AI based Robot Safe Learning and Control

Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities

Neural Network Model-Based Control for Manipulator: An Autoencoder Perspective

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as a powerful tool to achieve deep learning and has gained success in recent years. However, the performance of existing autoencoder approaches for manipulator control may be still largely dependent on the quality of data, and for extreme cases with noisy data it may even fail. How to incorporate the model knowledge into the autoencoder controller design with an aim to increase the robustness and reliability remains a challenging problem. In this work, a sparse autoencoder controller for kinematic control of manipulators with weights obtained directly from the robot model rather than training data is proposed for the first time. By encoding and decoding the control target though a new dynamic recurrent neural network architecture, the control input can be solved through a new sparse optimization formulation. In this work, input saturation, which holds for almost all practical systems but usually is ignored for analysis simplicity, is also considered in the controller construction. Theoretical analysis and extensive simulations demonstrate that the proposed sparse autoencoder controller with input saturation can make the end-effector of the manipulator system track the desired path efficiently. Further performance comparison and evaluation against the additive noise and parameter uncertainty substantiate robustness of the proposed sparse autoencoder manipulator controller

Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints

Redundancy resolution is a critical issue to achieve accurate kinematic control for manipulators. End-effectors of manipulators can track desired paths well with suitable resolved joint variables. In some manipulation applications such as selecting insertion paths to thrill through a set of points, it requires the distal link of a manipulator to translate along such fixed point and then perform manipulation tasks. The point is known as remote centre of motion (RCM) to constrain motion planning and kinematic control of manipulators. Together with its end-effector finishing path tracking tasks, the redundancy resolution of a manipulators has to maintain RCM to produce reliable resolved joint angles. However, current existing redundancy resolution schemes on manipulators based on recurrent neural networks (RNNs) mainly are focusing on unrestricted motion without RCM constraints considered. In this paper, an RNN-based approach is proposed to solve the redundancy resolution issue with RCM constraints, developing a new general dynamic optimisation formulation containing the RCM constraints. Theoretical analysis shows the theoretical derivation and convergence of the proposed RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on an industrial redundant manipulator system

Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints

Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy

Recursive recurrent neural network: A novel model for manipulator control with different levels of physical constraints

Manipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators. Due to physical limitations and actuation saturation of manipulator joints, the involvement of joint constraints for kinematic control of manipulators is essential and critical. However, current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints, and to the best of our knowledge, methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet reported. In this study, for the first time, a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints, and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different orders. The theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to solution. Simulation results further demonstrate the effectiveness of the proposed method in end-effector path tracking control under different levels of joint constraints based on the Kuka manipulator system. Comparisons with other methods such as the pseudoinverse-based method and conventional recurrent neural network method substantiate the superiority of the proposed method

An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators

For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control process. In order to improve sparsity of joint actuation for manipulator systems, a novel motion planning scheme which can optimally and sparsely adopt joint actuation is proposed in this paper. The proposed motion planning strategy is formulated as a constrained L1 norm optimization problem, and an equivalent enhanced optimization solution dealing with bounded joint velocity is proposed as well. A new primal dual neural network with a new solution set division is further proposed and applied to solve such bounded optimization which can sparsely adopt joint actuation for motion control. Simulation and experiment results demonstrate the efficiency, accuracy and superiority of the proposed method for optimally and sparsely adopting joint actuation. The average sparsity (i.e., -||˙θ||p where θ denotes the joint angle) of the joint motion of the manipulator can be increased by 39.22% and 51.30% for path tracking tasks in X-Y and X-Z planes respectively, indicating that the sparsity of joint actuation can be enhanced

Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

Redundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator

Control Techniques for Robot Manipulator Systems with Modeling Uncertainties

This dissertation describes the design and implementation of various nonlinear control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Chapter 2 describes the development of an adaptive task-space tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Experimental results for a planar application of the Barrett whole arm manipulator (WAM) are provided to illustrate the performance of the developed adaptive controller. The controller developed in Chapter 2 requires the assumption that the manipulator models are linearly parameterizable. However there might be scenarios where the structure of the manipulator dynamic model itself is unknown due to difficulty in modeling. One such example is the continuum or hyper-redundant robot manipulator. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In Chapter 3, a joint level controller for continuum robots is described which utilizes a neural network feedforward component to compensate for dynamic uncertainties. Experimental results are provided to illustrate that the addition of the neural network feedforward component to the controller provides improved tracking performance. While Chapter\u27s 2 and 3 described two different joint controllers for robot manipulators, in Chapter 4 a controller is developed for the specific task of whole arm grasping using a kinematically redundant robot manipulator. The whole arm grasping control problem is broken down into two steps; first, a kinematic level path planner is designed which facilitates the encoding of both the end-effector position as well as the manipulators self-motion positioning information as a desired trajectory for the manipulator joints. Then, the controller described in Chapter 3, which provides asymptotic tracking of the encoded desired joint trajectory in the presence of dynamic uncertainties is utilized. Experimental results using the Barrett Whole Arm Manipulator are presented to demonstrate the validity of the approach

Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated

In this article, three acceleration-level joint-drift-free (ALJDF) schemes for kinematic control of redundant manipulators are proposed and analyzed from perspectives of dynamics and kinematics with the corresponding tracking error analyses. First, the existing ALJDF schemes for kinematic control of redundant manipulators are systematized into a generalized acceleration-level joint-drift-free scheme with a paradox pointing out the theoretical existence of the velocity error related to joint drift. Second, to remedy the deficiency of the existing solutions, a novel acceleration-level joint-drift-free (NALJDF) scheme is proposed to decouple Cartesian space error from joint space with the tracking error theoretically eliminated. Third, in consideration of the uncertainty at the dynamics level, a multi-index optimization acceleration-level joint-drift-free scheme is presented to reveal the influence of dynamics factors on the redundant manipulator control. Afterwards, theoretical analyses are provided to prove the stability and feasibility of the corresponding dynamic neural network with the tracking error deduced. Then, computer simulations, performance comparisons, and physical experiments on different redundant manipulators synthesized by the proposed schemes are conducted to demonstrate the high performance and superiority of the NALJDF scheme and the influence of dynamics parameters on robot control. This work is of great significance to enhance the product quality and production efficiency in industrial production