Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators

This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the KUKA LBR iiwa R820, and the Franka Emika Panda, and exploiting these configurations in form of tailored potential functions for singularity avoidance. Monte Carlo simulations of the proposed method and the commonly used manipulability maximization approach are performed for comparison. The numerical results show that the average computing time can be reduced and shorter trajectories in both time and path length are obtained with the proposed approachComment: 8 pages, 2 figures, Accepted for publication at IFAC World Congress 202

Learning of Parameters in Behavior Trees for Movement Skills

Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero explanation when they fail to perform the task. For these reasons, the adoption of RL in industrial settings is not common. Behavior Trees (BTs), on the other hand, can provide a policy representation that a) supports modular and composable skills, b) allows for easy interpretation of the robot actions, and c) provides an advantageous low-dimensional parameter space. In this paper, we present a novel algorithm that can learn the parameters of a BT policy in simulation and then generalize to the physical robot without any additional training. We leverage a physical simulator with a digital twin of our workstation, and optimize the relevant parameters with a black-box optimizer. We showcase the efficacy of our method with a 7-DOF KUKA-iiwa manipulator in a task that includes obstacle avoidance and a contact-rich insertion (peg-in-hole), in which our method outperforms the baselines.Comment: 8 pages, 5 figures, accepted at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Camera geometry determination based on circular's shape for peg-in-hole task

A simple, inexpensive system and effective in performing required tasks is the most preferable in industry. The peg-in-hole task is widely used in manufacturing process by using vision system and sensors. However, it requires complex algorithm and high Degree of Freedom (DOF) mechanism with fine movement. Hence, it will increase the cost. Currently, a forklift-like robot controlled by an operator using wired controllers is used to pick up one by one of the copper wire spools arranged side by side on the shelf to be taken to the inspection area. The holder and puller attached to the robot is used to pick up the spool. It is difficult for the operator to ensure the stem is properly inserted into the hole (peg-in-hole problem) because of the structure of the robot. However, the holder design is not universal and not applicable to other companies. The spool can only be grasped and pulled out from the front side and cannot be grasped using robot arm and gripper. In this study, a vision system is developed to solve the peg-in-hole problem by enabling the robot to autonomously perform the insertion and pick up the spool without using any sensors except a low-cost camera. A low-cost camera is used to capture images of copper wire spool in real-time video. Inspired by how human perceive an object orientation based on its shape, a system is developed to determine camera orientation based on the spool image condition and yaw angle from the center of the camera (CFOV) to CHS. The performance of the proposed system is analyzed based on detection rate analysis. This project is developed by using MATLAB software. The analysis is done in controlled environment with 50-110 cm distance range of camera to the spool. In addition, the camera orientation is analyzed between -20º to 20º yaw angle range. In order to ensure the puller will not scratch the spool, a mathematical equation is derived to calculate the puller tolerance. By using this, the system can estimate the spool position based on the camera orientation and distance calculation. Application of this system is simple and costeffective. A Modified Circular Hough Transform (MCHT) method is proposed and tested with existing method which is Circular Hough Transform (CHT) method to eliminate false circles and outliers. The results of the analysis showed detection success rate of 96% compared to the CHT method. It shows the MCHT method is better than CHT method. The proposed system is able to calculate the distance and camera orientation based on spool image condition with low error rate. Hence, it solves the peg-in-hole problem without using Force/Torque sensor. In conclusion, a total of 7 analysis consist of image pre-processing, image segmentation, object classification, comparison between CHT and MCHT, illumination measurement, distance calculation and yaw angle analysis were experimentally tested including the comparison with the existing method. The proposed system was able to achieve all the objectives

Mastering Autonomous Assembly in Fusion Application with Learning-by-doing: a Peg-in-hole Study

Robotic peg-in-hole assembly is an essential task in robotic automation research. Reinforcement learning (RL) combined with deep neural networks (DNNs) lead to extraordinary achievements in this area. However, current RL-based approaches could hardly perform well under the unique environmental and mission requirements of fusion applications. Therefore, we have proposed a new designed RL-based method. Furthermore, unlike other approaches, we focus on innovations in the structure of DNNs instead of the RL model. Data from the RGB camera and force/torque (F/T) sensor as the input are fed into a multi-input branch network, and the best action in the current state is output by the network. All training and experiments are carried out in a realistic environment, and from the experiment result, this multi-sensor fusion approach has been shown to work well in rigid peg-in-hole assembly tasks with 0.1mm precision in uncertain and unstable environments

Contact force sensing and control for inserting operation during precise assembly using a micromanipulator integrated with force sensors

This paper proposes a novel contact force sensing and control method for the inserting operation during precise assembly process, which is based on a micromanipulator integrated with force sensors. At first, theoretical analysis is carried out to calculate the admissible contact force between the gripped holes and the pegs. The contact force thresholds which are smaller than the admissible contact forces are adopted in the control algorithm to avoid the rotating of the gripped holes during assembly process. The force sensors are calibrated using an ATI force sensor and the conversing coefficients are calculated. The admissible contact forces are tested when different contact distance and preload force are adopted. The performance of the proposed contact force sensing and control method is verified by carrying out the task of applying contact force on the surface of the gripped holes with different contacting speeds. The results indicate that the contact force can be adjusted to be smaller than the threshold 1 and the peg-in-hole assembly can be completed successfully. Note to Practitioners—This paper proposes a novel contact force sensing method during the inserting operation. Compared with the traditional contact force sensing method, this paper adopts the force sensor integrated into the micromanipulator instead of commercial force sensor to detect the contact force between two parts. To ensure the assembling precision, the theoretical analysis is conducted to calculated the admissible contact force to avoid the sliding and rotating of the gripped micro part during assembling. This work efficiently simplifies the contact force sensing and control process, where complex calibration process needn’t to be carried out to eliminate the influence of the mass of the micromanipulator on the testing results. In addition, the assembling costs are reduced by replacing commercial force sensors with strain gauges

Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%

Ergodic Exploration using Tensor Train: Applications in Insertion Tasks

By generating control policies that create natural search behaviors in autonomous systems, ergodic control provides a principled solution to address tasks that require exploration. A large class of ergodic control algorithms relies on spectral analysis, which suffers from the curse of dimensionality, both in storage and computation. This drawback has prohibited the application of ergodic control in robot manipulation since it often requires exploration in state space with more than 2 dimensions. Indeed, the original ergodic control formulation will typically not allow exploratory behaviors to be generated for a complete 6D end-effector pose. In this paper, we propose a solution for ergodic exploration based on the spectral analysis in multidimensional spaces using low-rank tensor approximation techniques. We rely on tensor train decomposition, a recent approach from multilinear algebra for low-rank approximation and efficient computation of multidimensional arrays. The proposed solution is efficient both computationally and storage-wise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task using a 7-axis Franka Emika Panda robot, where ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors

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