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.
The global population is ageing at an unprecedented rate. With changes in life expectancy across the world, three major issues arise: an increasing proportion of senior citizens; cognitive and physical problems progressively affecting the elderly; and a growing number of single-person households. The available data proves the ever-increasing necessity for efficient elderly care solutions such as healthcare service and assistive robots. Additionally, such robotic solutions provide safe healthcare assistance in public health emergencies such as the SARS-CoV-2 virus (COVID-19). CHARMIE is an anthropomorphic collaborative healthcare and domestic assistant robot capable of performing generic service tasks in non-standardised healthcare and domestic environment settings. The combination of its hardware and software solutions demonstrates map building and self-localisation, safe navigation through dynamic obstacle detection and avoidance, different human-robot interaction systems, speech and hearing, pose/gesture estimation and household object manipulation. Moreover, CHARMIE performs end-to-end chores in nursing homes, domestic houses, and healthcare facilities. Some examples of these chores are to help users transport items, fall detection, tidying up rooms, user following, and set up a table. The robot can perform a wide range of chores, either independently or collaboratively. CHARMIE provides a generic robotic solution such that older people can live longer, more independent, and healthier lives.
The mountain bike is one of the most used equipment's in outdoor sports activities. The thesis describes the design and all development and implementation of Performance Assessment for Mountain Bike based on Wireless Sensor Network (WSN) and Cloud Technologies. The work presents a distributed sensing system for cycling assessment-providing data for objective evaluation of the athlete performance during training. Thus a wireless sensor network attached to the sport equipment provides to the athlete and the coach with performance values during practice. The sensors placed in biker equipment's behave as nodes of a WSN. This is possible with the developing of IoT-based systems in sports, the tracking and monitoring of athletes in their activities has an important role on his formation as bikers and helps to increase performance, through the analyze of each session. The implemented system performs acquisition, processing and transmission, of data using a ZigBee wireless networks that provide also machine-to-machine communication and data storage in a server located in the cloud. As in many cycling applications use the phone as a module to get the values, this work will be a little different making use of phone/tablet to consult information.The information stored on the cloud server is accessed through a mobile application that analyses and correlates all metrics calculated using the training data obtained during practice.Additional information regarding the health status may be also considered. Therefore, the system permits that athletes perform an unlimited number of trainings that can be accessed at any time through the mobile application by the bikers and coach. Based on capability of the system to save a history of the evolution of each athlete during training the system permits to perform appropriate comparisons between different training sessions and different athlete's performances.Keywords: machine-to-machine; bicycle; cloud; wireless sensor network; IoT ISCTE-IULPerformance Assessment for Mountain bike based on WSN and Cloud Technologies ii ResumoA bicicleta de montanha é um dos equipamentos para desportos no exterior mais usada. A tese descreve todo o desenho, desenvolvimento e implementação de Performance Assessment for Mountain Bike based on WSN and Cloud Technologies. Este apresenta um sistema de deteção distribuída para o aumento do desempenho, melhorar a metodologia da prática do ciclismo e para formação de atletas. Para tal foi desenvolvida e anexada uma rede de sensores que está embutida no equipamento do ciclista, através desta rede de sensores sem fios são obtidos os valores respetivos à interação do utilizador e a sua bicicleta, sendo estes apresentados ao treinador e ao próprio ciclista. Os sensores colocados comportam-se como nós de uma rede de sensores sem fios. Isso é possível com o desenvolvimento de sistemas baseados na Internet das coisas no desporto, a observação da movimentação e monitoramento de atletas nas suas atividades tem um papel importante na sua formação como ciclistas e ...
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.
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