In the past, previous researchers have established different method for developing inversekinematics of the RV-2AJ robot than the one being discussed in this paper. Such is by implementing an iterative algorithm based on Jacobian transpose matrix (Haklidir & Tasdelen, 2009). However, for no reasonable justification, the kinematic model was developed only up to the first three joints (instead of five) and no experiments result were showed at all to verify the accuracy of the model. As a result of the limited DOF and the unknown accuracy, the position of the end-effector will always be ambiguous. Several other researches have also developed the inversekinematics solution for the RV-2AJ, but again the accuracy of the models were not proven by any approach (Coman, Balan, Donca, & Verde ş , 2011; Coman, Stan, Manic, & Balan, 2009; Šljivo, 2013). On that account, this paper addresses this matter thoroughly with comparison to experimental results in order to validate the accuracy of the developed model. In addition, considering that the proposed method in this paper is limited to just controlling the z-axis Cartesian position of the robot, the process is much simpler and straightforward to develop compared to other conventional method from previous researchers.
Figure 2.2 illustrates the block diagram that the path parameters in the operational space are the initial and final end-effector location, the inversekinematics is to calculate the required angles need to be rotated which is the inputs to the trajectory generation to specify the position, velocity and the acceleration of the end effector of the manipulator. In addition, Figure 2.3 shows the process of moving the end effector of the manipulator from an initial position to a desired final position.
. Assumptions are made such that the inversekinematics of single trunk is implemented to get the complete solution by compensating the changes in orientation. The InverseKinematics for a 5-DOF P-Arm Manipulator is done by De Xu . The solution is done multiplying the individual matrices formed by D-H conventions and computing the final Transformation matrix which gives the position and Orientation elements for inverse kinematic solution. The new strategy is also developed based on the geometric projection which creates the necessary criteria to determine the accurate solutions of inversekinematics without using the forward kinematics. Li Han et al, have given a new inversekinematics approach to serial chain robots . Instead of regular geometric parameters a new set of parameters like Diagonal length, triangle orientation etc. can be used in solving inversekinematics with spherical joints in space and revolute joints in plane. These parameters give linear equations which can be easily solved in techniques like linear programming, diagonal sweeping etc.
With the development of computer technology, robot technology has entered a peak period of development[1-2], especially for industrial robots used in manufacturing, which is expected to become the second largest industry after the automobile industry. More and more manufacturers are beginning to introduce industrial robots. Instead of manpower, this will greatly improve production efficiency and reduce labor costs. Industrial robots have a relatively fixed working environment and are used to complete a specific task for a long time. Therefore, trajectory planning has become a key technology. In this paper, the KR 210 R2700 extra type manipulator is used as the research object. The kinematics model is established by DH parameter method. The forward and inversekinematics problems are analyzed. The virtual prototype model is established by multi-body dynamics software ADAMS. The kinematics simulation achieves the goal of the mechanical arm to complete a certain preset condition, and obtains relevant dynamic data, which lays a foundation for further research.
This paper presents the inversekinematics analysis of the five degree of freedom (DOF) Mitsubishi Melfa RV- 2AJ industrial robot. The proposed method is used specifically for controlling the z-axis Cartesian position. The kinematics problem is defined as the transformation from the robot’s end-effector Cartesian space to the joint angle of the robotic arms. An analytical solution using trigonometry illustration is presented to describe the relation between the position of the robot end-effector to each of the robot joints. Several lab experiments to validate the established kinematics equations have been conducted. In this study, the developed kinematics solutions were found to be accurate to approximately 99.83% compared to the real robot. These findings have significant implication for developing a kinematic simulation model that can be used to evaluate position and force control algorithm.
employ various design and control approaches. In terms of design, like in most designing prac- tices, robots are desired to have minimum self-weight with maximum output with respect to a task/application for an energy efficient use. In this paper, we explore possibilities of designing a four-legged bio inspired robotic system made of wood. This robot that we call BiQR – Bio inspired quadruped robot, has a skeleton made of cedar wood. It has 8 degrees of freedom (2 at each leg) available through 8 servo motors in order to achieve basic static locomotion in a designed environment. We used inversekinematics based position control to obtain the walk gait implementation. Most the of the system components are designed and fabricated locally using off the shelf components hence making the system low cost and meeting the desired physical specification with respect to the desired size, the weight and the demonstration of the walking gait. Figure 1 shows an overview of BiQR robot. This paper consists of five segments. After introduction, we briefly discuss the design and control methods used to realize BiQR. In experiments, we have implemented a two-stage methodology to ob- tain static walking trajectories (we have also explained walking patterns that were aimed to be imple- mented using our robot) for all the robot legs; implementation of gait pattern at one leg, and expanding results to all the four legs of BiQR in order to implement the static walk gait. Finally, we conclude with a discussion of results followed by the conclusion and the future work.
9 In the general problem of inversekinematics is quite difficult, it turns out that for manipulators having six joints, with the last three joints intersecting at a point. It is possible to decouple the inversekinematics problem into two simpler problems, known respectively, as inverse position kinematics, and inverse orientation kinematics. To put it another way, for a 6 DOF manipulator with a spherical wrist, the inversekinematics problem may be separated into two simpler problems, namely first finding the position of the intersection of the wrist axes, hereafter called the wrist center, and then finding the orientation of the wrist.
Gait planning based on linear inverted pendulum (LIPM) on structured road surface can be quick- ly generated because of the simple model and definite physical meaning. However, over-simplifi- cation of the model and discontents of zero velocity and acceleration boundary conditions when robot starts and stops walking lead to obvious difference between the model and the real robot. In this paper, parameterized gait is planned and trajectories’ smoothness of each joint angle and centroid are ensured using the 3-D LIPM theory. Static walking method is used to satisfy zero ve- locity and acceleration boundary conditions. Besides, a multi-link model is built to validate the stability. Simulation experiments show that: despite of some deviation from the theoretical solu- tion, the actual zero-moment point (ZMP) is still within the support polygon, and the robot walks steadily. In consequence, the rationality and validity of model simplification of LIPM is demon- strated.
We have developed a bipedal robot called MARI-3  to explore fast walking, running and jumping dynamics. The mechanical specifications of MARI-3 are summarized in Table I. MARI-3 has total of 13 DOF (degrees of freedom). Each leg has 6 DOF and 1 DOF at the waist for undesired yaw moment compensation. Furthermore, it is equipped with 6 axis Force/Torque sensors at the ankles. Plus, MARI-3 has a 3 axis rate gyro sensor and a 3 axis accelerometer at the pelvis. In its electronic hardware structure, the main controller is a 32 bit built-in Renesas SH-4 microcontroller. Data communication is performed via RON (RObot Network) bus which is a serial communication bus system, developed in our laboratory. Fig. 2 and Fig. 3 show the actual robot and its ROCOS  (RObot COntrol Simulator) simulation model. ROCOS is a 3-D dynamic simulator, also developed in or laboratory.
In this paper we are using six link robot shown in fig. 2. In order to find relation between first link to last link fixed the base (first link). This can be obtained from the description of the coordinate transformations between the coordinate frames attached to all the links and forming the overall description in a recursive manner. For this purpose, the position and orientation of the rigid body is useful for obtaining the composition of coordinate transformations between the consecutive frames. As a first step, this method is to be derived to define the relative position and orientation of two successive links. The problem is to define two frames attached to two successive links and make the coordinate transformation between them. It is convenient to set some rules for the definition of the link frames.
A legged system is a robotic system that has a specific mission, ensuring locomotion. Such a system could be analyzed from its kinematics aspects and also its dynamics aspects. The kinematics aspects are centered on the joints motions, constraints and limits. Forward kinematics is the mathematical relationship between the end segments of an articulated system and its joints motions. In inversekinematics, the problem consists of computing the needed joints motion s leading a known end segment position . Biped robots are specific robotic system that combines open and close kinematics chains; in biped locomotion two key phases are important, the double support phase and the single support phase also known as stance phase and swing phase. In double support both legs are in contact with the flow the obtained articulated body tend to move its COM, Centre of mass, position to prepare the next step, in single support or swing phase a leg is used as a support while the opposite one swings forwards, this motion causes the displacement of the COM in the same direction of the swing, when the swing leg is once again in contact with the floor the COM is placed on its surface and that gives a forward step.
Robot path planning is one of the most important tasks in robotic, especially for robots that need to follow a particular path in hazardous environments [11-12]. The control problem of the manipulator can be defined as the determination and executing of the time sequence of the links movements including the determination of the forces or torques to be developed by the actuators to guarantee the accomplishing of the manipulator task. It is worth mentioning that the manipulator task may be affected by some internal and external factors coming from the manipulator construction and the environment, respectively. Environmental excitations can arise from inaccuracies due to workspace constraints, static friction in joints, mechanical compliance in linkages, electric noise in transducer signals, limitations in the precision of computation. The most important factor of interest in this paper is the external excitation that may occur in dynamic environments [13-15].
IJEDR1504082 International Journal of Engineering Development and Research (www.ijedr.org) 507 The forward kinematics is worried with the relationship between the patient joints of the robot manipulator and the career (x, y and z) and orientation ( Φ ) of the end-effector. Stated more formally, the forward kinematics is to ascertain the career and orientation of the end-effector, given the values for the joint variables ( Ɵ i , a i , d i , α i ) of the robot. The joint variables are the
In industrial sector especially involved robotic application, there were several methods to solve the forward kinematics solution by using conventional method or using software. Usually user will used the conventional method to solve the forward kinematics problem. The complexity of formulation and computational burden make this method hard to be implemented in practice which also will waste a lot of time . This method is a complex calculation that needs precision when working on kinematics solution. So, the more DOF is implemented, the longer and more complex the calculation would be.
Therefore, we will have 8 possible candidate points for M 2 : Finding these points does not mean that the robot arm’s second joint can be placed in each of them. The robotic arm has physical limits, as it is mention at the very begging and it can move with angle of θ 1min - θ 1max and θ 2min - θ 2max . For that reason, additional validation and selection needs to be done. Only those angles falling into intervals between θ 1min - θ 1max , θ 2min - θ 2max will be taken into consideration and will be used to determine a single possible solution. Fig 2: Spatial position of the robot arm links 3. ANFIS CONTROLLER FOR SOLVING THE INVERSEKINEMATICS PROBLEM Adaptive Neuro Fuzzy Interface System, or shortly ANFIS, generally represents combination of fuzzy logic and neural network. Fuzzy systems have the ability to represent comprehensive linguistic knowledge (given for example by a human expert and preform reasoning by means of rules). However, fuzzy systems do not provide a mechanism to automatically acquire or tune those rules. On the other hand, neural networks are adaptive systems that can be trained and tuned from a set of samples. Therefore, this combination of the positive characteristics of the neural networks with the fuzzy logic could be a viable option for solution of non-linear problems, such as inversekinematics of a robot manipulators. In our specific case of a planar two degrees of freedom robot arm the ANFIS controller is composed of two ANFIS networks aimed at computing the joint angles θ 1 and θ 2 taking as input the coordinates of the end effector. The proposed controller architecture is presented in Figure 3. Fig 3: ANFIS Controller architecture for 2DOF planar manipulator inversekinematics problem calculation The first phase of development of an ANFIS controller foresees the network training. For this reason, training data are required so that later on ANFIS output can be computed. In order to generate the training dataset, the forward kinematics equations are used: (19)
The goal is to approximate the trajectory of the humanoid foot of a robot which is produced by the torque generated by the actuators input variables. Such a model has to fulfil two conflicting objectives. It must include enough details to represent the real behavior of the robot with sufficient accuracy, and it should permit an efficient, stable evaluation not only of the model equations but also of their derivatives that are needed in optimization. Such a model for the dynamic behavior is necessary for the mechanical design of the structure , choice of actuators, development of control strategies, and simulation humanoid motion.
In this study, a non-iterative geometric method has been proposed for solving IK of lead- module in spatially redundant snake-like robotic manipulator designed for radiosurgical operation. The modular robot is designed such that there are 2n links in each module and the direction of rotation of each link is orthogonal to the preceding one. This is quite complex for existing approaches; hence, the basic idea of the proposed method is to define n-1 VPs in the workspace of an n-link module. Positions of the VPs determine viable joint space (posture) needed to place tip of the last link at given targets in a work- space. Similarly, location of the mid-VP for a given point can be varied to compute mul- tiple IK solutions. This is useful for collision avoidance and safe interaction between the snake-like robot and other organs in its workspace. In this paper, the geometric method is applied to solve IK of two-link and four-link modules which are capable of spatial nav- igations. Results from Matlab simulation prototype of the snake-like robot show that the proposed method is computationally inexpensive, fast and accurate. Collision avoidance strategy enriches the proposed method in switching between different joint configura- tions rather than interrupting movements of links in cluttered areas.
China, much progress has been made in the research of tomato harvesting robot, such as the manipulator, image recognition, and motion control [8-11] . In general, current robots are not intelligent enough, and the success rate and pitching rate fall far short of what is expected. In addition, most tomato harvesting robots are designed for harvesting tomatoes at a certain height along the guide rail. The two-wheel drive control and differential steering control cannot meet the demand for the automatic harvesting of fruits and vegetables. In addition, the picking arms are generally replaced by industrial robotic arms, which suffer from the drawback of high cost and complexity of control. It takes 3-7 s to pick a citrus, 15 s to pick a melon, 10 s to pick a cucumber, and 1 min to pick an eggplant, respectively, and the harvest rate is less than 90%. The fruits and vegetables are identified mainly by color characteristic, the grey degree threshold and the geometrical shape. These algorithms are likely to be affected by light and environmental factors, and it is difficult to identify overlapping fruits.
According to the “Robot Institute of America,” 1979, “A robot is defined as a reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks.” Webster’s dictionary simplifies the definition by stating that, “a robot is an automatic device that performs functions normally ascribed to humans or a machine in the form of a human.” A robot is described as a machine designed to execute one or more tasks repeatedly, with speed and precision. There are as many different types of robots as there are tasks for them to perform.