Motion Recognition and Planning Using Gaussian Process Dynamical Models

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 박종우.본 논문에서는 로봇이 해석적으로 정의되지 않은 환경에 대응하는 문제에 관해 다룬다. 이 환경에는 로봇이 피해야 하는 장애물과 하지 외골격 로봇 착용자의 동작 의도와 밀접하게 연관된 지형지물이 있다. 관절 공간과 그 저차원 공간에서의 경로 계획법을 통해 장애물을 회피 하였고 기계학습 기법을 통해 지형지물에 기인한 사람의 동작 의도를 추정하였다. 먼저 Gaussian process dynamical models (GPDM) 기반으로 하지 외골격 로봇 착용자의 운동 의도를 추정하는 알고리즘을 제안하였다. 관측한 짧은 시계열 입력 값에 대하여 이에 상응하는 저차원 공간 좌표를 Gaussian process regression 을 통해 얻는다. 각 모델에 대한 유사도는 학습 데이터에 대한 관측 값과 그 저차원 공간 좌표의 로그 조건부 확률분포 형태로 표현된다. 이 유사도를 비교하여 가장 가능성 있는 동작을 추정한다. 하지 외골격 로봇 프로토타입 및 동작 추적 시스템을 이용한 물리적 실험을 통해 우리의 알고리즘을 검증하였다. 다음으로는 적응적으로 스텝사이즈를 결정하는 RRT 알고리즘을 제안하였다. 지수 곱(Product of Exponentials, PoE) 형태로 표현된 로봇의 정기구학과 표준 작용소 노름 부등식으로부터 직렬 개 연쇄 로봇의 엔드이펙터의 작업공간에서의 최대 변위와 관절 공간에서의 변위에 대한 부등식으로 유도하였다. 이 부등식을 이용하여 주어진 장애물의 최소 크기에 대하여 적응적으로 스텝사이즈를 결정하였다. 10 자유도 평면 개 연쇄 로봇과 7축 산업용 매니퓰레이터를 이용하여 우리의 알고리즘을 검증하였다. 마지막으로 사람의 시연 동작을 GPDM을 이용해 저차원 공간으로 학습하여, 사람과 유사한 동작을 생성하는 저차원 공간에서의 경로 계획 방법을 제안하였다. 앞서 유도한 부등식을 저차원 공간에서의 변위와 작업공간에서의 각 링크의 변위에 대한 부등식으로 확장하였다. 이를 이용하여 작업공간에서 정의된 장애물을 샘플링 기반으로 저차원 공간으로 매핑하였다. 그리고 학습한 동작과 새롭게 생성한 동작 사이의 유사성을 측정하는 측도를 GPDM 커널함수를 기반으로 정의하였다. 시뮬레이터와 실제 로봇에 적용해 봄으로써 제안한 방법의 유효성을 검증하였다.In this thesis, we deal with the problems that the robot copes with unstructured environments. Examples of such environments are obstacles that robots should avoid and terrain features that are closely related to the intentions of the wearer of an exoskeleton robot. We make robots to avoid obstacles through path planning algorithms in joint space and its low-dimensional space. We also estimate human motion intentions caused by terrain features using machine learning techniques. First, we propose an algorithm based on Gaussian process dynamical models (GPDM) to estimate motion intention of the wearer of exoskeleton robot. For the observed short time series input values, the corresponding low dimensional space coordinates are obtained via Gaussian process regression. The similarity for each model is expressed in the form of the logarithm of the conditional probability distribution of observed values and its low-dimensional coordinates given the training data. This similarity is compared to estimate the most likely motion. We validate our algorithm through physical experiments using an exoskeleton robot prototype and motion tracking system. Next, we propose a rapidly-exploring random tree (RRT) algorithm that adaptively determines an appropriate stepsize. Using a standard operator norm inequality and the forward kinematics equations expressed as the product of exponentials, we derive an approximate bound on the Cartesian displacement of the open chain tip for a given joint space displacement. Using this inequality bound, we adaptively determine the stepsize for a given minimum obstacle size. We verify our algorithm by numerical experiments using a ten-dof planar open chain robot and a seven-axis industrial manipulator. Finally, we propose a path planning method in a low-dimensional space that generates a human-like motion by learning the human demonstration motion using GPDM. We extend the above inequality to the inequality between displacement in the low-dimensional space and displacement of each links in the workspace. We use this to map the obstacles defined in the workspace to the low-dimensional space based on the uniform sampling. In addition, we define a measure based on the GPDM kernel function to measure the similarity between the learned motion and the newly generated motion. We validate the proposed method by applying it to a simulator and an actual robot.Abstract iii List of Tables xi List of Figures xiii 1 Introduction 1 1.1 Contributions of This Thesis . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 GPDM-Based Human Motion Intention Recognition for Lower-Limb Exoskeleton . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 An Adaptive Stepsize RRT Planning Algorithm for Open Chain Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.3 A Gaussian Process Dynamical Model-Based Planning Method 8 1.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Preliminaries 11 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Gaussian Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Gaussian Process Regression . . . . . . . . . . . . . . . . . . 12 2.2.2 Gaussian Process Latent Variable models . . . . . . . . . . . 16 2.2.3 Gaussian Process Dynamical Models . . . . . . . . . . . . . . 19 2.3 Forward Kinematics of Open Chains . . . . . . . . . . . . . . . . . . 23 3 GPDM-Based Human Motion Intention Recognition for Lower-Limb Exoskeleton 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Human Motion Intention Recognition using GPDM . . . . . . . . . 29 3.3 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.1 Human Motion Capture Data . . . . . . . . . . . . . . . . . 30 3.3.2 Sensor Mock-up Data . . . . . . . . . . . . . . . . . . . . . . 33 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4.2 Human Motion Capture Data . . . . . . . . . . . . . . . . . 40 3.4.3 Sensor Mock-up Data . . . . . . . . . . . . . . . . . . . . . . 44 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.5.1 Comparison both Data Sets . . . . . . . . . . . . . . . . . . . 45 3.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . 49 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4 An Adaptive Stepsize RRT Planning Algorithm for Open Chain Robots 53 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2 A Cartesian Displacement Bound for Open Chains . . . . . . . . . 54 4.3 Adaptive Stepsize RRT . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.4.1 Ten-Dof Planar Robot . . . . . . . . . . . . . . . . . . . . . . 65 4.4.2 Ten-Dof Planar Robot Case II: Latent Space RRT . . . . . 70 4.4.3 Seven-DoF Industrial Robot Arm . . . . . . . . . . . . . . . 77 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 A Gaussian Process Dynamical Model-Based Planning Method 85 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2 Learning from Demonstration Framework . . . . . . . . . . . . . . . 88 5.2.1 Learning a New Pose in the Latent Space . . . . . . . . . . 90 5.2.2 Constraints in Latent Space . . . . . . . . . . . . . . . . . . 91 5.2.3 Mapping Obstacle into Latent Space . . . . . . . . . . . . . 92 5.2.4 Motion Planning in Latent Space . . . . . . . . . . . . . . . 102 5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5.3.1 Grasping Experiments . . . . . . . . . . . . . . . . . . . . . . 108 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 6 Conclusion 117 Bibliography 121 Abstract 128Docto

Stackelberg Meta-Learning for Strategic Guidance in Multi-Robot Trajectory Planning

Guided cooperation is a common task in many multi-agent teaming applications. The planning of the cooperation is difficult when the leader robot has incomplete information about the follower, and there is a need to learn, customize, and adapt the cooperation plan online. To this end, we develop a learning-based Stackelberg game-theoretic framework to address this challenge to achieve optimal trajectory planning for heterogeneous robots. We first formulate the guided trajectory planning problem as a dynamic Stackelberg game and design the cooperation plans using open-loop Stackelberg equilibria. We leverage meta-learning to deal with the unknown follower in the game and propose a Stackelberg meta-learning framework to create online adaptive trajectory guidance plans, where the leader robot learns a meta-best-response model from a prescribed set of followers offline and then fast adapts to a specific online trajectory guidance task using limited learning data. We use simulations in three different scenarios to elaborate on the effectiveness of our framework. Comparison with other learning approaches and no guidance cases show that our framework provides a more time- and data-efficient planning method in trajectory guidance tasks

Learning Probabilistic Generative Models For Fast Sampling-Based Planning

Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of GMMs learned from prior collision history. For efficient sampling, we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction to be automatically determined. Since these approaches with the learned probabilistic models require large size data and time for training, it is essential that they are able to be adapted to environmental change in an online manner. The suggested online learning approach with the Dirichlet Process Mixture Model (DPMM) can adapt the complexity to the data and learn new Gaussian clusters with streaming data in newly explored areas without batch learning. We have applied these approaches in a number of robot arm planning scenarios and have shown their utility and effectiveness in simulation and on a physical 7-DoF robot manipulator

A survey of path planning of industrial robots based on rapidly exploring random trees

Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm

Improved Sampling Based Motion Planning Through Local Learning

Every motion made by a moving object is either planned implicitly, e.g., human natural movement from one point to another, or explicitly, e.g., pre-planned information about where a robot should move in a room to effectively avoid colliding with obstacles. Motion planning is a well-studied concept in robotics and it involves moving an object from a start to goal configuration. Motion planning arises in many application domains such as robotics, computer animation (digital actors), intelligent CAD (virtual prototyping and training) and even computational biology (protein folding and drug design). Interestingly, a single class of planners, sampling-based planners have proven effective in all these domains. Probabilistic Roadmap Methods (PRMs) are one type of sampling-based planners that sample robot configurations (nodes) and connect them via viable local paths (edges) to form a roadmap containing representative feasible trajectories. The roadmap is then queried to find solution paths between start and goal configurations. Different PRM strategies perform differently given different input parameters, e.g., workspace environments and robot definitions. Motion planning, however, is computationally hard – it requires geometric path planning which has been shown to be PSPACE hard, complex representational issues for robots with known physical, geometric and temporal constraints, and challenging mapping/representing requirements for the workspace environment. Many important environments, e.g., houses, factories and airports, are heterogeneous, i.e., contain free, cluttered and narrow spaces. Heterogeneous environments, however, introduce a new set of problems for motion planning and PRM strategies because there is no ideal method suitable for all regions in the environment. In this work we introduce a technique that can adapt and apply PRM methods suitable for local regions in an environment. The basic strategy is to first identify a local region of the environment suitable for the current action based on identified neighbors. Next, based on past performance of methods in this region, adapt and pick a method to use at this time. This selection and adaptation is done by applying machine learning. By performing the local region creation in this dynamic fashion, we remove the need to explicitly partition the environment as was done in previous methods and which is difficult to do, slows down performance and includes the difficult process of determining what strategy to use even after making an explicit partitioning. Our method handles and removes these overheads. We show benefits of this approach in both planning robot motions and in protein folding simulations. We perform experiments on robots in simulation with different degrees of freedom and varying levels of heterogeneity in the environment and show an improvement in performance when our local learning method is applied. Protein folding simulations were performed on 23 proteins and we note an improvement in the quality of pathways produced with comparable performance in terms of time needed to build the roadmap

기구학적 및 동적 제한조건들을 고려한 모바일 매니퓰레이터의 작업 중심 전신 동작 생성 전략

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2023. 2. 박재흥.모바일 매니퓰레이터는 모바일 로봇에 장착된 매니퓰레이터입니다. 모바일 매니퓰레이터는 고정형 매니퓰레이터에 비해 모바일 로봇의 이동성을 제공받기 때문에 다양하고 복잡한 작업을 수행할 수 있습니다. 그러나 두 개의 서로 다른 시스템을 결합함으로써 모바일 매니퓰레이터의 전신을 계획하고 제어할 때 여러 특징을 고려해야 합니다. 이러한 특징들은 여자유도, 두 시스템의 관성 차이 및 모바일 로봇의 비홀로노믹 제한 조건 등이 있습니다. 본 논문의 목적은 기구학적 및 동적 제한조건들을 고려하여 모바일 매니퓰레이터의 전신 동작 생성 전략을 제안하는 것입니다. 먼저, 모바일 매니퓰레이터가 초기 위치에서 문을 통과하여 목표 위치에 도달하기 위한 전신 경로를 계산하는 프레임워크를 제안합니다. 이 프레임워크는 로봇과 문에 의해 생기는 기구학적 제한조건을 고려합니다. 제안하는 프레임워크는 두 단계를 거쳐 전신의 경로를 얻습니다. 첫 번째 단계에서는 그래프 탐색 알고리즘을 이용하여 모바일 로봇의 자세 경로와 문의 각도 경로를 계산합니다. 특히, 그래프 탐색에서 area indicator라는 정수 변수를 상태의 구성 요소로서 정의하는데, 이는 문에 대한 모바일 로봇의 상대적 위치를 나타냅니다. 두 번째 단계에서는 모바일 로봇의 경로와 문의 각도를 통해 문의 손잡이 위치를 계산하고 역기구학을 활용하여 매니퓰레이터의 관절 위치를 계산합니다. 제안된 프레임워크의 효율성은 비홀로노믹 모바일 매니퓰레이터를 사용한 시뮬레이션 및 실제 실험을 통해 검증되었습니다. 둘 째, 최적화 방법을 기반으로한 전신 제어기를 제안합니다. 이 방법은 등식 및 부등식 제한조건 모두에 대해 가중 행렬을 반영한 계층적 최적화 문제의 해를 계산합니다. 이 방법은 모바일 매니퓰레이터 또는 휴머노이드와 같이 자유도가 많은 로봇의 여자유도를 해결하기 위해 개발되어 작업 우선 순위에 따라 가중치가 다른 관절 동작으로 여러 작업을 수행할 수 있습니다. 제안된 방법은 가중 행렬을 최적화 문제의 1차 최적 조건을 만족하도록 하며, Active-set 방법을 활용하여 등식 및 부등식 작업을 처리합니다. 또한, 대칭적인 영공간 사영 행렬을 사용하여 계산상 효율적입니다. 결과적으로, 제안된 제어기를 활용하는 로봇은 우선 순위에 따라 개별적인 관절 가중치를 반영하여 전신 움직임을 효과적으로 보여줍니다. 제안된 제어기의 효용성은 모바일 매니퓰레이터와 휴머노이드를 이용한 실험을 통해 검증하였습니다. 마지막으로, 모바일 매니퓰레이터의 동적 제한조건들 중 하나로서 자가 충돌 회피 알고리즘을 제안합니다. 제안된 방법은 매니퓰레이터와 모바일 로봇 간의 자가 충돌에 중점을 둡니다. 모바일 로봇의 버퍼 영역을 둘러싸는 3차원 곡면인 distance buffer border의 개념을 정의합니다. 버퍼 영역의 두께는 버퍼 거리입니다. 매니퓰레이터와 모바일 로봇 사이의 거리가 버퍼 거리보다 작은 경우, 즉 매니퓰레이터가 모바일 로봇의 버퍼 영역 내부에 있는 경우 제안된 전략은 매니퓰레이터를 버퍼 영역 밖으로 내보내기 위해 모바일 로봇의 움직임을 생성합니다. 따라서 매니퓰레이터는 미리 정의된 매니퓰레이터의 움직임을 수정하지 않고도 모바일 로봇과의 자가 충돌을 피할 수 있습니다. 모바일 로봇의 움직임은 가상의 힘을 가함으로써 생성됩니다. 특히, 힘의 방향은 차동 구동 이동 로봇의 비홀로노믹 제약 및 조작기의 도달 가능성을 고려하여 결정됩니다. 제안된 알고리즘은 7자유도 로봇팔을 가진 차동 구동 모바일 로봇에 적용하여 다양한 실험 시나리오에서 입증되었습니다.A mobile manipulator is a manipulator mounted on a mobile robot. Compared to a fixed-base manipulator, the mobile manipulator can perform various and complex tasks because the mobility is offered by the mobile robot. However, combining two different systems causes several features to be considered when generating the whole-body motion of the mobile manipulator. The features include redundancy, inertia difference, and non-holonomic constraint. The purpose of this thesis is to propose the whole-body motion generation strategy of the mobile manipulator for considering kinematic and dynamic constraints. First, a planning framework is proposed that computes a path for the whole-body configuration of the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The framework handles the kinematic constraint imposed by the closed-chain between the robot and door. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called area indicator is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the inverse kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator. Second, a whole-body controller is presented based on the optimization method that can consider both equality and inequality constraints. The method computes the optimal solution of the weighted hierarchical optimization problem. The method is developed to resolve the redundancy of robots with a large number of Degrees of Freedom (DOFs), such as a mobile manipulator or a humanoid, so that they can execute multiple tasks with differently weighted joint motion for each task priority. The proposed method incorporates the weighting matrix into the first-order optimality condition of the optimization problem and leverages an active-set method to handle equality and inequality constraints. In addition, it is computationally efficient because the solution is calculated in a weighted joint space with symmetric null-space projection matrices for propagating recursively to a low priority task. Consequently, robots that utilize the proposed controller effectively show whole-body motions handling prioritized tasks with differently weighted joint spaces. The effectiveness of the proposed controller was validated through experiments with a nonholonomic mobile manipulator as well as a humanoid. Lastly, as one of dynamic constraints for the mobile manipulator, a reactive self-collision avoidance algorithm is developed. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, i.e. the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot and smoothly inserted with a top priority into the controller. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.1 INTRODUCTION 1 1.1 Motivation 1 1.2 Contributions of thesis 2 1.3 Overview of thesis 3 2 WHOLE-BODY MOTION PLANNER : APPLICATION TO NAVIGATION INCLUDING DOOR TRAVERSAL 5 2.1 Background & related works 7 2.2 Proposed framework 9 2.2.1 Computing path for mobile robot and door angle - S1 10 2.2.1.1 State 10 2.2.1.2 Action 13 2.2.1.3 Cost 15 2.2.1.4 Search 18 2.2.2 Computing path for arm configuration - S2 20 2.3 Results 21 2.3.1 Application to pull and push-type door 21 2.3.2 Experiment in cluttered environment 22 2.3.3 Experiment with different robot platform 23 2.3.4 Comparison with separate planning by existing works 24 2.3.5 Experiment with real robot 29 2.4 Conclusion 29 3 WHOLE-BODY CONTROLLER : WEIGHTED HIERARCHICAL QUADRATIC PROGRAMMING 31 3.1 Related works 32 3.2 Problem statement 34 3.2.1 Pseudo-inverse with weighted least-squares norm for each task 35 3.2.2 Problem statement 37 3.3 WHQP with equality constraints 37 3.4 WHQP with inequality constraints 45 3.5 Experimental results 48 3.5.1 Simulation experiment with nonholonomic mobile manipulator 48 3.5.1.1 Scenario description 48 3.5.1.2 Task and weighting matrix description 49 3.5.1.3 Results 51 3.5.2 Real experiment with nonholonomic mobile manipulator 53 3.5.2.1 Scenario description 53 3.5.2.2 Task and weighting matrix description 53 3.5.2.3 Results 54 3.5.3 Real experiment with humanoid 55 3.5.3.1 Scenario description 55 3.5.3.2 Task and weighting matrix description 55 3.5.3.3 Results 57 3.6 Discussions and implementation details 57 3.6.1 Computation cost 57 3.6.2 Composite weighting matrix in same hierarchy 59 3.6.3 Nullity of WHQP 59 3.7 Conclusion 59 4 WHOLE-BODY CONSTRAINT : SELF-COLLISION AVOIDANCE 61 4.1 Background & related Works 64 4.2 Distance buffer border and its score computation 65 4.2.1 Identification of potentially colliding link pairs 66 4.2.2 Distance buffer border 67 4.2.3 Evaluation of distance buffer border 69 4.2.3.1 Singularity of the differentially driven mobile robot 69 4.2.3.2 Reachability of the manipulator 72 4.2.3.3 Score of the DBB 74 4.3 Self-collision avoidance algorithm 75 4.3.1 Generation of the acceleration for the mobile robot 76 4.3.2 Generation of the repulsive acceleration for the other link pair 82 4.3.3 Construction of an acceleration-based task for self-collision avoidance 83 4.3.4 Insertion of the task in HQP-based controller 83 4.4 Experimental results 86 4.4.1 System overview 87 4.4.2 Experimental results 87 4.4.2.1 Self-collision avoidance while tracking the predefined trajectory 87 4.4.2.2 Self-collision avoidance while manually guiding the end-effector 89 4.4.2.3 Extension to obstacle avoidance when opening the refrigerator 91 4.4.3 Discussion 94 4.5 Conclusion 95 5 CONCLUSIONS 97 Abstract (In Korean) 113 Acknowlegement 116박

Modular Robots Morphology Transformation And Task Execution

Self-reconfigurable modular robots are composed of a small set of modules with uniform docking interfaces. Different from conventional robots that are custom-built and optimized for specific tasks, modular robots are able to adapt to many different activities and handle hardware and software failures by rearranging their components. This reconfiguration capability allows these systems to exist in a variety of morphologies, and the introduced flexibility enables self-reconfigurable modular robots to handle a much wider range of tasks, but also complicates the design, control, and planning. This thesis considers a hierarchy framework to deploy modular robots in the real world: the robot first identifies its current morphology, then reconfigures itself into a new morphology if needed, and finally executes either manipulation or locomotion tasks. A reliable system architecture is necessary to handle a large number of modules. The number of possible morphologies constructed by modules increases exponentially as the number of modules grows, and these morphologies usually have many degrees of freedom with complex constraints. In this thesis, hardware platforms and several control methods and planning algorithms are developed to build this hierarchy framework leading to the system-level deployment of modular robots, including a hybrid modular robot (SMORES-EP) and a modular truss robot (VTT). Graph representations of modular robots are introduced as well as several algorithms for morphology identification. Efficient mobile-stylereconfiguration strategies are explored for hybrid modular robots, and a real-time planner based on optimal control is developed to perform dexterous manipulation tasks. For modular truss robots, configuration space is studied and a hybrid planning framework (sampling-based and search-based) is presented to handle reconfiguration activities. A non-impact rolling locomotion planner is then developed to drive an arbitrary truss robot in an environment

Design and control of an origami-enabled soft crawling autonomous robot (OSCAR)

Soft mobile robots offer unique benefits as they are highly adaptable to the terrain of travel and safe for interaction with humans. However, the lack of autonomy currently limits their practical applications. Autonomous navigation has been well studied for conventional rigid-bodied robots; however, it is underrepresented in the soft mobile robot research community. Its implementation in soft robots comes with multiple challenges. However, the major challenge is the significant motion uncertainties due to the robot compliance, ground interactions, and limited available sensing. These uncertainties prevent high-level control implementation, such as autonomous navigation, to be performed successfully. Therefore, soft robots require robust design methods, as well as path following and path planning algorithms, to mitigate these uncertainties and enable autonomy. This dissertation develops and implements autonomous navigation for a novel origami-enabled soft crawling autonomous robot called OSCAR. In order to implement autonomous navigation, it first mitigates the OSCAR’s motion uncertainties by a multi-step iterative design process. Analysis has shown that OSCAR’s motion uncertainties are the result of: (i) the ground-feet interaction, (ii) effectiveness of low-level closed-loop control and, (iii) variability in the manufacturing assembly process. The iterative control-oriented design allows a robust and reliable OSCAR performance and enables high-level path following control implementation. To design and implement path following control, this research presents an idealized kinematic model and introduces an empirically based correction to make the model predictions match the experimental data. The dissertation investigates two separate path-following controllers: a model-based pure pursuit and a feedback controller. The controllers are investigated in both simulation and experiment and the need for feedback is clearly demonstrated. Finally, this research presents the path planning in order to complete OSCAR’s autonomous navigation. The simulation and experimental results show that OSCAR can accurately navigate in a 2D environment while avoiding static obstacles. Lastly, the coupled locomotion of multiple OSCARs demonstrates an extension of functionality and expands the potential design and operation space for this promising type of soft robot

Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage