Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks

Abstract

In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method with Kinect sensor. To comply with the external torque from the environment, this paper presents a sensorless admittance control approach in joint space based on an observer approach, which is used to estimate external torques applied by the operator. To deal with the tracking problem of the uncertain manipulator, an adaptive controller combined with the radial basis function NN (RBFNN) is designed. The RBFNN is used to compensate for uncertainties in the system. In order to achieve the prescribed tracking precision, an error transformation algorithm is integrated into the controller. The Lyapunov functions are used to analyze the stability of the control system. The experiments on the Baxter robot are carried out to demonstrate the effectiveness and correctness of the proposed control scheme.

Unsupervised Recurrent Neural Network with Parametric Bias Framework for Human Emotion Recognition with Multimodal Sensor Data Fusion

“…Using the law of cosines, the angle between two vectors can be calculated easily. (21) Similarly, the angle between two joints can be obtained by applying the same method. In the Kinect coordination, a joint can be regarded as a vector.…”

“…According to Yang et al, if the relevant joint angles are zero, any position of the human arm can be regarded as the initial position. (21) When the human arm is moved to a new position U, the corresponding angle from the initial position to pose U is the rotation angle, namely, the joint angle.…”

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

“…Industries are interested in using the manipulators to automate the common tasks, e.g., moving, assembling, packing, and transporting the products. Accurate tracking control, along with obstacle avoidance, is a critical requirement for the industrial manipulators [5], [6]. To fulfill those requirements, redundant manipulators [7] are particularly desirable because the extra degree of freedoms (DOFs) provided by redundant joints helps in achieving secondary design objectives, such as obstacle avoidance [8]- [10].…”

Asymmetric Bimanual Control of Dual-arm Serial Manipulator for Robot-assisted Minimally Invasive Surgeries

“…Their redundancy, such as more DoFs than required for the executed task, provides benefits over nonredundant robots, including the facilitation of human-like behavior, (2,4) enhanced manipulability, (3) and obstacle avoidance. (5,6) In robot-assisted MIS, the motion of the surgical tool is constrained to a point, referred to as the remote center of motion (RCM), where the surgical tool crosses the abdominal wall, allowing only translational movements around its axis. Whereas a mechanical implementation is generally considered safer but requires bulky, expensive structures and calibration procedures, a programmable RCM whose movement is restricted by the control algorithm is cheaper and more flexible and is therefore the preferable option.…”