Modeling, Analysis, and Experiments on a Robot Arm with Force-Feedback Interaction Control

MODELING, ANALYSIS, AND EXPERIMENTS ON A ROBOT ARM WITH FORCE-FEEDBACK INTERACTION CONTROL by SURAG BALAJEPALLI Submitted in partial fulfillment of the requirements For the degree of Master of Science Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY May, 2020 Modeling, Analysis, And Experiments On A Robot Arm With Force-Feedback Interaction Control Case Western Reserve University Case School of Graduate Studies We hereby approve the thesis1 of SURAG BALAJEPALLI for the degree of Master of Science Dr. Wyatt Newman Committee Chair, Adviser 03/05/2020 Electrical Engineering and Computer Science Dr. Cenk Cavusoglu Committee Member 03/05/2020 Electrical Engineering and Computer Science Dr. Gregory Lee Committee Member 03/05/2020 Electrical Engineering and Computer Science 1We certify that written approval has been obtained for any proprietary material contained therein. Table of Contents List of Tables v List of Figures vi Abstract ix Chapter 1. Introduction1 Robot Force Control2 Compliant Motion6 Compliant motion behaviors8 Scope of this thesis9 Chapter 2. Stability of Interaction Controllers 12 Performance measure 14 Passivity 15 Robot modeling 21 Controller model 32 Passivity analysis 37 Simulation 39 Chapter 3. Passivity Analysis 40 Robot Model 40 Single link analysis 42 Two link model analysis 46 Coulomb friction analysis 63 iii Chapter 4. Experiments on compliant behaviors 75 The robot 75 Virtual attractor strategies 84 Chapter 5. Conclusions and future work 92 Future work 93 Appendix. Complete References 95 iv List of Tables 3.1 Model arm dynamics 41 4.1 EGM system parameters 79 v List of Figures 1.1 Position/Force hybrid controller4 2.1 Lumped mass approximation of a robot link 21 2.2 The interaction controller 32 2.3 The interaction controller with modeled latency 34 2.4 The interaction controller with modeled latency implemented on a robot 35 3.1 Elbow link frequency response plots - no delay case 44 3.2 Shoulderlink frequency response plots - no delay case 44 3.3 Elbow link frequency response plots - with 10ms delay 45 3.4 Shoulderlink frequency response plots - with 10ms delay 45 3.5 Shoulder link frequency response plots - Bandwidth = 7Hz, delay = 10ms. The plot is in red for frequencies where the real part of the eigenvalues are negative. 46 3.6 Two link model violating passivity with 10ms controller latency. Negative values for the real part of the Eigenvalues of admittance are plotted in red. 48 3.7 Violation of passivity of the same robot and controller at different poses. Left plot: Joint 1 = 0, joint 2 = 90.± Right plot: Joint 1 = 0, joint 2 = 45.± Sections of this plot marked in red represent non-passive regions. 48 3.8 Effect of non-diagonal Mdes on passivity 52 vi 3.9 Effect of diagonal Mdes on passivity 53 3.10 Effect of desired stiffness on passivity 54 3.11 Effect of target damping on passivity 56 3.12 Effect of servo position bandwidth on passivity 58 3.13 Effect of servo velocity gain on passivity 59 3.14 Real part of admittance is positive over the chosen range of frequencies, the system is passive 61 3.15 Stable system simulation 62 3.16 Real part of admittance is negative for certain frequencies in the range, the system is non-passive 63 3.17 Unstable system simulation 64 3.18 Example of hunting due to Coulomb friction 67 3.19 Frequency domain passivity plot - Case 1 68 3.20 Force plot - Case 1 - Significant hunting 69 3.21 Frequency domain passivity plot - Case 2 70 3.22 Force plot - Case 2 - Significant hunting 70 3.23 Frequency domain passivity plot - Case 3 71 3.24 Force plot - Case 3 - No hunting 72 3.25 Friction suppression 74 4.1 EGM interface data flow 77 4.2 EGM interface block diagram 78 vii 4.3 Components of the EGM message protocol 81 4.4 Joint 5 following sinusoidal command with latency 83 4.5 Attractor strategy 1 for move until touch 86 4.6 Strategy 1 implementation 86 4.7 Attractor strategy 2 for move until touch 88 4.8 Strategy 2 implementation 89 4.9 Attractor strategy 3 for move until touch 90 4.10 Strategy 3 implementation 91 viii Abstract Modeling, Analysis, And Experiments On A Robot Arm With Force-Feedback Interaction Control Abstract by SURAG BALAJEPALLI Stable force feedback control of robot arms has the potential to improve the utility of robotic systems by equipping them with the ability to perform complex contact tasks like machining and assembly. This study explores the stability limitations of force feedback control on a robot arm for applications in remote supervisory control. Supervisory control is useful in situations where communication between a human op- erator and a robot suffers from large delays, making direct teleoperation impractical. It sets up a foundation of stable compliant behaviors specified using virtual attractors upon which algorithms to perform complex tasks can be developed. Influence of linear and nonlinear internal dynamics of a robot arm on efficacy of active compliance is studied. Additionally, it has been shown that force feedback can be effective in suppressing unwanted effects of nonlinear friction in the robot. Results have been validated experi- mentally by implementing force feedback control on an ABB IRB 120 robot. ix 1 1 Introduction This study is motivated by the requirements of typical supervisory control of contact tasks using robot arms. Potential applications of this robotic system include robot arms performing assembly and maintenance operations in space, underwater, or other remote locations that could be dangerous for humans to operate in. Remote supervisory control of a robot arm presents two major challenges. The first is that any communication to the robot must pass through channels that suffer from large delays as information needs to be transmitted over large distances through various media. Sec- ondly, the manipulation tasks that the robotic arm system aims to perform require robust force control functionality. Operations involving robot force control include cut- ting, insertion, grasping, and manipulation of environments with uncertainty in geom- etry. These form a large subset of contact operations that are expected of a robot arm. Introduction 2 1.1 Robot Force Control Control of robotic arms can be broadly classified into position control and force control. A position controller for a multiple link robot effectively controls the position of the end-effector in six degrees of freedom, three translational and three rota- tional, by controlling the position of each joint’s actuator. Decades of research in position controllers for robot arms has resulted in exceptional performance in terms of accuracy, repeatability, and speed. These position-controlled robots have been able to excel at a large class of operations like welding, painting, and palletization. These include operations involving grasping of objects too, however, position controllers’ effectiveness in such tasks is limited by the quality of information it receives from any sensors. For example, to successfully grasp an object, the robot must know the object’s position in 3D space with high confidence. The dimensions of the object must also be known to correctly engage the gripper. A pure position controller fails to robustly complete tasks involving a high degree of geometric uncertainty. Force control is able to achieve robust and versatile behavior in open-ended environments like these. By providing an intelli- gent response in unforeseen situations, it is able to deal with uncertainties to a larger degree than position controllers. Additionally, most assembly and machining operations require exertion of forces to successfully complete tasks. In grinding operations, for example, it is imperative that the force being applied at the points of contact is controlled to correctly match requirements to ensure quality. Force control has drawn the attention of many researchers over the years, yet its successful practical applications are few in number [1]. Among these, a majority can be classified as contact/interaction Introduction 3 tasks, where the robot’s end effector is expected to be in contact with an unknown environment. In other words, the robot is coupled to the environment. The performance of an interaction controller can be improved by closing a force feedback loop around it, resulting in better tracking accuracy. Force-feedback interaction control is useful to realize the full potential of robot arms in the context of performing operations with dex- terity comparable to that of humans, but its adoption is severely limited by its potential to result in unstable behavior. A force-controlled manipulation task can be divided into three distinct phases. In the first phase, the mechanism is unconstrained and the manipulator is free of any external load. The behavior of various control schemes in this phase is well studied and understood. In phase two, the system transitions from free motion to being under load from the environment. In phase three, it performs work on its environment and continues to stay coupled to it. An ideal controller would exhibit smooth and stable behavior in all three of these phases. 1.1.1 Position/Force control From the works of [2–6], who have tackled the task of designing a position/force control architecture, two different approaches to perform simultaneous position and force control have been explored. These are: Hybrid control. In a hybrid position/force controller, the task space is divided into a position control subspace and a force control subspace. Forces are specified and controlled in directions in which the end effector’s motion is constrained by its environment, while positions and velocities are controlled in the directions in which the end effector is free Introduction 4 Figure 1.1.

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