Development of a robotic hand using bioinspired optimization for mechanical and control design : UnB-hand

For the last four decades, the development of robotic hands has been the focus of several works. However, a small part of those approaches consider the exploitation of parallelism of FPGA-based (Field Programmable Gate Arrays) systems or discuss how using bioinspired optimization algorithms could improve the mechanical and controller components. This work considers developing a bioinspired robotic hand that achieves motion and force control with a logic hardware architecture implemented in FPGA intended to be replicated and executed with suitable parallelism, fitting a single device. The developed robotic hand prototype has five fingers and seven DoF (Degrees of Freedom). Using bioinspired optimization, such as PSO (Particle Swarm Optimization), both the rigid finger mechanism and the impedance controller were optimized and incorporated the results in several practical grasping experiments. The validation of this work is done with the Cutkosky grasping taxonomy and some grasping experiments with interference. The tests proved the proficiency of this works for a wide range of power and some precision grasp. The reader can see the experiments in the attached videos

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Swarm intelligence optimization based n parallel architectures for embedded applications

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2012.Este trabalho apresenta um estudo da implementação em FPGAs (Field Programma- ble Gate Array) de algoritmos de otimização bioinspirados baseados em inteligência de enxames, voltados principalmente para aplicações embarcadas. Nos problemas de otimização embarcada, a dimensionalidade (número de variáveis de decisão) é relativa- mente pequena (algumas dezenas), por em, os problemas devem ser resolvidos em uma escala de tempo desde os milissegundos até alguns segundos. A abordagem utilizada está baseada em uma representação aritmética de ponto utuante e no uso de operações de fácil implementação em FPGAs, permitindo explorar o paralelismo intrínseco dos algoritmos por inteligência de enxames, visando obter ganhos de desempenho em termos do tempo de execução e da qualidade da solução. Foram exploradas as arquiteturas de hardware dos algoritmos PSO (Particle Swarm Optimization), ABC (Arti cial Bee Colony), FA (Fire y Algorithm) e SFLA (Shu ed Frog Leaping Algorithm), assim como de algumas variantes propostas para os mesmos. Estudos de consumo de recursos para diferente número de partículas paralelas e dimensionalidade dos problemas foram realizados no intuito veri car a aplicabilidade dos algoritmos em arquiteturas reconguráveis. Adicionalmente, a qualidade das soluções obtidas pelas arquiteturas propostas foi validada usando problemas de teste tipo benchmark. Os algoritmos estudados foram implementados no processador de software embarcado MicroBlaze e em um PC de escritório, permitindo, assim, realizar comparações do tempo de execução entre as implementações de hardware e software. Finalmente, uma solucão de hardware foi proposta para a solução de um problema de otimização embarcada, onde é realizado o treinamento online de um controlador neural de um robô móvel de pequeno porte. Os resultados experimentais mostram que a implementação em FPGAs dos algoritmos por intelig^encia de enxames é viável em termos de consumo de recursos de hardware. Foram obtidos fatores de acelera ca~o de tr^es ordens de magnitude em comparação com a implementação software no MicroBlaze e de 3.6 vezes em comparação com a solução no PC de escritório. ______________________________________________________________________________ ABSTRACTThis work presents a study of the FPGA (Field Programmable Gate Array) implementation of swarm intelligence optimization algorithms, applied to embedded optimization systems. In embedded optimization problems the dimensionality (problem size) is smaller than in conventional ones; however, the problems must be solved at millisecond/second time-scales. The approach presented in this work is based on the oating-point arithmetic repre sentation as well as on operations that can be easily implemented on FPGAs, allowing the intrinsic parallelism of the swarm intelligence based algorithms to be explored in order to improve the execution time and the quality of the solutions. Hardware architectures of the PSO (Particle Swarm Optimization), ABC (Arti cial Bee Colony), FA (Fire y Algorithm) and SFLA (Shu ed Frog Leaping Algorithm) algorithms, as well as some proposed modi cations, were mapped on FPGAs. The cost in logic area of the proposed architectures was estimated for di erent swarm sizes and problem sizes in order to validate the applicability of the algorithms for recon gurable architectures. In addition, the quality of the solutions obtained by the proposed architectures was validated using two unimodal and two multimodal bechmarks test problems. The algorithms were also implemented on two software processors, the MicroBlaze embedded processor and a conventional Desktop solution, allowing for comparisons of the execution time between the hardware and software implementations. Finally, a hardware solution was proposed for solving the online training process of a neural network controller of a small mobile robot. The experimental results demonstrate that the FPGA implementation of the swarm intelligence algorithms is feasible in terms of the hardware resources consumption. Speed-up factors of three orders of magnitude and 3.6 times were achieved in compa- rison with the MicroBlaze and the Desktop solutions, respectively

Implementação e simulação de algoritmos de escalonamento para sistemas de elevadores usando arquiteturas reconfiguráveis

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2006.Este trabalho propõe um sistema de elevadores que permite o transporte vertical de passageiros de uma forma eficiente. A abordagem é baseada na implementação de algoritmos de escalonamento usando arquiteturas reconfiguráveis. Um método baseado em lógica nebulosa foi proposto no intuito de identificar padrões de tráfego no edifício e despachar os elevadores, adotando diferentes estratégias de atendimento de chamadas. Os sistemas de elevadores modernos para transporte vertical de passageiros são freqüentemente implementados por controladores microprocessados, no intuito de executar as tarefas de controle e ação. O estudo de estratégias para controle de elevadores tenta otimizar o desempenho do sistema, incrementando o fluxo de transporte e o conforto dos usuários. Ao mesmo tempo, o consumo de potência do sistema deve ser diminuído. A arquitetura proposta para o Sistema de Controle Local (LCS) considera o uso de cinco algoritmos de escalonamento, os quais foram implementados em placas de desenvolvimento FPGA (Field Programmable Gate Array) do tipo Spartan3 numa abordagem integrada, reduzindo o consumo de área e otimizando o desempenho do circuito. O Sistema de Controle de Grupo de Elevadores (EGCS), baseado em lógica nebulosa (FEGCS) foi desenvolvido em linguagem Java. Este sistema permite validar o desempenho dos algoritmos para diferentes situações de tráfego. Os resultados de simulação mostram que o tempo de espera é reduzido sempre que o consumo de potência é incrementado. O tempo de espera médio dos passageiros é aproximadamente de 36 segundos em um padrão de tráfego de descida. O número de cálculos no controlador de grupo é reduzido, dado que o EGCS não esta diretamente envolvido em calcular o próximo andar a ser visitado. A implementação em hardware dos algoritmos de escalonamento permite melhorar o desempenho do cálculo do próximo andar a ser visitado por cada elevador. _______________________________________________________________________________ ABSTRACTThis work proposes an elevator system that allows the vertical transport of passengers in a efficient way. This approach is based on the implementation of dispatching algorithms using Reconfigurable Architectures. A fuzzy logic method was proposed in order to identify traffic patterns in the building and schedule the elevators, which was carried out by implementing several strategies for attending the hall-calls. Modern elevator systems for vertical transport of the passengers are frequently implemented by several microprocessed controllers in order to achieve the control and several action tasks. The study of elevator control strategies tries to improve the performance of the system, incrementing the transport flow and the comfort of the users. At the same time, the power consumption of the overall system must be reduced. The proposed Local Control System (LCS) architecture considers the use of five dispatching algorithms for elevator systems, which were implemented on Spartan 3 FPGA (Field Programmable Gate Array) based boards in a integrated approach, reducing the area consumption of the overall circuit and improving its performance. The Elevator Group Control System (EGCS) based on fuzzy logic (FEGCS) was developed on Java language. This system allows to validate the algorithms performance for different traffic situations. Simulation results show that the waiting time is reduced whenever the power consumption is incremented. The average waiting time of the passengers is about 36 seconds in a down traffic pattern. The number of calculations in the group controller is reduced given that the EGCS is not directly involved in calculating the next floors to be visited. The hardware implementation of the dispatching algorithms allows to improve the performance calculation of the next floor to be visited by each elevator