Abstract
Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.
Highlights
In special manufacturing, troubleshooting, and other fields, there will inevitably be a closed and narrow operating environment, which is high risk and inefficient for manual operation
Redundant manipulators can be implemented in different ways, such as the wheeled mobile manipulator used for part handling [2, 3], the hyperredundant manipulator used for engine manufacturing [4] or spacecraft maintenance [5], and the flexible bionic manipulator inspired by the octopus claw [6] or the elephant trunk [7]
The RGB-D camera was connected to the industrial personal computer (IPC) through USB, and the MARG sensor and the joint encoders were connected to the sensor collector through the 485 bus and connected to the IPC through USB
Summary
In special manufacturing, troubleshooting, and other fields, there will inevitably be a closed and narrow operating environment, which is high risk and inefficient for manual operation. For one thing, adding angle sensors at joints and fusing with motor encoders can correct transmission errors and improve pose estimation accuracy [13]. Adding one or more eye-to-hand cameras to the environment and performing the visual measurement on redundant manipulators can correct deformation errors [14] and is an effective method for flexible robot pose estimation [15]. The sensor processing methods mentioned above can solve the problem of end-effector pose estimation, but there are many unsatisfactory shortcomings, especially when applied in complex environments. In terms of multisensor fusion, visual-inertial fusion has become an effective method of mobile robot navigation [19], it is not common in end-effector pose estimation of redundant robots, and it is mostly the fusion of eye-to-hand camera and kinematic sensors [20].
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