The design intent is to facilitate the movement of the proposed roboticmanipulator in constrained environments, such as rubble piles. The proposed roboticmanipulator with multi Degree of Freedom (m-DOF) links is capable of elongating by 25% of its nominal length. In this context, a design optimization problem with multiple objectives is also considered. In order to identify the benefits of the proposed design strategy, the reachable workspace of the proposed manipulator is compared with that of the Jet Propulsion Laboratory (JPL) serpentine robot. The simulation results show that the proposed manipulator has a relatively efficient reachable workspace, needed in constrained environments. The singularity and manipulability of the designed manipulator are investigated. In this study, we investigate the number of links that produces the optimal design architecture of the proposed roboticmanipulator. The total number of links decided by a design optimization can be useful distinction in practice
produce bulk quantities of high quality active material and reliably fabricate high-performance actuators and sensors. Biological continuum manipulators mostly rely on hard/discrete elements in their structure and/or operation to reduce complexity in interaction with their environment, and similarly, a judicious mixture of continuous/soft and discrete/hard elements would significantly improve the performance of these robots in most applications. To enable rapid virtual prototyping of soft robots, accurate physical models are needed. Development of models that accurately simulate the operation of these robots on the basis of the actuation inputs is a challenging multiphysics problem that can involve simultaneous analysis of solid and fluid mechanics, kinematics, electromechanics, thermodynamics and chemical kinetics of the processes involved. New approaches in path planning that are computationally efficient for deformable bodies with infinite DOF are required to make real-time path-planning viable. Integrating motionplanning with control and sensing is also an open problem. Grasping objects by using whole arm manipulation requires the grasp to be stable so that the arm does not undergo sudden changes in shape and drop the manipulated object. Robust path-planning and control algorithms must ensure the stability of all intermediate configurations of the manipulator along the prescribed path. Extensive literature exists for grasp synthesis and stability analysis of rigid-link robots that contact objects only at finite points Stable grasp synthesis for highly compliant and continuously deformable soft manipulators, however, will require solutions to many untouched and challenging design, control and planning problems.
The applications of all these controldesign techniques are also extended to robotic systems. In robotics, a manipulator is a device used to manipulate materials without direct contact. It consists of a controller and a manipulator arm. Their applications include Motionplanning, remote handling, Micro-robots, Machine tools etc. Performance of the manipulator depends upon controller to large extent. The robot performance is mainly influenced by the mechanical design and by the actuation system. The control problem is to define the input signals for the joints in order to achieve a predefined behavior for the manipulator.
Robotic manipulators have always been the major components of automated industries. The design of an industrial robotic work cell is a complex issue and it is highly dependent on the application and the environment. A robot equipped with a camera works more effi- ciently and prevents the damages that could occur in an industrial setup that has no vision component for the robot. A camera network, also known as the vision sensor network, is always considered an essential element in a robotic work cell design. The use of visual information to control a robot has been used extensively in many sectors for various appli- cations. The vision sensor network in a robotic work cell helps to monitor the work cell continuously. It leads to perceiving the entire scene in a robotic work cell. A dynamic work cell can be effectively controlled only with the help of a vision sensor network. This thesis also deals with a work cell consisting of a calibrated camera network. It is also interesting to note that a dynamic entity might enter this robotic work cell at any given time. These dynamic entities can either denote the target of interest or an obstacle to the target. The target of interest will be grasped by the robot’s end effector or gripper. The network of cameras helps to locate these dynamic entities and differentiate whether they are the tar- get or obstacle. The roboticmanipulator has to adapt to the position data provided by the vision sensor. The coverage strength model [6, 7] is used to achieve the detection and discrimination between the target and obstacle. The validation of the coverage strength model is found in  and an application of the coverage strength is found in . Section 2.4 explains the coverage strength model in detail.
In this section we derive the equations of motion for an individual link based on the direct method has been derived, i.e. Newton-Euler Formulation. The motion of a rigid body can be decomposed into the translational motion with respect to an arbitrary point fixed to the rigid body, and the rotational motion of the rigid body about that point. The dynamic equations of a rigid body can also be represented by two equations: one describes the translational motion of the centroid (or center of mass), while the other describes the rotational motion about the centroid. The former is Newton's equation of motion for a mass particle, and the latter is called Euler's equation of motion.
The Motion Assistant allows specifying motions in a graphical manner. By clicking and dragging with the mouse, we can manipulate the shape of the motion. Types of shapes that are available in the tool are straight-line movements and arc movements for up to 3 degrees of freedom. For the order of the profile we have the option between second order and third order profiles, which are also called respectively trapezoidal and s-curve profiles (these terms refer to the shape of the speed curve). A major drawback of the Motion Assistant is that it works only with hardware interfaces from National Instruments. This conflicts with the desire for a generally applicable path specification tool. Therefore the Motion Assistant will not be used further. Based on concepts as present in the Motion Assistant, the choice has been made to develop a path generation tool, which is hardware independent. This will be discussed in the next section.
Furthermore, using robot application in Malaysia will solve the problem for the rehabilitation instructor to monitor the patients frequently, it will give patient an advantaged to create the ability to do repetitive practice task during rehabilitation session in order to give repetitive practice for physical therapy to patient without the present of instructor . In addition, motion of robot can be controllable and able to quantify the recovery progress performance make them suitable to calibrate the rehabilitation motion with the recovery progress . Using this advantage, the rehabilitation session will goes smoothly with no human error by physiotherapists. Robot based rehabilitation will improve rehabilitation session more efficient because the no of finger rehabilitation robot can use more than three in a time. Make the more patient able to receive physical treatment in a period of treatment without need extra energy of physiotherapist. The robot will increase the effeteness of finger rehabilitation in future.
A review of currently available systems and tools is provided in section 2. The most traditional and widely used method of inspection has been by deploying ‘intelli- gent’ or ‘smart’ passive devices (pigs) into the pipe, driven by the pipeline product ow. Other inspection tools have included camera probes and robotic systems. However, the applications of these inspection devices have certain limitations due to pipeline congurations and restrictive components, such as bends and valves [6, 7]. The research work described in this paper is based on a critical evaluation of the above review. This is described in section 3, while section 4 outlines the system design overview. Section 5 describes the control scheme developed to provide a robust navigation inside gas pipe- lines. A navigation control algorithm is necessary to perform the desired motion and orientation control in an unknown pipeline environment. A proportional– integral–derivative (PID) control structure comple- mented with fuzzy logic is used. Initially, the input vari- ables are processed through the fuzzy logic operators and linked, via a set of linguistic rules, to the fuzzy output during the composition and encoding stages. The fuzzy output is then ‘defuzzied’ to produce a crisp output value . Section 6 summarizes the experimental results of the robotic vehicle unit in terms of its control and mobility in pipelines.
The problem of robot calibration is in the focus of the research community for many years (Stone 1987, Hollerbach 1989, Elatta 2004). However, there is a very limited number of works that directly address the issue of the identification accuracy and reduction of the calibration errors (Mooring 1991, Sun 2008, Hollerbach 2008). In general, there exist two main methods to improve the identification accuracy without increasing the number of experiments. The first of them consists in optimization of the manipulator measurement configuration used for the calibration experiments. The second method deals with proper tuning of the identification algorithms used for estimation of the manipulator parameters (fine choosing of the weighting coefficients, for instant). As follows from the literature analysis, the first method has been studied in a number of papers (Khalil 1991, Borm 1991, Daney 2002), while the second one received less attention in robotic research. For this reason, taking into account particularities of the measurement system used in our experiments, this paper focuses on the enhancement of the second method, which looks rather promising here.
To accomplish a surgical task, the MIS robot should manipulate its surgical instruments insert inside the patient’s body and rotating around a fixed point on the patient’s body. Meanwhile, there should no any collision between the instrument during surgery. It goes without saying that the manipulator of the robot should be designed with 6 DOFs For the purpose of reaching the specified spatial location. However, 2 DOFs have already been constrained by the abdominal wall, and the manipulator can complete the surgical procedure only with 4 DOFs. We have learned that the 4 DOFs should include three rotational DOFs and one translational DOF(i.e., a 3R1T motion where “R” denotes rotational DOF and “T” the translational DOF). For the reason of no collision, the three rotational DOFs should always pivot at the trocar point (i.e., insert point) on the patient’s body, while the translational DOF should move along the penetrating direction. An illustration for such a 4-DOF of motion for a MIS mechanism was described in Fig.1. As shown, the robot manipulator is inserted into patient’s body with a special trocar through a small incision. There are four DPFs including three rotational DOFs and one translational DOF. And, three rotational DOFs which described as pan, tilt and spin rotation rotate around a fixed point (i.e., trocar point in the figure). The translation DOF make the surgical tool achieve the function of insertion and retraction. It can be seen that surgical tool, with the help of four DOFs, can reach every point of the limited small spatial volume workplace in the patient’s body. A general multi-DOF robot can, of course, also realize this function through a perfect control strategy. However, it would make the mechanism become very heavy and complicated. So, a more simple mechanism which named remote center-of-motion (RCM) mechanism was devised and it can also accomplish these required motion depend on the structure constraint itself. In conclusion, a link that has a fixed center of rotation and locate outside the workspace of the mechanism can be called an RCM, and an RCM mechanism should own one RCM at least. For an RCM robot, it would be composed by one or more RCM mechanisms for generating RCMs.
The roboticmanipulator is a reprogrammable mechanical arm, moved or controlled by actuators to perform similar functions to human arm. It is a physical system with many subsystems such as the mechanical, electrical and electronics, etc. These subsystems are in most cases non-negligible in the mathematical description of the roboticmanipulator. If there is one technological advancement that would certainly make living easy and convenient, robots would be the answer. They have shown significance in decreasing human work load especially in industries by making works easy and convenient. Robots are mostly utilized in the manufacturing industry where they usually provide solutions to repetitive and monotonous works which are normally problems to human workers.
development of robotic applications, in industrial therefore dynamic control of robot manipulators is one of the most important and challenging fields of robotics, in the recent years using intelligence control such as fuzzy control, Neural Network, Neuro Fuzzy and because that they can control nonlinear systems that would be difficult or impossible to model mathematically. In the recently years In Dynamic control of robot have been utilized in many researchers work in this area. Such as Lianfang Tian et al use a neural network approach for the motioncontrol of constrained flexible manipulators robots . Yi , et al have investigated the robustness and stability of a fuzzy logic controller applied to a roboticmanipulator with uncertainties such as friction, unmodeled dynamics, and external disturbance etc , Kumbla et al have implemented hierarchical control on roboticmanipulator using fuzzy logic .
Predictive models are commonly used in model-based RL for the purpose of planning (Deisen- roth and Rasmussen, 2011; Finn and Levine, 2017; Nagabandi et al., 2018; Chua et al., 2018; Zhang et al., 2019) or generating cheap synthetic experience for RL to reduce the required amount of interaction with the real environment (Sutton, 1991; Gu et al., 2016). However, in this work, we are primarily concerned with their potential to alleviate the representation learning challenge in RL. We devise a stochastic predictive model by modeling the high-dimensional observations as the consequence of a latent process, with a Gaussian prior and latent dynamics, as illustrated in Figure 5.1. A model with an entirely stochastic latent state has the appealing interpretation of being able to properly represent uncertainty about any of the state variables, given its past obser- vations. We demonstrate in our work that fully stochastic state space models can in fact be learned effectively: With a well-designed stochastic network, such models outperform fully deterministic models, and contrary to the observations in prior work (Hafner et al., 2019; Buesing et al., 2018), are actually comparable to (if not better than) mixed deterministic/stochastic models. Finally, we note that this explicit representation learning, even on low-reward data, allows an agent with such a model to make progress on representation learning even before it makes progress on task learning. Equipped with this model, we can then perform RL in the learned latent space of the pre- dictive model. We posit—and confirm experimentally—that our latent variable model provides a useful representation for RL. Our model represents a partially observed Markov decision process (POMDP), and solving such a POMDP exactly would be computationally intractable (Astrom, 1965; Kaelbling et al., 1998; Igl et al., 2018). We instead propose a simple approximation that trains a Markovian critic on the (stochastic) latent state and trains an actor on a history of observa- tions and actions. The resulting stochastic latent actor-critic (SLAC) algorithm loses some of the benefits of full POMDP solvers, but it is easy and stable to train. It also produces good results, in practice, on a range of challenging problems, making it an appealing alternative to more complex POMDP solution methods.
The relevance of developing of navigation and motioncontrol systems of the underwater multiple robots consists in the following. In recent years, along with the use of large and expensive remote-controlled underwater robots, whose operation requires the availability of the special ship carriers with lifting devices, there is a global tendency of wide use of underwater mini autonomous vehicles intended for the search and inspection works at depths of up to 300 m. This tendency is accompanied by reduction in prices on vehicles as well as performance of deepwater operations in general. This increases the accessibility for both professionals and ordinary nonprofessional divers, who are fond of the underwater world. The function of underwater vehicles, such as the search and inspection of works at depths of up to 150 m, becomes relevant as well. In the 90-ies of the last century, a number of firms – JW Fishers Inc (USA), Hydrovision (UK), and Inuktun (Canada) began to design the so-called mini-ROVs (Remote Operated Vehicle) . Study of possibilities of using new technologies has shown that it is possible to create devices with even smaller dimensions (micro-ROV) and less power consumption, while maintaining the same operation speeds. Such devices can carry out the survey by going inside the various submerged objects.
The navigation tests showed that the robot was success- fully controlled in open field and a maze. When the robot was navigated in open field, following a perfectly straight line was impossible since the balloons had an unpredictable slight curvature because of their structure. This is also related to the manual control of the airflow rates to the balloons and heavily depends on the skills of the operator. If one or both of the balloons have a curvature, the deviation from a straight line is immediately observable. The operator, however, could easily correct this deviation by changing the inflation speeds of each balloon. Another problem that was encountered in the navigation tests that the radius of rotation was different when the robot was rotated due to the wrinkled structure of the outer sheath. A final remark is about shortening and re- elongation of the robot. Although elasticity of the balloons brings the tip of the robot back while the balloons are de- flated, the robot will not be able to return to its initial, the most compact form without some kind of mechanism to re- tract the slack outside of the tip.
In TableI above, which represents the set of possible paths of the mobile platform or the effector between the two Cartesian coordinates P1 (6, 5) and P2 (16,15). Between these two coordinates, there are several possible movements of the manipulator. For example, a simulation of the three movements of the mobile platform of the robot is shown below:
several disadvantages. One main drawback is that they require huge amounts of computational resources. This drawback is much obvious for an autonomous mobile robot that must carry its own computational resources. Secondly, this approach must be based on highly accurate model, thus it requires a number of high-precision sensors which are also often expensive. These sensors, however, are subject to noisy data. Finally, this sense-plan-act paradigm is by nature sequential, thus it would fail if the world happens to change in between of phases. Furthermore, there is always delay between sensing and act, due to longer time required in planning. As an alternative to the traditional approach, a new paradigm called subsumption architecture, also known as behaviour-based control, is devised . In this architecture, sensors are dealt with only implicitly in that they initiate behaviours. Each behaviour is simply layers of control systems that all run in parallel. Higher level behaviours have the power to temporarily suppress lower level behaviours. Therefore, a set of priority scheme is used to resolve the dominant behaviour for a given scenario. A more rigorous explanation of behaviour-based approach for controlling robot is presented in . In this work, inspired by the behaviour-based paradigm approach to robotics control, the searching algorithm employs Laplacian Behaviour-Based Control (LBBC) for robust space exploration of the configuration space. The LBBC comprises four core behaviours i.e. keep-forward, follow-wall, avoid-obstacle, and find-slope. All these core behaviours make use of the potential values represented by temperature distribution in the configuration space which are computed numerically to provide guidance during search exploration.
Abstract: In present work the various aspects on mathematical modelling, stability and control strategies of flexible double link manipulator have been investigated. A mathematical model of flexible double link manipulator has been developed using lagrangian method. This mathematical model has been characterized using classical and modern control theories. Their time domain and frequency domain analysis has been carried out and our study show that the mathematical model of flexible manipulator is highly unstable. Different control strategies such as PID, LQR and State feedback controller have been implemented for controlling the tip position of flexible double link manipulator using MATLAB programming. State feedback controller uses pole placement approach, while the linear quadratic regulator is obtained by resolving the Riccati equation. The best control strategy for controlling the tip position of flexible double link manipulator is obtained by implementation of LQR controller. Finally, this study proven that LQR control method is the best method as compare to PID and State feedback controller for controlling the flexible link roboticmanipulator.
Kinematics modeling is required for designing the motion and trajectory of the robot without considering the dynamics aspect such as force and friction. Kinematics modeling can be tested using simulation to avoid the complexity of the real system in testing the effectiveness of the proposed method. Simulation was conducted by Ceccarelli et al. 2008 utilizing the kinematic design for a manipulator by creating an algorithm for evaluating manipulator workspace , Lin et al. 2014 proposed an intuitive kinematic control of a robot via interface with human motion to control a robot directly teleoperated in avoiding obstacle and finishing its task , Reihara 2011 analyzed and solved the kinematics problem for an AdeptThree robot arm with the application of DH convention simulated in LabView , and Zodey et al. 2014 analyze the kinematics of a robotics gripper by simulating the capability of hand modeling, grasp definition, grasp modeling, grasp analysis and graphic to support the presentation .
In this research, there will be an evaluation of the 2-DOF robotic finger mechanism labelled as ‘B’ and illustrated in Figure 3. This paper only examines one finger to serve as an initial study towards the construction of a multi-fingered robotic hand. The mechanism of the 2-DOF robotic finger is made up of two joints and two links, where every actuator in the joint is fitted within the palm and robotic finger link. The 2-DOF robotic finger possesses two links, L1 and L2, which rotate because of the DC micro motor located at joints P and Q, respectively. L0 represents the displacement between the joint P and the robotic hand palm. Figure 3 shows the schematic design of the 2-DOF robotic finger that was formulated in this research. Each joint of the robotic finger is attached using shaft so that it can deliver motion for every link and smoothly move the 2-DOF robotic finger like a human hand. For this study, the end of the distal segment of fingertip is slightly curved to assist with object manipulation. By utilising the proposed mechanism, it was able to construct a multi-jointed robot finger. This 2-DOF robotic finger is advantageous because it is lightweight and compact. Table I illustrates in detail parameter design of single 2-DOF robotic finger mechanism.