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

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Novel Methods For Human-robot Shared Control In Collaborative Robotics

Blended shared control is a method to continuously combine control inputs from traditional automatic control systems and human operators for control of machines. An automatic control system generates control input based on feedback of measured signals, whereas a human operator generates control input based on experience, task knowledge, and awareness and sensing of the environment in which the machine is operating. Such active blending of inputs from the automatic control agent and the human agent to jointly control machines is expected to provide benefits in terms of utilizing the unique features of both agents, i.e., better task execution performance of automatic control systems based on sensed signals and maintaining situation awareness by having the human in the loop to handle safety concerns and environmental uncertainties. The shared control approach in this sense provides an alternative to full autonomy. Many existing and future applications of such an approach include automobiles, underwater vehicles, ships, airplanes, construction machines, space manipulators, surgery robots, and power wheelchairs, where machines are still mostly operated by human operators for safety concerns. Developing machines for full autonomy requires not only advances in machines but also the ability to sense the environment by placing sensors in it; the latter could be a very difficult task for many such applications due to perceived uncertainties and changing conditions. The notion of blended shared control, as a more practical alternative to full autonomy, enables keeping the human operator in the loop to initiate machine actions with real-time intelligent assistance provided by automatic control. The problem of how to blend the two inputs and development of associated scientific tools to formalize and achieve blended shared control is the focus of this work. Specifically, the following essential aspects are investigated and studied. Task learning: modeling of a human-operated robotic task from demonstration into subgoals such that execution patterns are captured in a simple manner and provide reference for human intent prediction and automatic control generation. Intent prediction: prediction of human operator's intent in the framework of subgoal models such that it encodes the probability of a human operator seeking a particular subgoal. Input blending: generating automatic control input and dynamically combining it with human operator's input based on prediction probability; this will also account for situations where the human operator may take unexpected actions to avoid danger by yielding full control authority to the human operator. Subgoal adjustment: adjusting the learned, nominal task model dynamically to adapt to task changes, such as changes to target object, which will cause the nominal model learned from demonstration to lose its effectiveness. This dissertation formalizes these notions and develops novel tools and algorithms for enabling blended shared control. To evaluate the developed scientific tools and algorithms, a scaled hydraulic excavator for a typical trenching and truck-loading task is employed as a specific example. Experimental results are provided to corroborate the tools and methods. To expand the developed methods and further explore shared control with different applications, this dissertation also studied the collaborative operation of robot manipulators. Specifically, various operational interfaces are systematically designed, a hybrid force-motion controller is integrated with shared control in a mixed world-robot frame to facilitate human-robot collaboration, and a method that utilizes vision-based feedback to predict the human operator's intent and provides shared control assistance is proposed. These methods provide ways for human operators to remotely control robotic manipulators effectively while receiving assistance by intelligent shared control in different applications. Several robotic manipulation experiments were conducted to corroborate the expanded shared control methods by utilizing different industrial robots

Ubiquitous Robotic Technology for Smart Manufacturing System

As the manufacturing tasks become more individualized and more flexible, the machines in smart factory are required to do variable tasks collaboratively without reprogramming. This paper for the first time discusses the similarity between smart manufacturing systems and the ubiquitous robotic systems and makes an effort on deploying ubiquitous robotic technology to the smart factory. Specifically, a component based framework is proposed in order to enable the communication and cooperation of the heterogeneous robotic devices. Further, compared to the service robotic domain, the smart manufacturing systems are often in larger size. So a hierarchical planning method was implemented to improve the planning efficiency. A test bed of smart factory is developed. It demonstrates that the proposed framework is suitable for industrial domain, and the hierarchical planning method is able to solve large problems intractable with flat methods

Motion planning for constrained mobile robots in unknown environments

Ph.DDOCTOR OF PHILOSOPH

Planning for human robot interaction

Les avancées récentes en robotique inspirent des visions de robots domestiques et de service rendant nos vies plus faciles et plus confortables. De tels robots pourront exécuter différentes tâches de manipulation d'objets nécessaires pour des travaux de ménage, de façon autonome ou en coopération avec des humains. Dans ce rôle de compagnon humain, le robot doit répondre à de nombreuses exigences additionnelles comparées aux domaines bien établis de la robotique industrielle. Le but de la planification pour les robots est de parvenir à élaborer un comportement visant à satisfaire un but et qui obtient des résultats désirés et dans de bonnes conditions d'efficacité. Mais dans l'interaction homme-robot (HRI), le comportement robot ne peut pas simplement être jugé en termes de résultats corrects, mais il doit être agréable aux acteurs humains. Cela signifie que le comportement du robot doit obéir à des critères de qualité supplémentaire. Il doit être sûr, confortable pour l'homme, et être intuitivement compris. Il existe des pratiques pour assurer la sécurité et offrir un confort en gardant des distances suffisantes entre le robot et des personnes à proximité. Toutefois fournir un comportement qui est intuitivement compris reste un défi. Ce défi augmente considérablement dans les situations d'interaction homme-robot dynamique, où les actions de la personne sont imprévisibles, le robot devant adapter en permanence ses plans aux changements. Cette thèse propose une approche nouvelle et des méthodes pour améliorer la lisibilité du comportement du robot dans des situations dynamiques. Cette approche ne considère pas seulement la qualité d'un seul plan, mais le comportement du robot qui est parfois le résultat de replanifications répétées au cours d'une interaction. Pour ce qui concerne les tâches de navigation, cette thèse présente des fonctions de coûts directionnels qui évitent les problèmes dans des situations de conflit. Pour la planification d'action en général, cette thèse propose une approche de replanification locale des actions de transport basé sur les coûts de navigation, pour élaborer un comportement opportuniste adaptatif. Les deux approches, complémentaires, facilitent la compréhension, par les acteurs et observateurs humains, des intentions du robot et permettent de réduire leur confusion.The recent advances in robotics inspire visions of household and service robots making our lives easier and more comfortable. Such robots will be able to perform several object manipulation tasks required for household chores, autonomously or in cooperation with humans. In that role of human companion, the robot has to satisfy many additional requirements compared to well established fields of industrial robotics. The purpose of planning for robots is to achieve robot behavior that is goal-directed and establishes correct results. But in human-robot-interaction, robot behavior cannot merely be judged in terms of correct results, but must be agree-able to human stakeholders. This means that the robot behavior must suffice additional quality criteria. It must be safe, comfortable to human, and intuitively be understood. There are established practices to ensure safety and provide comfort by keeping sufficient distances between the robot and nearby persons. However providing behavior that is intuitively understood remains a challenge. This challenge greatly increases in cases of dynamic human-robot interactions, where the actions of the human in the future are unpredictable, and the robot needs to constantly adapt its plans to changes. This thesis provides novel approaches to improve the legibility of robot behavior in such dynamic situations. Key to that approach is not to merely consider the quality of a single plan, but the behavior of the robot as a result of replanning multiple times during an interaction. For navigation planning, this thesis introduces directional cost functions that avoid problems in conflict situations. For action planning, this thesis provides the approach of local replanning of transport actions based on navigational costs, to provide opportunistic behavior. Both measures help human observers understand the robot's beliefs and intentions during interactions and reduce confusion

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research