Review of Intelligent Control Systems with Robotics

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domai

Human inspired humanoid robots control architecture

This PhD Thesis tries to present a different point of view when talking about the development of control architectures for humanoid robots. Specifically, this Thesis is focused on studying the human postural control system as well as on the use of this knowledge to develop a novel architecture for postural control in humanoid robots. The research carried on in this thesis shows that there are two types of components for postural control: a reactive one, and other predictive or anticipatory. This work has focused on the development of the second component through the implementation of a predictive system complementing the reactive one. The anticipative control system has been analysed in the human case and it has been extrapolated to the architecture for controlling the humanoid robot TEO. In this way, its different components have been developed based on how humans work without forgetting the tasks it has been designed for. This control system is based on the composition of sensorial perceptions, the evaluation of stimulus through the use of the psychophysics theory of the surprise, and the creation of events that can be used for activating some reaction strategies (synergies) The control system developed in this Thesis, as well as the human being does, processes information coming from different sensorial sources. It also composes the named perceptions, which depend on the type of task the postural control acts over. The value of those perceptions is obtained using bio-inspired evaluation techniques of sensorial inference. Once the sensorial input has been obtained, it is necessary to process it in order to foresee possible disturbances that may provoke an incorrect performance of a task. The system developed in this Thesis evaluates the sensorial information, previously transformed into perceptions, through the use of the “Surprise Theory”, and it generates some events called “surprises” used for predicting the evolution of a task. Finally, the anticipative system for postural control can compose, if necessary, the proper reactions through the use of predefined movement patterns called synergies. Those reactions can complement or substitute completely the normal performance of a task. The performance of the anticipative system for postural control as well as the performance of each one of its components have been tested through simulations and the application of the results in the humanoid robot TEO from the RoboticsLab research group in the Systems Engineering and Automation Department from the Carlos III University of Madrid. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Esta Tesis Doctoral pretende aportar un punto de vista diferente en el desarrollo de arquitecturas de control para robots humanoides. En concreto, esta Tesis se centra en el estudio del sistema de control postural humano y en la aplicación de este conocimiento en el desarrollo de una nueva arquitectura de control postural para robots humanoides. El estudio realizado en esta Tesis pone de manifiesto la existencia de una componente de control postural reactiva y otra predictiva o anticipativa. Este trabajo se ha centrado en el desarrollo de la segunda componente mediante la implementación de un sistema predictivo que complemente al sistema reactivo. El sistema de control anticipativo ha sido estudiado en el caso humano y extrapolado para la arquitectura de control del robot humanoide TEO. De este modo, sus diferentes componentes han sido desarrollados inspirándose en el funcionamiento humano y considerando las tareas para las que dicho robot ha sido concebido. Dicho sistema está basado en la composición de percepciones sensoriales, la evaluación de los estímulos mediante el uso de la teoría psicofísica de la sorpresa y la generación de eventos que sirvan para activar estrategias de reacción (sinergias). El sistema de control desarrollado en esta Tesis, al igual que el ser humano, procesa información de múltiples fuentes sensoriales y compone las denominadas percepciones, que dependen del tipo de tarea sobre la que actúa el control postural. El valor de estas percepciones es obtenido utilizando técnicas de evaluación bioinspiradas de inferencia sensorial. Una vez la entrada sensorial ha sido obtenida, es necesario procesarla para prever posibles perturbaciones que puedan ocasionar una incorrecta realización de una tarea. El sistema desarrollado en esta Tesis evalúa la información sensorial, previamente transformada en percepciones, mediante la ‘Teoría de la Sorpresa’ y genera eventos llamados ‘sorpresas’ que sirven para predecir la evolución de una tarea. Por último, el sistema anticipativo de control postural puede componer, si fuese necesario, las reacciones adecuadas mediante el uso de patrones de movimientos predefinidos llamados sinergias. Dichas reacciones pueden complementar o sustituir por completo la ejecución normal de una tarea. El funcionamiento del sistema anticipativo de control postural y de cada uno de sus componentes ha sido probado tanto por medio de simulaciones como por su aplicación en el robot humanoide TEO del grupo de investigación RoboticsLab en el Departamento de Ingeniería de Sistemas y Automática de la Universidad Carlos III de Madrid

Fuzzy-Genetic Control of Quadrotors Unmanned Aerial Vehicles

This article presents a novel fuzzy identification method for dynamic modelling of quadrotor unmanned aerial vehicles. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remain intact throughout an unconstrained hybrid evolutionary and gradient descent based optimization process. In the equations of motion the first order derivative component is calculated based on Christoffel symbols, the derivatives of fuzzy systems are used for modelling the Coriolis effects, gyroscopic and centrifugal terms. The non-linear parameters are subjected to an initial global evolutionary optimization scheme and fine tuning with gradient descent based local search. Simulation results of the proposed new quadrotor dynamic model identification method are promising

Analysis and control of FES-assisted paraplegic walking with wheel walker.

The number of people with spinal cord injury (SCI) is increasing every year and walking has been found to be the most exciting and important prospect to these patients to improve their quality of life. Many individuals with incomplete SCI have the potential to walk and everyone of them wants to try. Unfortunately up to now, there is less than one third of patients could walk again after SCI. Residual function, the orthotic support, energy expenditure, patient motivation and control technique are some of the factors that influence the walking outcome of spinal cord injured people. In this thesis, a series of studies are carried out to investigate the possibility of enhancing the performance of the functional electrical stimulation (PES) assisted paraplegic walking with wheel walker through the development and implementation of intelligent control technique and spring brake orthosis (SBO) with full utilization of the voluntary upper body effort. The main aim of this thesis is to enable individuals with complete paraplegia to walk again with maximum performance and the simplest approach as possible. Firstly, before simulation of the system can be made, it is important to select the right model to represent the actual plant. In this thesis, the development of a humanoid and wheel walker models are carried out using MSC.visualNastran4D (vN4D) software and this is integrated with Matlab Simulink® for simulation. The newly developed quadriceps and hamstrings muscle models from the series of experiments are used to represent subject muscles after comparison and validation with other two well-known muscle models are performed. Several experiments are conducted to investigate the effect of stimulation frequency and pulse-width in intermittent stimulation with isometric measurement from paraplegic subjects. The results from this work can serve as a guidance to determine the optimum stimulation parameters such as frequency and pulse-width to reduce muscle fatigue during PES application. The ability test is introduced to determine the maximum leg force that can be applied to the specific paraplegic subject during FES functional task with minimum chance of spasm and leg injury. Investigations are carried out on the control techniques implemented for FES walking with wheel walker. PID control and fuzzy logic control (FLC) are used to regulate the electrical stimulation required by the quadriceps and hamstrings muscles in order to perform the FES walking manoeuvre according to predefined walking trajectory. The body weight transfer is introduced to increase the efficiency of FES walking performance. The effectiveness of body weight transfer and control strategy to enhance the performance of FES walking and reduce stimulation pulses required is examined. Investigations are carried out on the effectiveness of spring brake orthosis (SBO) for FES assisted paraplegic walking with wheel walker. A new concept in hybrid orthotics provides solutions to the problems that affect current 'hybrid orthosis, including knee and hip flexion without relying on the withdrawal reflex or a powered actuator and foot-ground clearance without extra upper body effort. The use of SBO can also eliminate electrical stimulation pulses required by the hamstrings muscle for the same FES walking system. Further improvement of the FES walking system is achieved by introducing finite state control (FSC) to control the switching time between springs, brakes and electrical stimulation during FES assisted walking with wheel walker with the combInation of FLC to regulate the electrical stimulation required for the knee extension. The results show that FSC can be used to accurately control the switching time and improve the system robustness and stability

A Survey on Obstacles Avoidance Mobile Robot in Static Unknown Environment

Autonomous mobile robots have in recent times gained interest from many researchers. This is due to wide range of mobile robot application. Numerous robots especially in navigation, obstacle avoidance and path following are currently under development. A reliable collision avoidance methodology is needed for effective navigation. Normally robots are fitted with transducers such as ultrasonic sensors, infrared and cameras for detecting environment. Various methods have been established in the past years to resolve navigational problems associated with mobile robots. They include fuzzy logic, potential fields, genetic algorithm, neural network and vision base approaches. Fuzzy logic demonstrates to be an appropriate tool for handling uncertainty that emerge from imprecise knowledge during route finding

Proceedings of 3. International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4’2020)

Çevrimiçi ( XIV, 67 pages