Sensor Fusion for Joint Kinematic Estimation in Serial Robots Using Encoder, Accelerometer and Gyroscope

Abstract

Open-chain manipulator robots play an important role in the industry, since they are utilized in applications requiring precise motion. Highperformance motion of a robot system mainly relies on adequate trajectory planning and the controller that coordinates the movement. The controller performance depends of both, the employed control law and the sensor feedback. Optical encoder feedback is the most used sensor for angular position estimation of each joint in the robot, since they feature accurate and low noise angular position measurements. However, it cannot detect mechanical imperfections and deformations common in open chain robots. Moreover, velocity and acceleration cannot be extracted from the encoder data without adding phase delays. Sensor fusion techniques are found to be a good solution for solving this problem. However, few works has been carried out in serial robots for kinematic estimation of angular position, velocity and acceleration, since the delays induced by the filtering techniques avoids its use as controller feedback. This work proposes a novel sensor-fusion-based feedback system capable of providing complete kinematic information from each joint in 4-degrees of freedom serial robot, with the contribution of a proposed methodology based on Kalman filtering for fusing the information from optical encoder, gyroscope and accelerometer appended to the robot. Calibration and experimentation are carried out for validating the proposal. The results are compared with another kinematic estimation technique finding that this proposal provides more information about the robot movement without adding state delays, which is important for being used as controller feedback.

Rejecting the effects of both input disturbance and measurement noise: A second‐order control system example

“…Due to the aforementioned inherent contradiction for generic systems, we narrow down our concern to the output tracking for a class of second-order systems in the presence of model uncertainty, input disturbance and measurement noise. Note the well-known facts: MEMS inertial measurement units (IMU) and encoders (see Figure 2) are widely used in motion control systems (for instance, the inverted pendulum systems, 17,18 various robotic manipulators, 19 and servomotors 20 ) to provide position and velocity measurements; and the position measurements from low-cost encoders are relatively accurate whereas the velocity measurements from MEMS IMUs inevitably suffer from high-frequency noise and time-varying drift. Thus, it is practically reasonable to assume that the measurement noises of linear/angular velocity sensors are nonnegligible whereas those of linear/angular position sensors are negligible.…”

“…In all cases, the identical feedback gains k p = 5 and k d = 3 are applied. The filter parameter T f for the classic UDE (17) is chosen to be the same as the proposed UDE (20). Three full-state feedback controllers are compared with the proposed controller in Case 1, while controller (63) is no longer considered in Case 2 because the Kalman filter rejects the noise in the measurement y 2 and the noise compensation is not needed.…”

“…Among all the controllers, the proposed NE+UDE-based control delivers the best tracking accuracy. Since this control is only different from (63) in the UDE design, we should give the credit to the proposed UDE (20), which systematically addresses the measurement noise. This result also shows that the noise rejection in the UDE plays the dominant role in the overall tracking performance.…”

Vibration Suppression for Improving the Estimation of Kinematic Parameters on Industrial Robots

“…The solutions mentioned so far use only one sensor. On the other hand, sensor fusion is being used to obtain more accurate results because it tries to compensate for the problems and mistakes from a single sensor with the information provided by different sensors [2,[9][10][11]. Although all these techniques yield favorable results, they still can be improved.…”

“…Current researches reported in literature point out that the encoder in servomotors and the accelerometer are two of the most widely used sensors for monitoring motion dynamics and vibrations on computerized numeric control (CNC) machines and robotic manipulator arms [9]. The accelerometer is used to measure the inclination or angular position of several CNC and robotics systems [2,[9][10][11][12][13]. Nevertheless, accelerometers have been also used for monitoring vibrations in many structures like bridges [14], washing machines [15], CNC machines [16], and industrial robots [9].…”