Efficient PID Controller based Hexapod Wall Following Robot

This paper presents a design of wall followingbehaviour for hexapod robot based on PID controller. PIDcontroller is proposed here because of its ability to controlmany cases of non-linear systems. In this case, we proposed aPID controller to improve the speed and stability of hexapodrobot movement while following the wall. In this paper, PIDcontroller is used to control the robot legs, by adjusting thevalue of swing angle during forward or backward movement tomaintain the distance between the robot and the wall. Theexperimental result was verified by implementing the proposedcontrol method into actual prototype of hexapod robot

Efficient PID Controller based Hexapod Wall Following Robot

This paper presents a design of wall following behaviour for hexapod robot based on PID controller. PID controller is proposed here because of its ability to control many cases of non-linear systems. In this case, we proposed a PID controller to improve the speed and stability of hexapod robot movement while following the wall. In this paper, PID controller is used to control the robot legs, by adjusting the value of swing angle during forward or backward movement to maintain the distance between the robot and the wall. The experimental result was verified by implementing the proposed control method into actual prototype of hexapod robot

Automated design of complex dynamic systems

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems

Bridging Vision and Dynamic Legged Locomotion

Legged robots have demonstrated remarkable advances regarding robustness and versatility in the past decades. The questions that need to be addressed in this field are increasingly focusing on reasoning about the environment and autonomy rather than locomotion only. To answer some of these questions visual information is essential. If a robot has information about the terrain it can plan and take preventive actions against potential risks. However, building a model of the terrain is often computationally costly, mainly because of the dense nature of visual data. On top of the mapping problem, robots need feasible body trajectories and contact sequences to traverse the terrain safely, which may also require heavy computations. This computational cost has limited the use of visual feedback to contexts that guarantee (quasi-) static stability, or resort to planning schemes where contact sequences and body trajectories are computed before starting to execute motions. In this thesis we propose a set of algorithms that reduces the gap between visual processing and dynamic locomotion. We use machine learning to speed up visual data processing and model predictive control to achieve locomotion robustness. In particular, we devise a novel foothold adaptation strategy that uses a map of the terrain built from on-board vision sensors. This map is sent to a foothold classifier based on a convolutional neural network that allows the robot to adjust the landing position of the feet in a fast and continuous fashion. We then use the convolutional neural network-based classifier to provide safe future contact sequences to a model predictive controller that optimizes target ground reaction forces in order to track a desired center of mass trajectory. We perform simulations and experiments on the hydraulic quadruped robots HyQ and HyQReal. For all experiments the contact sequences, the foothold adaptations, the control inputs and the map are computed and processed entirely on-board. The various tests show that the robot is able to leverage the visual terrain information to handle complex scenarios in a safe, robust and reliable manner

Conducting polymer actuators: from basic concepts to proprioceptive systems

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Es apoyado por 12 documentos, de los 27 en coautoría del candidato y publicados en diferentes revistas durante el desarrollo de esta tesis. Todos los documentos están indexados en el Journal Citation Reports (ISI-JCR). Los elementos seleccionados alcanzan 45 puntos de acuerdo con los reglamentos de la UPCT (Artículo 33 del Reglamento de estudios oficiales de máster y doctorado de la Universidad Politécnica de Cartagena, aprobado por el Consejo de Gobierno el 13 de abril de 2011 y modificado en Consejo de Gobierno el 11 de julio de 2012.), requiriendo un mínimo de 12 puntos para permitir la presentación de la tesis a través de un conjunto de publicaciones. Siguiendo esas regulaciones, esta tesis incluye: los objetivos de la tesis, el estado del arte, un resumen extendido para cada artículo (incluido el procedimiento experimental y los principales logros), una copia de cada trabajo seleccionado y las conclusiones generales.[ENG] This thesis is presented through a set of publications. Designers and engineers have been dreaming for decades of motors sensing, by themselves, working and surrounding conditions, as biological muscles do originating proprioception. Here bilayer full polymeric artificial muscles were checked up to very high cathodic potential limits (-2.5 V) in aqueous solution by cyclic voltammetry. The electrochemical driven exchange of ions from the conducting polymer film, and the concomitant Faradaic bending movement of the muscle, takes place in the full studied potential range. The presence of trapped counterion after deep reduction was corroborated by EDX determinations giving quite high electronic conductivity to the device. The large bending movement was used as a tool to quantify the amount of water exchanged per reaction unit (exchanged electron or ion). The potential evolutions of self-supported films of conducting polymers or conducting polymers (polypyrrole, polyaniline) coating different microfibers, during its oxidation/reduction senses working mechanical, thermal, chemical or electrical variables. The evolution of the muscle potential from electrochemical artificial muscles based on electroactive materials such as intrinsically conducting polymers and driven by constant currents senses, while working, any variation of the mechanical (trailed mass, obstacles, pressure, strain or stress), thermal or chemical conditions of work. One physically uniform artificial muscle includes one electrochemical motor and several sensors working simultaneously under the same driving reaction. Actuating (current and charge) and sensing (potential and energy) magnitudes are present, simultaneously, in the only two connecting wires and can be read by the computer at any time. From basic polymeric, mechanical and electrochemical principles a physicochemical equation describing artificial proprioception has been developed. It includes and describes, simultaneously, the evolution of the muscle potential during actuation as a function of the motor characteristics (rate and sense of the movement, relative position, and required energy) and the working variables (temperature, electrolyte concentration, mechanical conditions and driving current). By changing working conditions experimental results overlap theoretical predictions. The ensemble computer-generator-muscle theoretical equation constitutes and describes artificial mechanical, thermal and chemical proprioception of the system. Proprioceptive tools and most intelligent zoomorphic or anthropomorphic soft robots can be envisaged.Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Está formada por un total de doce artículos: 1. Toribio F. Otero, Jose G. Martinez and Joaquin Arias-Pardilla. Biomimetic electrochemistry from conducting polymers. A review. Artificial muscles, smart membranes, smart drug delivery and computer/ neuron interfaces Electrochimica Acta, year 2012, volume 84, pages 112-128. (ISI-JCR IF: 4.086, Q1 in Electrochemistry). 2. Toribio F. Otero and Jose G. Martinez. Artificial Muscles: A Tool To Quantify Exchanged Solvent During Biomimetic Reactions Chemistry of Materials, year 2012, volume 24, pages 4093-4099. (IF=8.535, Q1 in ‘Materials science, Multidisciplinary’ and ‘Chemistry, Physical’). 3. Toribio F. Otero and Jose G. Martinez. Ionic exchanges, structural movements and driven reactions in conducting polymers from bending artificial muscles Sensors and Actuators B: Chemical, year 2014, volume 199, pages 27-30. (IF=3.840, Q1 in ‘Instruments & instrumentation’, ‘Chemistry, analytical’ and ‘Electrochemistry’). 4. Toribio F. Otero and Jose G. Martinez. Structural Electrochemistry: Conductivities and Ionic Content from Rising Reduced Polypyrrole Films Advanced Functional Materials, year 2014, volume 24, pages 1259-1264. (IF=10.439, Q1 in ‘Materials science, multidisciplinary’, ‘Nanoscience & nanotechnology’, ‘Physics, applied’, ‘Chemistry, multidisciplinary’, ‘Chemistry, physical’ and ‘Physics, condensed matter’). 5. Jose G. Martinez, Toribio F. Otero and Edwin W. H. Jager. Effect of the Electrolyte Concentration and Substrate on Conducting Polymer Actuators Langmuir, year 2014, volume 30, pages 3894-3904. (IF=4.384, Q1 in ‘Materials science, multidisciplinary’, ‘Chemistry, multidisciplinary’ and ‘Chemistry, physical’). 6. Toribio F. Otero, Juan J. Sanchez and Jose G. Martinez. Biomimetic Dual Sensing-Actuators Based on Conducting Polymers. Galvanostatic Theoretical Model for Actuators Sensing Temperature The Journal of Physical Chemistry B, year 2012, volume 116, pages 5279-5290. (IF=3.377, Q2 in ‘Chemistry, physical’). 7. Jose G. Martinez and Toribio F. Otero. Biomimetic Dual Sensing-Actuators: Theoretical Description. Sensing Electrolyte Concentration and Driving Current The Journal of Physical Chemistry B, year 2012, volume 116, pages 9223-9230. (IF=3.377, Q2 in ‘Chemistry, physical’). 8. Jose G. Martinez and Toribio F. Otero. Mechanical awareness from sensing artificial muscles: Experiments and modeling Sensors and Actuators B: Chemical, year 2014, volume 195, pages 365-372. (IF=3.840, Q1 in ‘Instruments & instrumentation’, ‘Chemistry, analytical’, and ‘Electrochemistry’). 9. Jose G. Martinez and Toribio F. Otero. Structural Electrochemistry. Chronopotentiometric Responses From Rising Compacted Polypyrrole Electrodes: Experiments and Model RSC Advances, year 2014, volume 4, pages 29139-29145. (IF=3.708, Q1 in ‘Chemistry, multidisciplinary’). 10. Toribio F. Otero and Jose G. Martinez. Physical and chemical awareness from sensing polymeric artificial muscles. Experiments and modeling Progress in Polymer Science, year 2014, DOI: 10.1016/ j.progpolymsci.2014.09.002. (IF=26.854, Q1 in ‘Polymer science’). 11. Yahya A. Ismail, Jose G. Martinez and Toribio F. Otero. Polyurethane microfibrous mat template polypyrrole: Preparation and biomimetic reactive sensing capabilities Journal of Electroanalytical Chemistry, year 2014, volume 719, pages 47-53. (IF=2.871, Q2 in ‘Chemistry, analytical’ and ‘Electrochemistry’). 12. Yahya A. Ismail, Jose G. Martinez and Toribio F. Otero. Fibroin/Polyaniline microfibrous mat. Preparation and electrochemical characterization as reactive sensor Electrochimica Acta, year 2014, volume 123, pages 501-510. (IF: 4.086, Q1 in ‘Electrochemistry’).Universidad Politécnica de CartagenaPrograma de doctorado de Electroquímica. Ciencia y Tecnologí

NOCH: A framework for biologically plausible models of neural motor control

This thesis examines the neurobiological components of the motor control system and relates it to current control theory in order to develop a novel framework for models of motor control in the brain. The presented framework is called the Neural Optimal Control Hierarchy (NOCH). A method of accounting for low level system dynamics with a Linear Bellman Controller (LBC) on top of a hierarchy is presented, as well as a dynamic scaling technique for LBCs that drastically reduces the computational power and storage requirements of the system. These contributions to LBC theory allow for low cost, high-precision control of movements in large environments without exceeding the biological constraints of the motor control system

Pattern Generation for Rough Terrain Locomotion with Quadrupedal Robots:Morphed Oscillators & Sensory Feedback

Animals are able to locomote on rough terrain without any apparent difficulty, but this does not mean that the locomotor system is simple. The locomotor system is actually a complex multi-input multi-output closed-loop control system. This thesis is dedicated to the design of controllers for rough terrain locomotion, for animal-like quadrupedal robots. We choose the problem of blind rough terrain locomotion as the target of experiments. Blind rough terrain locomotion requires continuous and momentary corrections of leg movements and body posture, and provides a proper testbed to observe the interaction of different mod- ules involved in locomotion control. As for the specific case of this thesis, we have to design rough terrain locomotion controllers that do not depend on the torque-control capability, have limited sensing, and have to be computationally light, all due to the properties of the robotics platform that we use. We propose that a robust locomotion controller, taking into account the aforementioned constraints, is constructed from at least three modules: 1) pattern generators providing the nominal patterns of locomotion; 2) A posture controller continuously adjusting the attitude of the body and keeping the robot upright; and 3) quick reflexes to react to unwanted momentary events like stumbling or an external force impulse. We introduce the framework of morphed oscillators to systematize the design of pattern gen- erators realized as coupled nonlinear oscillators. Morphed oscillators are nonlinear oscillators that can encode arbitrary limit cycle shapes and simultaneously have infinitely large basins of attraction. More importantly, they provide dynamical systems that can assume the role of feedforward locomotion controllers known as Central Pattern Generators (CPGs), and accept discontinuous sensory feedback without the risk of producing discontinuous output. On top of the CPG module, we add a kinematic model-based posture controller inspired by virtual model control (VMC), to control the body attitude. Virtual model control produces forces, and through the application of the Jacobian transpose method, generates torques which are added to the CPG torques. However, because our robots do not have a torque- control capability, we adapt the posture controller by producing task-space velocities instead of forces, thus generating joint-space velocity feedback signals. Since the CPG model used for locomotion generates joint velocities and accepts feedback without the fear of instability or discontinuity, the posture control feedback is easily integrated into the CPG dynamics. More- over, we introduce feedback signals for adjusting the posture by shifting the trunk positions, which directly update the limit cycle shape of the morphed oscillator nodes of the CPG. Reflexes are added, with minimal complexity, to react to momentary events. We implement simple impulse-based feedback mechanisms inspired by animals and successful rough terrain robots to 1) flex the leg if the robot is stumbling (stumbling correction reflex); 2) extend the leg if an expected contact is missing (leg extension reflex); or 3) initiate a lateral stepping sequence in response to a lateral external perturbation. CPG, posture controller, and reflexes are put together in a modular control architecture alongside additional modules that estimate inclination, control speed and direction, maintain timing of feedback signals, etc. [...