Vision-based self-calibration and control of parallel kinematic mechanisms without proprioceptive sensing

International audienceThis work is a synthesis of our experience over parallel kinematic machine control, which aims at changing the standard conceptual approach to this problem. Indeed, since the task space, the state space and the measurement space can coincide in this class of mechanism, we came to redefine the complete modeling, identification and control methodology. Thus, it is shown in this paper that, generically and with the help of sensor-based control, this methodology does not require any joint measurement, thus opening a path to simplified mechanical design and reducing the number of kinematic parameters to identify. This novel approach was validated on the reference parallel kinematic mechanism (the Gough-Stewart platform) with vision as the exteroceptive sensor

Image-based Visual Servoing of a Gough-Stewart Parallel Manipulator using Leg Observations

International audienceIn this paper, a tight coupling between computer vision and paral- lel robotics is exhibited through the projective line geometry. Indeed, contrary to the usual methodology where the robot is modeled indepen- dently from the control law which will be implemented, we take into ac- count, since the early modeling stage, that vision will be used for con- trol. Hence, kinematic modeling and projective geometry are fused into a control-devoted projective kinematic model. Thus, a novel vision-based kinematic modeling of a Gough-Stewart manipulator is proposed through the image projection of its cylindrical legs. Using this model, a visual ser- voing scheme is presented, where the image projection of the non-rigidly linked legs are servoed, rather than the end-effector pose

Body models in humans, animals, and robots: mechanisms and plasticity

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent - yet, as is so often the case, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. In the biological realm, evidence has been accumulated by diverse disciplines giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables to control the machine. In this article I compare the character of body representations in biology with their robotic counterparts and relate that to the differences in performance that we observe. I put forth a number of axes regarding the nature of such body models: fixed vs. plastic, amodal vs. modal, explicit vs. implicit, serial vs. parallel, modular vs. holistic, and centralized vs. distributed. An interesting trend emerges: on many of the axes, there is a sequence from robot body models, over body image, body schema, to the body representation in lower animals like the octopus. In some sense, robots have a lot in common with Ian Waterman - "the man who lost his body" - in that they rely on an explicit, veridical body model (body image taken to the extreme) and lack any implicit, multimodal representation (like the body schema) of their bodies. I will then detail how robots can inform the biological sciences dealing with body representations and finally, I will study which of the features of the "body in the brain" should be transferred to robots, giving rise to more adaptive and resilient, self-calibrating machines.Comment: 27 pages, 8 figure

One Camera in Hand for Kinematic Calibration of a Parallel Robot

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. Incremental positions of the end effector are related to incremental positions of encoders of the motors of the robot. A kinematic model of the robot is modified in order to take into account possible errors of kinematic parameters. The solution of the model utilizes incremental positions of the resolvers and end effector, the new parameters minimizes errors in the kinematic equations. Spherical properties and intrinsic camera parameters are utilized to model sphere projection in order to improve spatial measurements. The robot system is designed to carry out tracking tasks and the calibration of the system is finally validated by means of integrating the errors of the visual controller

The Role of Stereopsis in the Control of Grasp Forces during Prehension

Background: Binocular viewing is associated with a superior prehensile performance, which is particularly evident in the latter part of the reach as the hand approaches and makes contact with the target object. However, the visuomotor mechanisms through which binocular vision serves prehensile performance remains unclear. The present study was designed to investigate the role of stereopsis in the planning and control of grasping using outcome measures which reflect predictive control. It was hypothesized that binocular viewing will be associated with more efficient grasp execution because stereoacuity provides more accurate sensory input about the object’s material properties to plan appropriate grip forces to successfully lift the target object. In the case when binocular vision is reduced or unavailable, predictive control of grasping will be reduced, and subjects will have to rely on somatosensory feedback to successfully execute the grasp. Methods: 20 healthy participants (17-35 years, 11 male) with normal vision were recruited. Subjects performed a precision reach-to-grasp task which required them to reach, grasp, and transport a bead (~2 cm in diameter) to a specified location. Subjects were instructed to perform the task as fast as possible in the following viewing conditions: binocular, monocular, and two conditions with reduced stereoacuity: 200 arcsec stereo, 800 arcsec stereo, which were randomized in blocks. Results: Binocular, compared to monocular viewing had a greater influence on the grasp phase compared to the reach and transport phase. Specifically, there was a 36% increase in post-contact time, 29% decrease in grip force 50ms following object grasp, and 30% increase in grasp errors. In contrast, parameters of the reach and transport phase only demonstrated a 3-8% reduction in performance. Grasp performance was similarly disrupted during binocular viewing with reduced stereoacuity whereby a reduction in stereoacuity was associated with a proportional reduction in grasp performance. Notably, grip force at the time of object lift-off was comparable between all viewing conditions. Conclusion: The results demonstrate that binocular viewing contributes significantly more to the performance of grasping relative to the reach and transport phase. In addition, the results suggest that stereopsis provides important sensory information which enables the central nervous system to engage in predictive control of grasp forces. When binocular disparity information is reduced or absent, subjects take on a more cautious approach to the grasp and make more errors (i.e., collisions followed by readjustments). Overall, findings from the current study indicate that stereopsis provides important sensory input for the predictive control of grasping, and a progressive reduction in stereopsis is associated with increased uncertainty which results in a greater reliance on somatosensory feedback control

Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin

This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence. More specifically, with respect to i) the present model contributes to hypothesizing a learning mechanisms for peripersonal space. In relation to point ii) we show how a relatively simple controller can exploit the learned receptive fields to generate either avoidance or reaching of an incoming stimulus and for iii) we show how the robot can select arbitrary body parts as the controlled end-point of an avoidance or reaching movement

Preliminary variation on multiview geometry for vision-guided laser surgery.

International audienceThis paper proposes to use the multiview geometry to control an orientable laser beam for surgery. Two methods are proposed based on the analogy between a scanning laser beam and a camera: the first method uses one camera and the laser scanner as a virtual camera to form a virtual stereoscopic system while the second method uses two cameras to form a virtual trifocal system. Using the associated epipolar or trifocal geometry, two control laws are derived without any matrix inversion nor estimation of the 3D scene. It is shown that the more geometry is used, the simpler the control gets. These control laws show, as expected, exponential convergence in simulation validation

인간 기계 상호작용을 위한 강건하고 정확한 손동작 추적 기술 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 이동준.Hand-based interface is promising for realizing intuitive, natural and accurate human machine interaction (HMI), as the human hand is main source of dexterity in our daily activities. For this, the thesis begins with the human perception study on the detection threshold of visuo-proprioceptive conflict (i.e., allowable tracking error) with or without cutantoues haptic feedback, and suggests tracking error specification for realistic and fluidic hand-based HMI. The thesis then proceeds to propose a novel wearable hand tracking module, which, to be compatible with the cutaneous haptic devices spewing magnetic noise, opportunistically employ heterogeneous sensors (IMU/compass module and soft sensor) reflecting the anatomical properties of human hand, which is suitable for specific application (i.e., finger-based interaction with finger-tip haptic devices). This hand tracking module however loses its tracking when interacting with, or being nearby, electrical machines or ferromagnetic materials. For this, the thesis presents its main contribution, a novel visual-inertial skeleton tracking (VIST) framework, that can provide accurate and robust hand (and finger) motion tracking even for many challenging real-world scenarios and environments, for which the state-of-the-art technologies are known to fail due to their respective fundamental limitations (e.g., severe occlusions for tracking purely with vision sensors; electromagnetic interference for tracking purely with IMUs (inertial measurement units) and compasses; and mechanical contacts for tracking purely with soft sensors). The proposed VIST framework comprises a sensor glove with multiple IMUs and passive visual markers as well as a head-mounted stereo camera; and a tightly-coupled filtering-based visual-inertial fusion algorithm to estimate the hand/finger motion and auto-calibrate hand/glove-related kinematic parameters simultaneously while taking into account the hand anatomical constraints. The VIST framework exhibits good tracking accuracy and robustness, affordable material cost, light hardware and software weights, and ruggedness/durability even to permit washing. Quantitative and qualitative experiments are also performed to validate the advantages and properties of our VIST framework, thereby, clearly demonstrating its potential for real-world applications.손 동작을 기반으로 한 인터페이스는 인간-기계 상호작용 분야에서 직관성, 몰입감, 정교함을 제공해줄 수 있어 많은 주목을 받고 있고, 이를 위해 가장 필수적인 기술 중 하나가 손 동작의 강건하고 정확한 추적 기술 이다. 이를 위해 본 학위논문에서는 먼저 사람 인지의 관점에서 손 동작 추적 오차의 인지 범위를 규명한다. 이 오차 인지 범위는 새로운 손 동작 추적 기술 개발 시 중요한 설계 기준이 될 수 있어 이를 피험자 실험을 통해 정량적으로 밝히고, 특히 손끝 촉각 장비가 있을때 이 인지 범위의 변화도 밝힌다. 이를 토대로, 촉각 피드백을 주는 것이 다양한 인간-기계 상호작용 분야에서 널리 연구되어 왔으므로, 먼저 손끝 촉각 장비와 함께 사용할 수 있는 손 동작 추적 모듈을 개발한다. 이 손끝 촉각 장비는 자기장 외란을 일으켜 착용형 기술에서 흔히 사용되는 지자기 센서를 교란하는데, 이를 적절한 사람 손의 해부학적 특성과 관성 센서/지자기 센서/소프트 센서의 적절한 활용을 통해 해결한다. 이를 확장하여 본 논문에서는, 촉각 장비 착용 시 뿐 아니라 모든 장비 착용 / 환경 / 물체와의 상호작용 시에도 사용 가능한 새로운 손 동작 추적 기술을 제안한다. 기존의 손 동작 추적 기술들은 가림 현상 (영상 기반 기술), 지자기 외란 (관성/지자기 센서 기반 기술), 물체와의 접촉 (소프트 센서 기반 기술) 등으로 인해 제한된 환경에서 밖에 사용하지 못한다. 이를 위해 많은 문제를 일으키는 지자기 센서 없이 상보적인 특성을 지니는 관성 센서와 영상 센서를 융합하고, 이때 작은 공간에 다 자유도의 움직임을 갖는 손 동작을 추적하기 위해 다수의 구분되지 않는 마커들을 사용한다. 이 마커의 구분 과정 (correspondence search)를 위해 기존의 약결합 (loosely-coupled) 기반이 아닌 강결합 (tightly-coupled 기반 센서 융합 기술을 제안하고, 이를 통해 지자기 센서 없이 정확한 손 동작이 가능할 뿐 아니라 착용형 센서들의 정확성/편의성에 문제를 일으키던 센서 부착 오차 / 사용자의 손 모양 등을 자동으로 정확히 보정한다. 이 제안된 영상-관성 센서 융합 기술 (Visual-Inertial Skeleton Tracking (VIST)) 의 뛰어난 성능과 강건성이 다양한 정량/정성 실험을 통해 검증되었고, 이는 VIST의 다양한 일상환경에서 기존 시스템이 구현하지 못하던 손 동작 추적을 가능케 함으로써, 많은 인간-기계 상호작용 분야에서의 가능성을 보여준다.1 Introduction 1 1.1. Motivation 1 1.2. Related Work 5 1.3. Contribution 12 2 Detection Threshold of Hand Tracking Error 16 2.1. Motivation 16 2.2. Experimental Environment 20 2.2.1. Hardware Setup 21 2.2.2. Virtual Environment Rendering 23 2.2.3. HMD Calibration 23 2.3. Identifying the Detection Threshold of Tracking Error 26 2.3.1. Experimental Setup 27 2.3.2. Procedure 27 2.3.3. Experimental Result 31 2.4. Enlarging the Detection Threshold of Tracking Error by Haptic Feedback 31 2.4.1. Experimental Setup 31 2.4.2. Procedure 32 2.4.3. Experimental Result 34 2.5. Discussion 34 3 Wearable Finger Tracking Module for Haptic Interaction 38 3.1. Motivation 38 3.2. Development of Finger Tracking Module 42 3.2.1. Hardware Setup 42 3.2.2. Tracking algorithm 45 3.2.3. Calibration method 48 3.3. Evaluation for VR Haptic Interaction Task 50 3.3.1. Quantitative evaluation of FTM 50 3.3.2. Implementation of Wearable Cutaneous Haptic Interface 51 3.3.3. Usability evaluation for VR peg-in-hole task 53 3.4. Discussion 57 4 Visual-Inertial Skeleton Tracking for Human Hand 59 4.1. Motivation 59 4.2. Hardware Setup and Hand Models 62 4.2.1. Human Hand Model 62 4.2.2. Wearable Sensor Glove 62 4.2.3. Stereo Camera 66 4.3. Visual Information Extraction 66 4.3.1. Marker Detection in Raw Images 68 4.3.2. Cost Function for Point Matching 68 4.3.3. Left-Right Stereo Matching 69 4.4. IMU-Aided Correspondence Search 72 4.5. Filtering-based Visual-Inertial Sensor Fusion 76 4.5.1. EKF States for Hand Tracking and Auto-Calibration 78 4.5.2. Prediction with IMU Information 79 4.5.3. Correction with Visual Information 82 4.5.4. Correction with Anatomical Constraints 84 4.6. Quantitative Evaluation for Free Hand Motion 87 4.6.1. Experimental Setup 87 4.6.2. Procedure 88 4.6.3. Experimental Result 90 4.7. Quantitative and Comparative Evaluation for Challenging Hand Motion 95 4.7.1. Experimental Setup 95 4.7.2. Procedure 96 4.7.3. Experimental Result 98 4.7.4. Performance Comparison with Existing Methods for Challenging Hand Motion 101 4.8. Qualitative Evaluation for Real-World Scenarios 105 4.8.1. Visually Complex Background 105 4.8.2. Object Interaction 106 4.8.3. Wearing Fingertip Cutaneous Haptic Devices 109 4.8.4. Outdoor Environment 111 4.9. Discussion 112 5 Conclusion 116 References 124 Abstract (in Korean) 139 Acknowledgment 141박

An Overview about Emerging Technologies of Autonomous Driving

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems

人間を模擬した知覚系を持つ2足ヒューマノイドロボットの自己位置推定機能に対する歩行動作の影響に関する研究

早大学位記番号:新8279早稲田大