Fig. 1). The simulated robot communicates with the control script via ROS. The robot must surpass three levels of iterative complexity mazes similar to the ones presented on RoboParty [1] educational event challenge. For both algorithms, an extensive hyperparameter search was taken into account by testing hundreds of simulations with different parameters. Both Q-Learning solutions develop different strategies trying to solve the three labyrinths, enhancing its learning ability as well as discovering different approaches to certain situations, and finishing the task in complex environments