다자유도 갠트리형 용접로봇 시스템의 최적모션 생성을 위한 벡터 방정식 알고리즘 기초 연구

Dynamics and motion planning of a wheel-legged robot with two manipulator arms is discussed in this paper. This robot can move on flat surfaces fast, while it has the capability of moving over rough terrains. The two locomotion subsystems, i.e. wheeled mechanism and legged mechanism, will be discussed separately. Wheeled locomotion mechanism includes three wheels, two of them are in the back as active wheels with a differential driver. Legged locomotion mechanism includes two legs each with 4 revolute joints, i.e. 4 degrees of freedom (DOF). Two manipulators are installed on the main body to perform variant tasks, each of them is a PRR 3 DOF arm. Using direct path method (DPM) the robot kinematics is developed, and using Lagrangian approach the equations of motion are derived. A new method, called return path method (RPM), is proposed to add the constraints of the system to the kinetics equations without introducing additional variables. To eliminate the terms corresponding to the constraints, natural orthogonal complement method is used. Finally, motion planning of the robot is investigated to develop a stable gait planning procedure.

Many small and large industries use robot arms to establish a range of tasks such as picking and placing, and painting in today's world. However, to complete these tasks, one of the most critical problems is to obtain the desire position of the robot arm's end-effector. There are two methods for analyzing the robot arm: forward kinematic analysis and inverse kinematic analysis. This study aims to model the forward and inverse kinematic of an open-source 4 degrees of freedom (DoF) articulated robotic arm. A kinematic model is designed and further evaluated all the joint parameters to calculate the end-effector's desired position. Forward kinematic is simple to design, but as for the inverse kinematic, a closed-form solution is needed. The developed kinematic model's performance is assessed on a simulated robot arm, and the results were analyzed if the errors were produced within the accepted range. At the end of this study, forward kinematic and inverse kinematic solutions of a 4-DoF articulated robot arm are successfully modeled, which provides the theoretical basis for the subsequent analysis and research of the robot arm.

The main objectives of the present work are to model and implement a control system of five degrees of freedom (5 DOF) humanoid arm by using an exoskeleton system with 3 DOF. The exoskeleton system is used to transport the movements of the human’s arm to the controller to control the humanoid arm by using sensors at each target joint to measure the angular displacement of it. To achieve this target, a kinematic analysis of the 5 DOF arm robot has been carried out including the forward, and solving the inverse kinematics. In addition, heterogeneous teleoperation based on forward and inverse kinematic between the humanoid arm and the exoskeleton model has been performed. Also, an experimental set up has been arranged to include the sensors considered (angular displacement sensors), control unit, signal-processing boards, and the actuators (servo motors of the humanoid arm robots). The control experiments have been conducted based on PID control. Keywords : Forward kinematic, inverse kinematic, teleoperation, humanoid robot Cite this Article Elsamanty M, Sayed M, Abdr Aboo S. Heterogeneous Teleoperation Model for Controlling 3D Printed Humanoid Robot Arm with 5 DOF using 3 DOF Exoskeleton. Journal of Control & Instrumentation. 2019; 10(2): 1–15p.

ABSTRACTDue to interference phenomena among unnatural dimensions of the motion robots’ operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

This paper proposes a new kinematic structure of a redundant serial robot arm and presents forward and inverse kinematic analysis. This is a new structure developed based on the robot IRB 2400 of ABB. The new structure consists of six revolute joints and two prismatic joints. The proposed robot arm has only seven degrees of freedom because the structure has a constraint between two revolute joints. Two prismatic joints help to expand workspaces of manipulator from small to very large. The paper describes in details about forward and inverse kinematics. Forward kinematics is derived based on DH Convention while inverse kinematics is calculated based on an objective function to minimize motions of a revolute joint and two prismatic joints. The simulation results on Matlab software indicated that the joint positions and velocities of a redundant serial robot arm matched well the trajectories in Cartesian Space.

In recent years, the utilization of 3D modeling for various robotic systems has gained significant traction, serving educational, research, and diverse applications. Presently, there are a multitude of 3D modeling tools tailored to different facets of robotic research, each possessing distinct advantages and limitations. This paper presents the development of novel algorithms and software aimed at constructing 3D representations of robotic arms that have enabled the visualization of the manipulator and its links’ motion. These algorithms allow for the depiction of forward kinematic outcomes through spatial 3D graphs. Consequently, these graphical representations provide a tangible means to observe the changes in module components and parameter orientations as the manipulator assumes different spatial configurations. Within the Maple environment, a comprehensive 3D model of robotic arms was constructed, and the forward kinematic problem was solved for a manipulator that had five degrees of freedom (DoF). This endeavor was achieved by implementing the Denavit-Hartenberg method to elucidate kinematic characteristics and the Newton-Euler method to ascertain the velocity and acceleration of the robot manipulator’s links. The newly created algorithms and software are expected to be widely used in 3D modeling of robotic manipulators as scientific research in industries and other complex mechanical systems.

At AROB5, we proposed a solution to the path planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term, and a collision term. While the results of our simulation showed the effectiveness of our approach, the values of the weights in the objective function were not given by any theoretical method. This article presents a theoretical method using reinforcement learning for adjusting the weight parameters. We applied Williams' learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. We verified the learning rule by some experiments.

Policy learning is an active topic in dialogue systems research, but it has not been explored in relation to interactive question answering (IQA). We take a first step in learning adaptive interaction policies for question answering : we address the question of how to acquire enough reliable query constraints, how many database results to present to the user and when to present them, given the competing trade-offs between the length of the answer list, the length of the interaction, the type of database and the noise in the communication channel. The operating conditions are reflected in an objective function which we use to derive a hand-coded threshold-based policy and rewards to train a reinforcement learning policy. The same objective function is used for evaluation. We show that we can learn strategies for this complex trade-off problem which perform significantly better than a variety of hand-coded policies, for a wide range of noise conditions, user types, types of DB and turn-penalties. Our policy learning framework thus covers a wide spectrum of operating conditions. The learned policies produce an averagerelativeincrease in reward of 86.78% over the hand-coded policies. In 93% of the cases the learned policies perform significantly better than the hand-coded ones (p< .001). Furthermore we show that the type of database has a significant effect on learning and we give qualitative descriptions of the learned IQA policies.

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

This paper presents a kinematic characterization of the Origami Spring, which can change its shape from a flat, contracted state to a solid, extended state. Through observation using a real model, the Origami Spring was modelled as a spherical 6R linkage with 3 degrees of freedom (DOF). To obtain a kinematic model, which enables kinematic design of the Origami Spring for real applications, mobility analysis and displacement analysis have been performed. To reveal the actual behavior of the Origami Spring: it behaves like a single DOF, displacement analysis has been formulated as an optimization problem with a single input using objective functions taking into consideration collision between links. Numerical examples were presented to discuss the kinematic characteristics of the Origami Spring.

This issue's SIGEST paper, from the SIAM Journal on Optimization (SIOPT), is an excellent example of deep mathematical analysis, involving a range of mathematical techniques, which is having significant practical algorithmic and computational consequences. The title of the paper, “A Sum of Squares Approximation of Nonnegative Polynomials,” by Jean B. Lasserre, combines two concepts that are known to all SIAM readers: sums of squares, the types of functions that arise in many data fitting and other applications; and nonnegative polynomials, polynomials whose values are greater than or equal to zero for all inputs. It is obvious that any polynomial that can be written as a sum of squares is nonnegative. It is far from obvious whether any nonnegative polynomial has a representation as, or arbitrarily close to, a sum of squares of polynomials. In this SIGEST paper, Lasserre shows that this is the case. The nomination from the editors of SIOPT captures the immediate and more general contributions of this paper eloquently, in the context of a series of three papers of which this is the second: “Jean Lasserre, in a series of papers (starting from the first paper in SIOPT Vol. 11, pp. 796–817, and concluding with the third paper in SIOPT Vol. 15, pp. 383–393) by exploiting deep results from real algebraic geometry and theory of moments for multivariate polynomials, showed that the optimal value of a nonconvex polynomial optimization problem (i.e., an optimization problem whose objective function and constraints are specified by multivariate polynomials) can asymptotically be approximated by an increasing sequence of optimal values from increasing tighter semidefinite programming relaxation problems. Even though the result is asymptotic in the most general case, fortuitously, practical experience has revealed that very often, the first few levels of semidefinite programming relaxations typically can achieve the global optimum value. The theoretical connection that Jean Lasserre (and Pablo Parrilo at about the same time from the viewpoint of sum of squares representations of multivariate polynomials) had established between polynomial optimizations with real algebraic geometry and theory of moments has led to exciting developments in combinatorial optimization. In particular, many hard combinatorial problems such as the maximum clique problem of a graph and the quadratic assignment problem are now actively revisited by researchers under the framework of polynomial optimization to derive increasingly tighter bounds.” That is, a consequence of this and related work is the ability to use a sequence of semidefinite programming problems to (nearly) solve much more difficult optimization problems. Semidefinite programming problems are problems with a linear objective function and linear constraints in a matrix variable X, plus the additional constraint that X be positive semidefinite. Since a rich array of efficient algorithms for solving semidefinite programming problems has been developed in recent years, reducing more general problems to a sequence of semidefinite programming problems is an important advance. We hope that readers of SIAM Review will enjoy this glimpse into the sophisticated mathematics behind an important algorithmic innovation in optimization.

In order to minimize the processing time for multiple robots to finish lots of tasks, an optimized multiple robots task allocation and path planning approach is proposed. In this approach, the optimized task sequence is first schemed on the assumption that all the tasks are processed by one robot. While scheming the task sequence. A star algorithm is employed to plan paths and calculate the path cost. Then, according to the number of the robots, the task sequence has been randomly divided into several task segments that will be assigned to robots. At last, the task number of each task segments are averaged according to the time each robot used, so that the optimized task allocation scheme is got. In this approach, not only multiple robot task allocation, but also the optimized path planning is realized to ensure that the robots can finish all the tasks in the shortest time. The experiments and simulation are provided to validate the effectiveness of the approach. The results show that the proposed approach can scheme optimized multiple robots task allocation and path planning scheme.

Optimization problems are used to model many real-life problems. Therefore, solving these problems is one of the most important goals of algorithm design. A general optimization problem can be defined by specifying a set of constraints that defines a subset in some underlying space (like the Euclidean space) called the feasible subset and an objective function that we are trying to maximize or minimize, as the case may be, over the feasible set. The difficulty of solving such problems typically depends on how ‘complex’ the feasible set and the objective function are. For example, a very important class of optimization problems is linear programming. Here the feasible subset is specified by a set of linear inequalities (in the Euclidean space); the objective function is also linear. A more general class of optimization problems is convex programming, where the feasible set is a convex subset of a Euclidean space and the objective function is also convex. Convex programs (and hence, linear programs) have a nice property that any local optimum is also a global optimum for the objective function. There are a variety of techniques for solving such problems – all of them try to approach a local optimum (which we know would be a global optimum as well). These notions are discussed in greater detail in a later section in this chapter. The more general problem, the so-called non-convex programs, where the objective function and the feasible subset could be arbitrary can be very challenging to solve. In particular, discrete optimization problems, where the feasible subset could be a (large) discrete subset of points falls under this category.

This book is on modern continuous nonlinear optimization. Continuous nonlinear optimization problems have a simple mathematical model and always refer to a real physical system, the running of which we want to optimize. Firstly, a nonlinear optimization problem contains an objective function which measures the performances or requirements of the system. Often, this function represents a profit, a time interval, a level, a sort of energy or combination of different quantities which have a physical significance for the modeler. The objective function depends on some characteristics of the system, called variables or unknowns. The purpose of any optimization problem is to find the values of these variables that minimize (or maximize) the objective function subject to some constraints which the variables must satisfy. Constraints of an optimization problem may have different algebraic expressions. There are static and dynamic constraints called functional constraints. The difference between these types of constraints comes from the structure of their Jacobian. The dynamic constraints always involve a Jacobian with a block-diagonal structure. Besides, these functional constraints are nonlinear functions which may be equalities or inequalities, or even range constraints. Another very important type of constraints is the simple bounds on variables. The real applications of optimization include simple bounds on variables which express the constructive engineering conditions and limits of the system to be optimized. The constraints of an optimization problem define the so-called feasible domain of the problem. Both the objective function and the constraints may depend on some parameters with known values which characterize the system under optimization. The process of identifying the variables, parameters, simple bounds on variables, the objective functions, and constraints is known as modeling, one of the finest intellectual activities. In this book we assume that the variables can take real values and the objective function and the constraints are smooth enough (at least twice continuously differentiable) with known first-order derivatives. When the number of variables and the number of constraints is large, then the optimization problem is quite challenging.

Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be controlled by an operator, set to do some specific jobs, or work autonomously. Robot path planning is the task of an autonomous robot to move safely from one position to another. In this paper, three new objective functions are introduced in the structure of improved grey wolf optimizer (IGWO) and improved particle swarm optimization (IPSO) for the robot path planning problems. As another part of our proposed method, a reduction of laser range finder (LRF) data is performed, and the avoidance collision approach is also introduced. Robots determine the next position by using LRF data and IGWO (IPSO) algorithms in a local approach. The initial and the goal positions are predefined for each robot. Moreover, the location of static obstacles and other robots are unknown for each robot. Finally, the experimental results of the robot path planning using IGWO are compared to different algorithms. The results indicate that the proposed method performs better in determining an optimal, short, safe, and smooth path. Also, it has less power and time consumption than other methods. All the algorithms are implemented in the V-REP robot simulator.