Humanoid Robot Cooperative Motion Control Based on Optimal Parameterization

The implementation of low-energy cooperative movements is one of the key technologies for the complex control of the movements of humanoid robots. A control method based on optimal parameters is adopted to optimize the energy consumption of the cooperative movements of two humanoid robots. A dynamic model that satisfies the cooperative movements is established, and the motion trajectory of two humanoid robots in the process of cooperative manipulation of objects is planned. By adopting the control method with optimal parameters, the parameters optimization of the energy consumption index function is performed and the stability judgment index of the robot in the movement process is satisfied. Finally, the effectiveness of the method is verified by simulations and experimentations

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA

Über den Einfluss der Fußgeometrie auf die Energieeffizienz beim zweibeinigen Gehen

Der Einfluss der Fußgeometrie auf die Energieeffizienz beim zweibeinigen Gehen wird untersucht. Es wird eine Methode zur Optimierung der Fußgeometrie für einen zweibeinigen Roboter entwickelt. Grundlage ist ein ebenes Modell mit beliebieger, konvexer Fußgeometrie in Kombination mit einer Regelung auf Basis der hybriden Nulldynamik. Es werden optimale Bewegungen und Fußgeometrien ermittelt. Im Vergleich zu einem Modell mit Punktfüßen ergeben sich Energieeinsparungen von über 80%

Über den Einfluss der Fußgeometrie auf die Energieeffizienz beim zweibeinigen Gehen

Die Energieeffizienz beim Gehen ist ein wichtiger Aspekt bei der Entwicklung zweibeiniger Roboter. Diese verfügen nur über einen begrenzten Energiespeicher, mit dem ein möglichst langer Betrieb angestrebt wird. Die Energieeffizienz wird einerseits von der konstruktiven Gestaltung und den Modellparametern beeinflusst, andererseits jedoch auch von der verwendeten Regelung, mit der die Bewegung erzeugt und stabilisiert wird. In einem Entwicklungsprozess werden daher bei der Konzeption und der konstruktiven Gestaltung bereits früh Modelle zur Simulation und Methoden zur Optimierung benötigt. Da in diesem Entwicklungsstadium erst wenige Details konkretisiert und festgelegt sind, eignen sich einfache Mehrkörpermodelle für diese Fragestellung. Durch eine Regelung auf Basis der hybriden Nulldynamik können für solche Systeme stabile Gehbewegungen mit hoher Energieeffizienz erzeugt werden, die die natürliche Dynamik des Systems ausnutzen. In dieser Arbeit wird untersucht, welchen Einfluss die Fußgeometrie auf die Energieeffizienz beim zweibeinigen Gehen hat und wie diese bei der Entwicklung eines zweibeinigen Roboters optimiert werden kann. Hierfür wird ein Modell für einen konvexen, starren Fuß entwickelt, dessen Kontaktpunkt mit dem Boden explizit berechnet werden kann. Dadurch ist eine Beschreibung der Abrollbewegung in Minimalkoordinaten möglich und für die Dynamik des Gesamtsystems kann eine gewöhnliche Differentialgleichung abgeleitet werden. Für das Fußmodell werden zwei Parametrierungen entwickelt, bei denen jeweils von einem Polygon ausgegangen wird, dessen Kanten abgerundet werden, damit sich eine kontinuierliche Abrollbewegung ergibt. Auf diese Weise wird ein flacher Fuß, und ein Fuß mit zusätzlichem Zehenbereich beschrieben. Der Roboter wird durch ein ebenes Mehrkörpersystem beschrieben, das aus einem Oberkörper, Oberschenkeln, Unterschenkeln und dem konvexen Fuß besteht, die jeweils durch Drehgelenke in Hüfte, Knie und Sprunggelenk miteinander verbunden sind. Für dieses System wird eine Regelung auf Basis der hybriden Nulldynamik entworfen. Dieses Regelungskonzept wird somit auf Systeme mit beliebiger Fußgeometrie erweitert. Mittels numerischer Optimierung werden optimale Gehbewegungen erzeugt und zugleich die Fußgeometrie optimiert. Zur Durchführung von Parameterstudien wird eine numerische Fortsetzungsmethode für dieses nichtglatte Problem entwickelt. Durch die Optimierung der Fußgeometrie kann der durchschnittliche Energieverbrauch eines 80 kg schweren und 1,80 m großen Roboters im Geschwindigkeitsbereich 0,3 bis 2,3 m/s gegenüber einem Modell mit Punktfüßen um 81 % reduziert werden

Optimality, Objectives, and Trade-Offs in Motor Control under Uncertainty

Biological motor control involves multiple objectives and constraints. In this thesis, I investigated the influence of uncertainty on biological sensorimotor control and decision-making, considering various objectives. In the first study, I used a simple biped walking model simulation to study the control of a rhythmic movement under uncertainty. Uncertainty necessitates a more sophisticated form of motor control involving internal model and sensing, and their effective integration. The optimality of the neural pattern generator incorporating sensory information was shown to be dependent on the relative amount of physical disturbance and sensor noise. When the controller was optimized for state estimation, other objectives of improved energy efficiency, reduced variability, and reduced number of falls were also satisfied. In the second study, human participants performed regression and classification tasks on visually presented scatterplot data. The tasks involved a trade-off between acting on small but prevalent errors and acting on big but scarce errors. We used inverse optimization to characterize the loss function used by humans in these regression and classification tasks, and found that these loss functions change systematically as the data sparsity changed. Despite being highly variable, there were overall shifts towards compensating for prevalent small errors more when the sparsity of the visual data decreased. In the third study, I extended the pattern recognition tasks to include visually mediated force tracking. When participants tracked force targets with visual noise, we observed a slight yet consistent force tracking bias. This bias, which increased with noise, was not explained by commonly hypothesized objectives such as a tendency to reduce effort while regulating error. Additional experiments revealed that a model balancing error reduction and transition reduction tendencies effectively explained and predicted experimental data. Transition reduction tendency was further separated into recency bias and central tendency bias. Notably, this bias disappeared when the task became purely visual, suggesting that such biases could be task-dependent. These findings across the three studies provide useful insights into understanding how uncertainty changes objectives and their trade-offs in biological motor control, and in turn, results in a different control strategy and behaviors

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Cognitive-developmental learning for a humanoid robot : a caregiver's gift

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 319-341).(cont.) which are then applied to developmentally acquire new object representations. The humanoid robot therefore sees the world through the caregiver's eyes. Building an artificial humanoid robot's brain, even at an infant's cognitive level, has been a long quest which still lies only in the realm of our imagination. Our efforts towards such a dimly imaginable task are developed according to two alternate and complementary views: cognitive and developmental.The goal of this work is to build a cognitive system for the humanoid robot, Cog, that exploits human caregivers as catalysts to perceive and learn about actions, objects, scenes, people, and the robot itself. This thesis addresses a broad spectrum of machine learning problems across several categorization levels. Actions by embodied agents are used to automatically generate training data for the learning mechanisms, so that the robot develops categorization autonomously. Taking inspiration from the human brain, a framework of algorithms and methodologies was implemented to emulate different cognitive capabilities on the humanoid robot Cog. This framework is effectively applied to a collection of AI, computer vision, and signal processing problems. Cognitive capabilities of the humanoid robot are developmentally created, starting from infant-like abilities for detecting, segmenting, and recognizing percepts over multiple sensing modalities. Human caregivers provide a helping hand for communicating such information to the robot. This is done by actions that create meaningful events (by changing the world in which the robot is situated) thus inducing the "compliant perception" of objects from these human-robot interactions. Self-exploration of the world extends the robot's knowledge concerning object properties. This thesis argues for enculturating humanoid robots using infant development as a metaphor for building a humanoid robot's cognitive abilities. A human caregiver redesigns a humanoid's brain by teaching the humanoid robot as she would teach a child, using children's learning aids such as books, drawing boards, or other cognitive artifacts. Multi-modal object properties are learned using these tools and inserted into several recognition schemes,by Artur Miguel Do Amaral Arsenio.Ph.D

Investigation into the control of an upper-limb myoelectric prosthesis

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN053608 / BLDSC - British Library Document Supply CentreGBUnited Kingdo