Payload maximization for mobile flexible manipulators in environment with an obstacle

A mobile flexible manipulator is developed in order to achieve high performance requirements such as high-speed operation, increased high payload to mass ratio, less weight, and safer operation due to reduced inertia. Hence, this paper presents a method for finding the Maximum Allowable Dynamic Load (MADL) of geometrically nonlinear flexible link mobile manipulators. The full dynamic model of a wheeled mobile base and the mounted flexible manipulator is considered with respect to dynamics of non-holonomic constraint in environment including an obstacle. In dynamical analysis, an efficient model is employed to describe the treatment of a flexible structure in which both the geometric elastic nonlinearity and the foreshortening effects are considered. Then, a path planning algorithm is developed to find the maximum payload that the optimal strategy is based on the indirect solution to the open-loop optimal control problem. In order to verify the effectiveness of the presented algorithm, several simulation studies are carried out for finding the optimal path between two points in the presence of obstacles. The results clearly show the effect of flexibility and the proposed approach on mobile flexible manipulators

Do Chatbots Dream of Androids? Prospects for the Technological Development of Artificial Intelligence and Robotics

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation

Robust time-optimal path tracking control of robots : theory and experiments

[v.1. Main work] -- [v.2]. Implementation detail

Design and analysis of robots that perform dynamic tasks using internal body motion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1994.Includes bibliographical references (leaves 92-97).by Kevin Lawrence Brwon.Ph.D

Kinodynamic planning and control of closed-chain robotic systems

Aplicat embargament des de la data de defensa fins el dia 1/6/2022This work proposes a methodology for kinodynamic planning and trajectory control in robots with closed kinematic chains. The ability to plan trajectories is key in a robotic system, as it provides a means to convert high-level task commands¾like “move to that location'', or “throw the object at such a speed''¾into low-level controls to be followed by the actuators. In contrast to purely kinematic planners, which only generate collision-free paths in configuration space, kinodynamic planners compute state-space trajectories that also account for the dynamics and force limits of the robot. In doing so, the resulting motions are more realistic and exploit gravity, inertia, and centripetal forces to the benefit of the task. Existing kinodynamic planners are fairly general and can deal with complex problems, but they require the state coordinates to be independent. Therefore, they are hard to apply to robots with loop-closure constraints whose state space is not globally parameterizable. These constraints define a nonlinear manifold on which the trajectories must be confined, and they appear in many systems, like parallel robots, cooperative arms manipulating an object, or systems that keep multiple contacts with the environment. In this work, we propose three steps to generate optimal trajectories for such systems. In a first step, we determine a trajectory that avoids the collisions with obstacles and satisfies all kinodynamic constraints of the robot, including loop-closure constraints, the equations of motion, or any limits on the velocities or on the motor and constraint forces. This is achieved with a sampling-based planner that constructs local charts of the state space numerically, and with an efficient steering method based on linear quadratic regulators. In a second step, the trajectory is optimized according to a cost function of interest. To this end we introduce two new collocation methods for trajectory optimization. While current methods easily violate the kinematic constraints, those we propose satisfy these constraints along the obtained trajectories. During the execution of a task, however, the trajectory may be affected by unforeseen disturbances or model errors. That is why, in a third step, we propose two trajectory control methods for closed-chain robots. The first method enjoys global stability, but it can only control trajectories that avoid forward singularities. The second method, in contrast, has local stability, but allows these singularities to be traversed robustly. The combination of these three steps expands the range of systems in which motion planning can be successfully applied.Aquest treball proposa una metodologia per a la planificació cinetodinàmica i el control de trajectòries en robots amb cadenes cinemàtiques tancades. La capacitat de planificar trajectòries és clau en un robot, ja que permet traduir instruccions d'alt nivell com ara ¿mou-te cap aquella posició'' o ¿llença l'objecte amb aquesta velocitat'' en senyals de referència que puguin ser seguits pels actuadors. En comparació amb els planificadors purament cinemàtics, que només generen camins lliures de col·lisions a l'espai de configuracions, els planificadors cinetodinàmics obtenen trajectòries a l'espai d'estats que són compatibles amb les restriccions dinàmiques i els límits de força del robot. Els moviments que en resulten són més realistes i aprofiten la gravetat, la inèrcia i les forces centrípetes en benefici de la tasca que es vol realitzar. Els planificadors cinetodinàmics actuals són força generals i poden resoldre problemes complexos, però assumeixen que les coordenades d'estat són independents. Per tant, no es poden aplicar a robots amb restriccions de clausura cinemàtica en els quals l'espai d'estats no admeti una parametrització global. Aquestes restriccions defineixen una varietat diferencial sobre la qual cal mantenir les trajectòries, i apareixen en sistemes com ara els robots paral·lels, els braços que manipulen objectes coordinadament o els sistemes amb extremitats en contacte amb l'entorn. En aquest treball, proposem tres passos per generar trajectòries òptimes per a aquests sistemes. En un primer pas, determinem una trajectòria que evita les col·lisions amb els obstacles i satisfà totes les restriccions cinetodinàmiques, incloses les de clausura cinemàtica, les equacions del moviment o els límits en les velocitats i en les forces d'actuació o d'enllaç. Això s'aconsegueix mitjançant un planificador basat en mostratge aleatori que utilitza cartes locals construïdes numèricament, i amb un mètode eficient de navegació local basat en reguladors quadràtics lineals. En un segon pas, la trajectòria s'optimitza segons una funció de cost donada. A tal efecte, introduïm dos nous mètodes de col·locació per a l'optimització de trajectòries. Mentre els mètodes existents violen fàcilment les restriccions cinemàtiques, els que proposem satisfan aquestes restriccions al llarg de les trajectòries obtingudes. Durant l'execució de la tasca, tanmateix, la trajectòria pot veure's afectada per pertorbacions imprevistes o per errors deguts a incerteses en el model dinàmic. És per això que, en un tercer pas, proposem dos mètodes de control de trajectòries per robots amb cadenes tancades. El primer mètode gaudeix d'estabilitat global, però només permet controlar trajectòries que no travessin singularitats directes del robot. El segon mètode, en canvi, té estabilitat local, però permet travessar aquestes singularitats de manera robusta. La combinació d'aquests tres passos amplia el ventall de sistemes en els quals es pot aplicar amb èxit la planificació cinetodinàmica.Postprint (published version

에너지 효율 향상을 위한 로봇 머니퓰레이터의 경로 최적화와 스케줄링

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 8. 박종우.이 논문은 기구학적, 동역학적 조건에서 에너지 효율을 높이는 작업 스케줄링에 대해 다루고 있다. 작업 스케줄링에서 에너지 최적화를 수행 위해서는 로봇 매니퓰레이터의 최적 경로 생성이 같이 수행 되어져야하기 때문에, 작업 스케줄링과 최적 경로 생성의 통합 이슈는 이 논문에서 중요하게 다루어진다. 먼저 우리는 지나가야하는 경로점이 주어졌을 때 최적 에너지 경로 생성 알고리즘을 제시한다. 최적화 문제는 여러 개의 경로 점이 주어지거나 다양한 경계 조건들이 있거나 작업 수행 시간을 최적화해야 하는 경우를 다룰 수 있도록 정의되었다. 모든 경로들은 C-공간(조인트 공간)에서 B-spline으로 매개화 되었으며 목적함수들은 재귀적인 역 다이나믹스 방법으로 계산된다. 우리는 최적화 알고리즘의 계산 효율을 위해 해석적인 미분을 이용한다. 또한 적분을 위해 가우시안 구적법을 사용한다. 최적 에너지 경로 생성 알고리즘의 성능을 평가하기 위해 우리는 몇 가지 상황에서 경로를 생성해보고 그 결과를 분석한다. 또한 우리는 이 논문에서 동적 계획법을 이용한 작업 스케줄링 알고리즘을 제시한다. 우리는 먼저 몇 가지 가정을 통해 현실적인 문제 정의를 내린다. 알고리즘은 작업마다 최적의 로봇을 결정하고 언제 작업을 시작하면 좋을지 판단하며 최적의 작업 수행 시간을 찾는다. 얼마나 에너지 소비를 했는지는 이 논문에서 제시한 경로 최적화 알고리즘에 의해 계산되며 우리는 계산 량을 줄이기 위해 에너지 소비 함수를 근사하여 사용한다.This thesis presents an energy-optimal task scheduling algorithm with a point-to-point trajectory generation method under kinematic and dynamic constraints. Because the energy-optimal trajectory generation is inevitable for performing task scheduling with respect to energy optimality, the integration of them is a big issue in this thesis. We first propose an energy-optimal trajectory generation algorithm. The optimization problem is defined for multiple waypoints and various boundary conditions with free execution times. The trajectories are parameterized by B-spline curves in the joint space and the objective functions are obtained with joint torques which are calculated by a recursive inverse dynamics method. To make our algorithm computationally efficient, the gradients for the optimization are calculated analytically. Gaussian quadrature method which is proper for several reasons is used for the integration. We generate the optimal trajectories in several situations to evaluate our algorithm. We also propose an energy-optimal task scheduling algorithm using dynamic programming method. We first define a problem with four assumptions which can make our problem more practical. Our algorithm determines which robot is optimal for performing each task and finds the optimal time when each task starts and also we optimize the task execution times to minimize the energy consumption. The energy consumption for each task is calculated by energy-optimal trajectory generation algorithm proposed in this thesis. To reduce the computation time of our task scheduling algorithm, we provide an optimal energy consumption measurement which is approximated as a function of the execution time by performing energy-optimal trajectory generation algorithm only four times.Introduction 1 1.1 Main Contributions of This Thesis 5 1.2 Organization and Preview 6 Preliminaries 8 2.1 Lie Group 8 2.1.1 Special Euclidean Group 8 2.1.2 Generalized Velocity and Force 10 2.1.3 Adjoint Mapping 11 2.2 Kinematics of Serial Open-Chain Manipulators 12 2.2.1 Forward Kinematics 13 2.2.2 Inverse Kinematics 15 2.3 Dynamics of Serial Open-Chain Manipulators 15 2.3.1 Generalized Inertia 16 2.3.2 Dynamics of a Rigid Body 17 2.3.3 Recursive Inverse Dynamics and Its Derivatives 18 2.3.4 Closed Form of Dynamic Equations 20 Energy-Optimal Trajectory Generation 22 3.1 Problem Definition 23 3.2 B-Spline Curves 26 3.2.1 B-Splines with Final Time Parameter 30 3.2.2 Partial Differentiation of a B-Spline Curve 32 3.3 Gaussian Quadrature 34 3.4 Algorithm for Generating Energy-Efficient Trajectory 36 3.5 Case Studies 37 3.5.1 Effort 40 3.5.2 Energy Loss 41 3.5.3 Base Link Optimization 41 Task Scheduling of Energy-Optimal Trajectories 54 4.1 Problem Definition 55 4.2 Assumptions 57 4.3 Algorithm for Task Scheduling 62 4.3.1 Task Arranging 62 4.3.2 Optimization: Dynamic Programming and Trajectory Generation 63 4.3.3 Cost Function Approximation 64 4.4 Example: Pick and Place Motion 73 Conclusion 77 Bibliography 80 국문 초록 84Maste

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

Human centric collaborative workplace: the human robot interaction system perspective

The implementation of smart technologies and physical collaboration with robots in manufacturing can provide competitive advantages in production, performance and quality, as well as improve working conditions for operators. Due to the rapid advancement of smart technologies and robot capabilities, operators face complex task processes, decline in competences due to robots overtaking tasks, and reduced learning opportunities, as the range of tasks that they are asked to perform is narrower. The Industry 5.0 framework introduced, among others, the human-centric workplace, promoting operators wellbeing and use of smart technologies and robots to support them. This new human centric framework enables operators to learn new skills and improve their competencies. However, the need to understand the effects of the workplace changes remain, especially in the case of human robot collaboration, due to the dynamic nature of human robot interaction. A literature review was performed, initially, to map the effects of workplace changes on operators and their capabilities. Operators need to perform tasks in a complex environment in collaboration with robots, receive information from sensors or other means (e.g. through augmented reality glasses) and decide whether to act upon them. Meanwhile, operators need to maintain their productivity and performance. This affects cognitive load and fatigue, which increases safety risks and probability of human-system error. A model for error probability was formulated and tested in collaborative scenarios, which regards the operators as natural systems in the workplace environment, taking into account their condition based on four macro states; behavioural, mental, physical and psychosocial. A scoping review was then performed to investigate the robot design features effects on operators in the human robot interaction system. Here, the outcomes of robot design features effects on operators were mapped and potential guidelines for design purposes were identified. The results of the scoping review showed that, apart from cognitive load, operators perception on robots reliability and their safety, along with comfort can influence team cohesion and quality in the human robot interaction system. From the findings of the reviews, an experimental study was designed with the support of the industrial partner. The main hypothesis was that cognitive load, due to collaboration, is correlated with quality of product, process and human work. In this experimental study, participants had to perform two tasks; a collaborative assembly and a secondary manual assembly. Perceived task complexity and cognitive load were measured through questionnaires, and quality was measured through errors participants made during the experiment. Evaluation results showed that while collaboration had positive influence in performing the tasks, cognitive load increased and the temporal factor was the main reason behind the issues participants faced, as it slowed task management and decision making of participants. Potential solutions were identified that can be applied to industrial settings, such as involving participants/operators in the task and workplace design phase, sufficient training with their robot co-worker to learn the task procedures and implement direct communication methods between operator and robot for efficient collaboration

ロボティクスのための深層強化学習による運動シナジー発現と冗長性定量化に関する研究

Tohoku University林部充宏課

Model-free Optimization of Trajectory And Impedance Parameters on Exercise Robots With Applications To Human Performance And Rehabilitation

This dissertation focuses on the study and optimization of human training and its physiological effects through the use of advanced exercise machines (AEMs). These machines provide an invaluable contribution to advanced training by combining exercise physiology with technology. Unlike conventional exercise machines (CEMs), AEMs provide controllable trajectories and impedances by using electric motors and control systems. Therefore, they can produce various patterns even in the absence of gravity. Moreover, the ability of the AEMs to target multiple physiological systems makes them the best available option to improve human performance and rehabilitation. During the early stage of the research, the physiological effects produced under training by the manual regulation of the trajectory and impedance parameters of the AEMs were studied. Human dynamics appear as not only complex but also unique and time-varying due to the particular features of each person such as its musculoskeletal distribution, level of fatigue,fitness condition, hydration, etc. However, the possibility of the optimization of the AEM training parameters by using physiological effects was likely, thus the optimization objective started to be formulated. Some previous research suggests that a model-based optimization of advanced training is complicated for real-time environments as a consequence of the high level of v complexity, computational cost, and especially the many unidentifiable parameters. Moreover, a model-based method differs from person to person and it would require periodic updates based on physical and psychological variations in the user. Consequently, we aimed to develop a model-free optimization framework based on the use of Extremum Seeking Control (ESC). ESC is a non-model based controller for real-time optimization which its main advantage over similar controllers is its ability to deal with unknown plants. This framework uses a physiological effect of training as bio-feedback. Three different frameworks were performed for single-variable and multi-variable optimization of trajectory and impedance parameters. Based on the framework, the objective is achieved by seeking the optimal trajectory and/or impedance parameters associated with the orientation of the ellipsoidal path to be tracked by the user and the stiffness property of the resistance by using weighted measures of muscle activations