Control of Redundant Robots Under Hard Joint Constraints: Saturation in the Null Space

We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed SNS (Saturation in the Null Space) algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution. When processing multiple tasks with priority, the SNS method realizes a preemptive strategy by preserving the correct order of priority in spite of the presence of saturations. In the single- and multi-task case, the algorithm automatically integrates a least possible task scaling procedure, when an original task is found to be unfeasible. The optimality properties of the SNS algorithm are analyzed by considering an associated Quadratic Programming problem. Its solution leads to a variant of the algorithm, which guarantees optimality also when the basic SNS algorithm does not. Numerically efficient versions of these algorithms are proposed. Their performance allows real-time control of robots executing many prioritized tasks with a large number of hard bounds. Experimental results are reported

Passive Compliance Control of Redundant Serial Manipulators

Current industrial robotic manipulators, and even state of the art robotic manipulators, are slower and less reliable than humans at executing constrained manipulation tasks, tasks where motion is constrained in some direction (e.g., opening a door, turning a crank, polishing a surface, or assembling parts). Many constrained manipulation tasks are still performed by people because robots do not have the manipulation ability to reliably interact with a stiff environment, for which even small commanded position error yields very high contact forces in the constrained directions. Contact forces can be regulated using compliance control, in which the multi-directional elastic behavior (force-displacement relationship) of the end-effector is controlled along with its position. Some state of the art manipulators can directly control the end-effector\u27s elastic behavior using kinematic redundancy (when the robot has more than the necessary number of joints to realize a desired end-effector position) and using variable stiffness actuators (actuators that adjust the physical joint stiffness in real time). Although redundant manipulators with variable stiffness actuators are capable of tracking a time-varying elastic behavior and position of the end-effector, no prior work addresses how to control the robot actuators to do so. This work frames this passive compliance control problem as a redundant inverse kinematics path planning problem extended to include compliance. The problem is to find a joint manipulation path (a continuous sequence of joint positions and joint compliances) to realize a task manipulation path (a continuous sequence of end-effector positions and compliances). This work resolves the joint manipulation path at two levels of quality: 1) instantaneously optimal and 2) globally optimal. An instantaneously optimal path is generated by integrating the optimal joint velocity (according to an instantaneous cost function) that yields the desired task velocity. A globally optimal path is obtained by deforming an instantaneously generated path into one that minimizes a global cost function (integral of the instantaneous cost function). This work shows the existence of multiple local minima of the global cost function and provides an algorithm for finding the global minimum

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

Continuous-time recurrent neural networks for quadratic programming: theory and engineering applications.

Liu Shubao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 90-98).Abstracts in English and Chinese.Abstract --- p.i摘要 --- p.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Time-Varying Quadratic Optimization --- p.1Chapter 1.2 --- Recurrent Neural Networks --- p.3Chapter 1.2.1 --- From Feedforward to Recurrent Networks --- p.3Chapter 1.2.2 --- Computational Power and Complexity --- p.6Chapter 1.2.3 --- Implementation Issues --- p.7Chapter 1.3 --- Thesis Organization --- p.9Chapter I --- Theory and Models --- p.11Chapter 2 --- Linearly Constrained QP --- p.13Chapter 2.1 --- Model Description --- p.14Chapter 2.2 --- Convergence Analysis --- p.17Chapter 3 --- Quadratically Constrained QP --- p.26Chapter 3.1 --- Problem Formulation --- p.26Chapter 3.2 --- Model Description --- p.27Chapter 3.2.1 --- Model 1 (Dual Model) --- p.28Chapter 3.2.2 --- Model 2 (Improved Dual Model) --- p.28Chapter II --- Engineering Applications --- p.29Chapter 4 --- KWTA Network Circuit Design --- p.31Chapter 4.1 --- Introduction --- p.31Chapter 4.2 --- Equivalent Reformulation --- p.32Chapter 4.3 --- KWTA Network Model --- p.36Chapter 4.4 --- Simulation Results --- p.40Chapter 4.5 --- Conclusions --- p.40Chapter 5 --- Dynamic Control of Manipulators --- p.43Chapter 5.1 --- Introduction --- p.43Chapter 5.2 --- Problem Formulation --- p.44Chapter 5.3 --- Simplified Dual Neural Network --- p.47Chapter 5.4 --- Simulation Results --- p.51Chapter 5.5 --- Concluding Remarks --- p.55Chapter 6 --- Robot Arm Obstacle Avoidance --- p.56Chapter 6.1 --- Introduction --- p.56Chapter 6.2 --- Obstacle Avoidance Scheme --- p.58Chapter 6.2.1 --- Equality Constrained Formulation --- p.58Chapter 6.2.2 --- Inequality Constrained Formulation --- p.60Chapter 6.3 --- Simplified Dual Neural Network Model --- p.64Chapter 6.3.1 --- Existing Approaches --- p.64Chapter 6.3.2 --- Model Derivation --- p.65Chapter 6.3.3 --- Convergence Analysis --- p.67Chapter 6.3.4 --- Model Comparision --- p.69Chapter 6.4 --- Simulation Results --- p.70Chapter 6.5 --- Concluding Remarks --- p.71Chapter 7 --- Multiuser Detection --- p.77Chapter 7.1 --- Introduction --- p.77Chapter 7.2 --- Problem Formulation --- p.78Chapter 7.3 --- Neural Network Architecture --- p.82Chapter 7.4 --- Simulation Results --- p.84Chapter 8 --- Conclusions and Future Works --- p.88Chapter 8.1 --- Concluding Remarks --- p.88Chapter 8.2 --- Future Prospects --- p.88Bibliography --- p.8

DETC2004-57447 MANIPULATOR TASK-BASED PERFORMANCE OPTIMIZATION

ABSTRACT This research uses new developments in redundancy resolution and real-time capability analysis to improve the ability of an articulated arm to satisfy task constraints. Task constraints are specified using numerical values of position, velocity, force, and accuracy. Inherent in the definition of task constraints is the number of output constraints that the system needs to satisfy. The relationship of this with the input space (degrees of freedom) defines the ability to optimize manipulator performance. This is done through a Task-Based Redundancy Resolution (TBRR) scheme that uses the extra resources to find a solution that avoids system constraints (joint limits, singularities, etc.) and satisfies task constraints. To avoid system constraints, we use well-understood criteria associated with the constraints. For task requirements, the robot capabilities are estimated based on kinematic and dynamic manipulability analyses. We then compare the robot capabilities with the userspecified requirement values. This eliminates a confusing chore of selecting a proper set of performance criteria for a task at hand. The breakthrough of this approach lies in the fact that it continuously evaluates the relationship between task constraints and system resources, and when possible, improves system performance. This makes it equally applicable to redundant and non-redundant systems. The scheme is implemented using an object-oriented operational software framework and its effectiveness is demonstrated in computer simulations of a 10-DOF manipulator

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Multi-Point Impedance Control for Redundant Manipulators

The present paper proposes an impedance controlmethod called the Multi-Point Impedance Control (MPIC) forredundant manipulators. The method can not only control endeffectorimpedance,but also regulate impedances of several pointson the links of the manipulator, which are called virtual endpointimpedances, utilizing arm redundancy. Two approachesfor realizing the MPIC are presented. In the first approach,controlling the end-effector impedance and the virtual end-pointimpedances are considered as the tasks with the same level, andthe joint control law developed in this approach can realize theclosest impedances of the multiple points, including the endeffectorand the virtual end-points to the desired ones in theleast squared sense. On the other hand, in the second approach,controlling the end-effector impedance is considered the mostimportant task, and regulating the impedances of the virtual endpointsis considered as a sub-task. Under the second approach,the desired end-effector impedance can be always realized sincethe joint control torque for the regulation of the virtual end-pointimpedances is designed in such a way that it has no effect on theend-effector motion of the manipulator. Simulation experimentsare performed to confirm the validity and to show the advantagesof the proposed method

Synthesis of continuous whole-body motion in hexapod robot for humanitarian demining

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn the context of control, the motion of a legged robot is very challenging compared with traditional fixed manipulator. Recently, many researches have been conducted to control the motion of legged robot with different techniques. On the other hand, manipulation tasks have been addressed in many applications. These researches solved either the mobility or the manipulation problems, but integrating both properties in one system is still not available. In this thesis, a control algorithm is presented to control both locomotion and manipulation in a six legged robot. Landmines detection process is considered as a case study of this project to accelerate the mine detection operation by performing both walking and scanning simultaneously. In order to qualify the robot to perform more tasks in addition to the walking task, the joint redundancy of the robot is exploited optimally. The tasks are arranged according to their importance to high level of priority and low level of priority. A new task priority redundancy resolution technique is developed to overcome the effect of the algorithmic singularities and the kinematic singularity. The computational aspects of the solution are also considered in view of a real-time implementation. Due to the dynamic changes in the size of the robot motion space, the algorithm has the ability to make a trade-off between the number of achieved tasks and the imposed constraints. Furthermore, an appropriate hierarchy is imposed in order to ensure an accurate decoupling between the executed tasks. The dynamic effect of the arm on the overall performance of the robot is attenuated by reducing the optimisation variables. The effectiveness of the method is evaluated on a Computer Aided Design (CAD) model and the simulations of the whole operation are conducted using MATLAB and SimMechanics.Iraqi ministry of Higher Education and Scientific Researc

Instantaneous Momentum-Based Control of Floating Base Systems

In the last two decades a growing number of robotic applications such as autonomous drones, wheeled robots and industrial manipulators started to be employed in several human environments. However, these machines often possess limited locomotion and/or manipulation capabilities, thus reducing the number of achievable tasks and increasing the complexity of robot-environment interaction. Augmenting robots locomotion and manipulation abilities is a fundamental research topic, with a view to enhance robots participation in complex tasks involving safe interaction and cooperation with humans. To this purpose, humanoid robots, aerial manipulators and the novel design of flying humanoid robots are among the most promising platforms researchers are studying in the attempt to remove the existing technological barriers. These robots are often modeled as floating base systems, and have lost the assumption -- typical of fixed base robots -- of having one link always attached to the ground. From the robot control side, contact forces regulation revealed to be fundamental for the execution of interaction tasks. Contact forces can be influenced by directly controlling the robot's momentum rate of change, and this fact gives rise to several momentum-based control strategies. Nevertheless, effective design of force and torque controllers still remains a complex challenge. The variability of sensor load during interaction, the inaccuracy of the force/torque sensing technology and the inherent nonlinearities of robot models are only a few complexities impairing efficient robot force control. This research project focuses on the design of balancing and flight controllers for floating base robots interacting with the surrounding environment. More specifically, the research is built upon the state-of-the-art of momentum-based controllers and applied to three robotic platforms: the humanoid robot iCub, the aerial manipulator OTHex and the jet-powered humanoid robot iRonCub. The project enforces the existing literature with both theoretical and experimental results, aimed at achieving high robot performances and improved stability and robustness, in presence of different physical robot-environment interactions

Neural-Dynamic Based Synchronous-Optimization Scheme of Dual Redundant Robot Manipulators

In order to track complex-path tasks in three dimensional space without joint-drifts, a neural-dynamic based synchronous-optimization (NDSO) scheme of dual redundant robot manipulators is proposed and developed. To do so, an acceleration-level repetitive motion planning optimization criterion is derived by the neural-dynamic method twice. Position and velocity feedbacks are taken into account to decrease the errors. Considering the joint-angle, joint-velocity, and joint-acceleration limits, the redundancy resolution problem of the left and right arms are formulated as two quadratic programming problems subject to equality constraints and three bound constraints. The two quadratic programming schemes of the left and right arms are then integrated into a standard quadratic programming problem constrained by an equality constraint and a bound constraint. As a real-time solver, a linear variational inequalities-based primal-dual neural network (LVI-PDNN) is used to solve the quadratic programming problem. Finally, the simulation section contains experiments of the execution of three complex tasks including a couple task, the comparison with pseudo-inverse method and robustness verification. Simulation results verify the efficacy and accuracy of the proposed NDSO scheme