Sensorless torque/force control

Motion control systems represent a main subsystem for majority of processing systems that can be found in the industrial sector. These systems are concerned with the actuation of all devices in the manufacturing process such as machines, robots, conveyor systems and pick and place mechanisms such that they satisfy certain motion requirements, e.g., the pre specified reference trajectories are followed along with delivering the proper force or torque to the point of interest at which the process occurs. In general, the aim of force/torque control is to impose the desired force on the environment even if the environment has dynamical motion

불확실성을 포함하는 조립작업을 위한 컴플라이언스 기반 펙인홀 전략

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2020. 8. 박재흥.The peg-in-hole assembly is a representative robotic task that involves physical contact with the external environment. The strategies generally involve performing the assembly task by estimating the contact state between the peg and the hole. The contact forces and moments, measured using force sensors, are primarily used to estimate the contact state. In this paper, in contrast to past research in the area, which has involved the utilization of such expensive devices as force/torque sensors or remote compliance mechanisms, an inexpensive method is proposed for peg-in-hole assembly without force feedback or passive compliance mechanisms. The method consists of an analysis of the state of contact between the peg and the hole as well as a strategy to overcome the inevitable positional uncertainty of the hole incurred in the recognition process. A control scheme was developed to yield compliant behavior from the robot with physical contact under the condition of hybrid position/force control. Proposed peg-in-hole strategy is based on compliance characteristics and generating the force and moment. The peg is inserted into the hole as it adapts to the external environment. The effectiveness of the proposed method was experimentally verified using a humanoid upper body robot with fifty degrees of freedom and a peg-in-hole apparatus with a small clearance (0.1 mm). Three cases of experiments were conducted; Assembling the peg attached to the arm in the hole fixed in the external environment, grasping a peg with an anthropomorphic hand and assembling it into a fixed hole, and grasping both peg and hole with both hands and assembling each other. In order to assemble the peg-in-hole through the proposed strategy by the humanoid upper body robot, I present a method of gripping an object, estimating the kinematics of the gripped object, and manipulating the gripped object. In addition to the cost aspect, which is the fundamental motivation for the proposed strategy, the experimental results show that the proposed strategy has advantages such as fast assembly time and high success rate, but has the disadvantage of unpredictable elapsed time. The reason for having a high variance value for the success time is that the spiral trajectory, which is most commonly used, is used. In this study, I analyze the efficiency of spiral force trajectory and propose an improved force trajectory. The proposed force trajectory reduces the distribution of elapsed time by eliminating the uncertainty in the time required to find a hole. The efficiency of the force trajectory is analyzed numerically, verified through repeated simulations, and verified by the actual experiment with humanoid upper body robot developed by Korea institute of industrial technology.펙인홀 조립은 로봇의 접촉 작업을 대표하는 작업으로, 펙인홀 조립 전략을 연구함으로써 산업 생산 분야의 조립작업에 적용할 수 있다. 펙인홀 조립작업은 일반적으로 펙과 홀 간의 접촉상태를 추정함으로써 이루어진다. 접촉상태를 추정하기 위해 가장 널리 쓰이는 방법은 힘 센서를 사용하는 것인데, 접촉 힘과 모멘트를 측정하여 접촉상태를 추정하는 방식이다. 만약 이러한 센서를 사용하지 않을 수 있다면, 하드웨어 비용과 소프트웨어 연산량 감소 등의 장점이 있음은 자명하다. 본 논문에서는 힘 센서 혹은 수동 컴플라이언스 장치를 사용하지 않는 펙인홀 전략을 제안한다. 홀에 대한 인식 오차 혹은 로봇의 제어 오차를 극복하기 위하여 먼저 펙과 홀의 접촉 가능 상태를 분석하고 로봇의 컴플라이언스 모션을 위한 제어 프레임워크를 디자인한다. 전략은 컴플라이언스 특징에 기반하며 펙에 힘과 모멘트를 생성시킴으로써 조립작업을 수행한다. 펙은 외부환경에 순응함으로써 홀에 삽입된다. 제안한 전략은 낮은 공차를 갖는 펙인홀 실험을 통해서 그 유효성이 검증된다. 펙과 홀을 로봇팔과 외부환경에 각각 고정된 환경에서의 실험, 인간형 로봇핸드를 이용하여 펙을 잡아서 고정된 홀에 삽입하는 실험, 그리고 테이블에 놓인 펙과 홀을 각각 로봇핸드로 파지하여 조립하는 총 세 가지의 실험을 수행하였다. 핸드로 펙을 파지하고 조작하기 위하여, 파지 방법과 핸드를 이용한 물체 조작 알고리즘을 간략히 소개하였다. 제안한 전략의 성능을 실험적으로 분석한 결과, 높은 조립 성공률을 갖는 대신 조립시간이 예측할 수 없는 단점이 나타나 이를 보완하기 위해서 렌치 궤적 또한 제안하였다. 먼저 가장 일반적으로 사용되는 나선 힘 궤적을 이용했을 때 조립 성공시간의 분산이 큰 이유를 확률개념을 이용해 분석하고, 이를 보완하기 위한 부분적 나선 힘 궤적을 제안한다. 제안한 힘 궤적이 나선 힘 궤적에 비해 갖는 성능의 우수성을 증명하기 위하여 수치적 분석, 반복적 시뮬레이션, 그리고 로봇을 이용한 실험을 수행하였다.1 INTRODUCTION 1 1.1 Motivation: Peg-in-Hole Assembly 1 1.2 Contributions of Thesis 2 1.3 Overview of Thesis 3 2 COMPLIANCE BASED STRATEGY 5 2.1 Background & Related Works 5 2.2 Analysis of Peg-in-Hole Procedure 6 2.2.1 Contact Analysis 7 2.2.2 Basic Idea 9 2.3 Peg-in-Hole Strategy 12 2.3.1 Unit Motions 12 2.3.2 State of Strategy 13 2.3.3 Conditions for State Transition 15 2.4 Control Frameworks 18 2.4.1 Control for Compliant Behavior 18 2.4.2 Friction Compensate 20 2.4.3 Control Input for the Strategy 25 2.5 Experiment 29 2.5.1 Experiment Environment 29 2.5.2 Fixed Peg and Fixed Hole 31 2.5.2.1 Experiment Results 31 2.5.2.2 Analysis of Force and Control Gain 36 2.5.3 Peg-in-Hole with Multi Finger Hand 41 2.5.3.1 Object Grasping 42 2.5.3.2 Object In-Hand Manipulation 44 2.5.3.3 Experiment Results 49 2.5.4 With Upper Body Robot 50 2.5.4.1 Peg-in-Hole Procedure 52 2.5.4.2 Kinematics of Grasped Object 54 2.5.4.3 Control Frameworks 54 2.5.4.4 Experiment Results 56 2.6 Discussion 59 2.6.1 Peg-in-Hole Transition 59 2.6.2 Influential Issues 59 3 WRENCH TRAJECTORY 63 3.1 Problem Statement 64 3.1.1 Hole Search Process 64 3.1.2 Spiral Force Trajectory Analysis 66 3.2 Partial Spiral Force Trajectory 70 3.2.1 Force Trajectory with Tilted Posture 70 3.2.2 Probability to Three-point Contact 76 3.3 SIMULATION & EXPERIMENT 78 3.3.1 Simulation 78 3.3.2 Experiment 83 4 CONCLUSIONS 90 Abstract (In Korean) 102Docto

Master-Slave Coordination Using Virtual Constraints for a Redundant Dual-Arm Haptic Interface

Programming robots for tasks involving force interaction is difficult, since both the knowledge of the task and the dynamics of the robots are necessary. An immersive haptic interface for task demonstration is proposed, where theoperator can sense and act through the robot. This is achieved by coupling two robotic systems with virtual constraints such that they have the same coordinates in the operational space disregarding a fixed offset. Limitations caused by the singular configurations or the reach of the robots are naturally reflected to either side as haptic feedback

Learning-based adaption of robotic friction models

In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding problem due to the lack of a good mathematical description. This motivates the usage of data-driven methods in recent works. However, model-based and data-driven models often exhibit limitations in their ability to generalize beyond the specific dynamics they were trained on, as we demonstrate in this paper. To address this challenge, we introduce a novel approach based on residual learning, which aims to adapt an existing friction model to new dynamics using as little data as possible. We validate our approach by training a base neural network on a symmetric friction data set to learn an accurate relation between the velocity and the friction torque. Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network's output. By combining the output of both networks in a suitable manner, our proposed estimator outperforms the conventional model-based approach and the base neural network significantly. Furthermore, we evaluate our method on trajectories involving external loads and still observe a substantial improvement, approximately 60-70\%, over the conventional approach. Our method does not rely on data with external load during training, eliminating the need for external torque sensors. This demonstrates the generalization capability of our approach, even with a small amount of data-only 43 seconds of a robot movement-enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings

On Sensorless Collision Detection and Measurement of External Forces in Presence of Modeling Inaccuracies

The field of human-robot interaction has garnered significant interest in the last decade. Every form of human-robot coexistence must guarantee the safety of the user. Safety in human-robot interaction is being vigorously studied, in areas such as collision avoidance, soft actuators, light-weight robots, computer vision techniques, soft tissue modeling, collision detection, etc. Despite the safety provisions, unwanted collisions can occur in case of system faults. In such cases, before post-collision strategies are triggered, it is imperative to effectively detect the collisions. Implementation of tactile sensors, vision systems, sonar and Lidar sensors, etc., allows for detection of collisions. However, due to the cost of such methods, more practical approaches are being investigated. A general goal remains to develop methods for fast detection of external contacts using minimal sensory information. Availability of position data and command torques in manipulators permits development of observer-based techniques to measure external forces/torques. The presence of disturbances and inaccuracies in the model of the robot presents challenges in the efficacy of observers in the context of collision detection. The purpose of this thesis is to develop methods that reduce the effects of modeling inaccuracies in external force/torque estimation and increase the efficacy of collision detection. It is comprised of the following four parts: 1. The KUKA Light-Weight Robot IV+ is commonly employed for research purposes. The regressor matrix, minimal inertial parameters and the friction model of this robot are identified and presented in detail. To develop the model, relative weight analysis is employed for identification. 2. Modeling inaccuracies and robot state approximation errors are considered simultaneously to develop model-based time-varying thresholds for collision detection. A metric is formulated to compare trajectories realizing the same task in terms of their collision detection and external force/torque estimation capabilities. A method for determining optimal trajectories with regards to accurate external force/torque estimation is also developed. 3. The effects of velocity on external force/torque estimation errors are studied with and without the use of joint force/torque sensors. Velocity-based thresholds are developed and implemented to improve collision detection. The results are compared with the collision detection module integrated in the KUKA Light-Weight Robot IV+. 4. An alternative joint-by-joint heuristic method is proposed to identify the effects of modeling inaccuracies on external force/torque estimation. Time-varying collision detection thresholds associated with the heuristic method are developed and compared with constant thresholds. In this work, the KUKA Light-Weight Robot IV+ is used for obtaining the experimental results. This robot is controlled via the Fast Research Interface and Visual C++ 2008. The experimental results confirm the efficacy of the proposed methodologies

A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions

This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via F/T sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1 degree-of-freedom testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload.Comment: Accepted in IEEE/ASME Transactions on Mechatronics (T-MECH