Forward and Inverse Kinematics Solution of A 3-DOF Articulated Robotic Manipulator Using Artificial Neural Network

In this research paper, the multilayer feedforward neural network (MLFFNN) is architected and described for solving the forward and inverse kinematics of the 3-DOF articulated robot. When designing the MLFFNN network for forward kinematics, the joints' variables are used as inputs to the network, and the positions and orientations of the robot end-effector are used as outputs. In the case of inverse kinematics, the MLFFNN network is designed using only the positions of the robot end-effector as the inputs, whereas the joints’ variables are the outputs. For both cases, the training of the proposed multilayer network is accomplished by Levenberg Marquardt (LM) method. A sinusoidal type of motion using variable frequencies is commanded to the three joints of the articulated manipulator, and then the data is collected for the training, testing, and validation processes. The experimental simulation results demonstrate that the proposed artificial neural network that is inspired by biological processes is trained very effectively, as indicated by the calculated mean squared error (MSE), which is approximately equal to zero. The resulted in smallest MSE in the case of the forward kinematics is 4.592×10^(-8) in the case of the inverse kinematics, is 9.071×10^(-7). This proves that the proposed MLFFNN artificial network is highly reliable and robust in minimizing error. The proposed method is applied to a 3-DOF manipulator and could be used in more complex types of robots like 6-DOF or 7-DOF robots

Distance Estimation on Ultrasonic Sensor Using Kalman Filter

This research discusses about the distance estimation on ultrasonic sensor using Kalman Filter method. Accuracy level problem on ultrasonic sensor will be increased using Kalman Filter. Kalman Filter consists of two parts which are prediction and update. This research applies Kalman Filter method using Arduino Uno and Ultrasonic sensor HC-SR04. The test result compares the sensor data before and after Kalman Filter is applied. The test result of sensor value after given Kalman Filter depends on the value of noise sensor covariance matrix (R) and process noise covariance (Q). The best value of R and Q is 100 and 0.01. If the distance value between R and Q is too small, the filtering result will be invisible. In contrast, if the distance value between R and Q is too far, the filtering result could remove the original measured sensor data. In conclusion, applying Kalman Filter method in Ultrasonic sensor could estimate and increase the accuracy of sensor value up to 7%

Effect of Joints’ Configuration Change on the Effective Mass of the Robot

Effective mass of robot is considered of great significance in enhancing the safety of human-robot collaboration. In this paper, the effective mass of the robot is investigated using different joint configurations. This investigation is executed in two steps. In the first step, the position of each joint of the robot is changing alone, whereas the positions of the other joints of the robot are fixed and then the effective mass is determined. In the second step, the positions of all joints of the robot are changing together, and the effective mass of the robot is determined. From this process, the relation between the effective mass of the robot and the joint configurations can be presented. This analysis is implemented in MATLAB and using two collaborative robots; the first one is UR10e robot which is a 6-DOF robot and the second one is KUKA LBR iiwa 7 R800 robot which is a 7-DOF robot. The results from this simulation prove that the change in any joint position of the robot except the first and the last joint affect the effective mass of the robot. In addition, the change in all joints’ positions of the robot affect the effective mass. Effective mass can thus be considered as one of the criteria in optimizing the robot kinematics and configuration

Principle of Neural Network and Its Main Types: Review

International audienceIn this paper, an overview of the artificial neural networks is presented. Their main and popular types such asthe multilayer feedforward neural network (MLFFNN), the recurrent neural network (RNN), and the radial basis function(RBF) are investigated. Furthermore, the main advantages and disadvantages of each type are included as well as thetraining process

Forward and inverse kinematics solution of a robotic manipulator using a multilayer feedforward neural network

In this paper, a multilayer feedforward neural network (MLFFNN) is proposed for solving the problem of the forward and inverse kinematics of a robotic manipulator. For the forward kinematics solution, two cases are presented. The first case is that one MLFFNN is designed and trained to find solely the position of the robot end-effector. In the second case, another MLFFNN is designed and trained to find both the position and the orientation of the robot end-effector. Both MLFFNNs are designed considering the joints’ positions as the inputs. For the inverse kinematics solution, a MLFFNN is designed and trained to find the joints’ positions considering the position and the orientation of the robot end-effector as the inputs. For training any of the proposed MLFFNNs, data is generated in MATLAB using two different cases. The first case is that data is generated assuming an incremental motion of the robot’s joints, whereas the second case is that data is obtained with a real robot considering a sinusoidal joint motion. The MLFFNN training is executed using the Levenberg-Marquardt algorithm. This method is designed to be used and generalized to any DOF manipulator, particularly more complex robots such as 6-DOF and 7-DOF robots. However, for simplicity, this is applied in this paper using a 2-DOF planar robot. The results show that the approximation error between the desired output and the estimated one by the MLFFNN is very low and it is approximately equal to zero. In other words, the MLFFNN is efficient enough to solve the problem of the forward and inverse kinematics, regardless of the joint motion type

Neural Networks for Robot Collision Estimation and Detection

International audienceThis paper presents a mini-review on our previous work presented in ref. [1-6] in which the neural networks (NNs) were used for estimating and then detecting the robot's collisions with the human operator during the cooperation task. This review investigates and compares the designed NN architectures, their application, the resulted mean squared error (MSE) from training, and their effectiveness (%) in detecting the robot's collisions. This review reveals that the NN is an effective method in estimating and detecting the human-robot collisions

Human–Robot Interaction: A Review and Analysis on Variable Admittance Control, Safety, and Perspectives

Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first part of this paper reviews the variable admittance (VA) control for human–robot co-manipulation tasks, where the virtual damping, inertia, or both are adjusted. An overview of the published research for the VA control approaches, their methods, the accomplished collaborative co-manipulation tasks and applications, and the criteria for evaluating them are presented and compared. Then, the performance of various VA controllers is compared and investigated. In the second part, the safety of HRI systems is discussed. The various methods for detection of human–robot collisions (model-based and data-based) are investigated and compared. Furthermore, the criteria, the main aspects, and the requirements for the determination of the collision and their thresholds are discussed. The performance measure and the effectiveness of each method are analyzed and compared. The third and final part of the paper discusses the perspectives, necessity, influences, and expectations of the HRI for future robotic systems

NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor

Background: Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods: In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator’s joints and considers only the signals of the position sensors that are intrinsic to the manipulator’s joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. Results: The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. Conclusions: Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work