Dynamic Neural Networks for Motion-Force Control of Redundant Manipulators: An Optimization Perspective

Abstract

Accurate position-force control is a core and challenging problem in robotics, especially for manipulators with redundant DOFs. For example, trajectory tracking based control usually fails for grinding robots due to intolerable impact forces imposed onto the end-effectors. The main difficulties lie in the coupling of motion and contact force, redundancy resolution and physical constraints, etc. In this paper, we propose a novel motionforce control strategy in the framework of projection recurrent neural networks. Tracking error and contact force are described in orthogonal spaces respectively, and by selecting minimizing joint torque as secondary task, the control problem is formulated as a quadratic-programming (QP) problem under multiple constraints. In order to obtain real-time optimization of joint toque which is non-convex relative to joint angles, the original QP is reconstructed in velocity level, where the original objective function is replaced by its time derivative. Then a dynamic neural network which is convergence provable is established to solve the modified QP problem online. This work generalizes projection recurrent neural network based position control of manipulators to that of position-force control, which opens a new avenue to shift position-force control of manipulators from pure control perspective to cross design with both convergence and optimality consideration. Numerical and experimental results show that the proposed scheme achieves accurate position-force control, and is capable of handling inequality constraints such as joint angular, velocity and torque limitations, simultaneously, consumption of joint torque can be decreased effectively.

Nonparametric Bayesian Method for Robot Anomaly Monitoring

“…This method combines the Fisher Kernel into the OCSVM (One-Class Support Vector Machine) model, which has the advantages of generating models and discriminative models. Dilello et al [15,16] first proposed the application of the nonparameterized Bayesian time series model (sHDP-HMM) for abnormal monitoring and fault diagnoses during robot operation, using the model to transmit force/torque of the robot to perform normal alignment tasks [17,18]. Sensing data is used for modeling, and the threshold value for abnormality monitoring is calculated from the validation data set.…”

Incremental Learning Robot Task Representation and Identification

“…Therefore, how to correctly select the next motion primitive after the execution of the current motion primitive is another research difficulty of the complex task description of the robot. In response to the serialization of motion primitives, Pastor et al [27] proposed a motion description based on the Associative Skill Memories (ASM) of DMP motion primitives for motion selection, which differ from the control scheme [31]. Statistical data such as mean and variance are saved at all times.…”

Introduction to Robot Introspection

“…Then, it formulates a task execution plan, and finally executes this plan. However, the control framework is difficult to meet the increasingly complex robot operation tasks in unstructured dynamic environments [20,23]. In the recent decades, increasing the robot multi-modal introspection and anomaly recovery policy after the robot execution would be an effective way to address the aforementioned issues.…”