Automation of train cab front cleaning with a robot manipulator

In this letter we present a control and trajectory tracking approach for wiping the train cab front panels, using a velocity controlled robotic manipulator and a force/torque sensor attached to its end effector, without using any surface model or vision-based surface detection. The control strategy consists in a simultaneous position and force controller, adapted from the operational space formulation, that aligns the cleaning tool with the surface normal, maintaining a set-point normal force, while simultaneously moving along the surface. The trajectory tracking strategy consists in specifying and tracking a two dimensional path that, when projected onto the train surface, corresponds to the desired pattern of motion. We first validated our approach using the Baxter robot to wipe a highly curved surface with both a spiral and a raster scan motion patterns. Finally, we implemented the same approach in a scaled robot prototype, specifically designed by ourselves to wipe a 1/8 scaled version of a train cab front, using a raster scan pattern

Singularity analysis and handling towards mobile manipulation

Ph.DDOCTOR OF PHILOSOPH

Modular Relative Jacobian for Dual-Arms and the Wrench Transformation Matrix

A modular relative Jacobian is recently derived and is expressed in terms of the individual Jacobians of stand-alone manipulators. It includes a wrench transformation matrix, which was not shown in earlier expressions. This paper is an experimental extension of that recent work, which showed that at higher angular end-effector velocities the contribution of the wrench transformation matrix cannot be ignored. In this work, we investigate the dual-arm force control performance, without necessarily driving the end-effectors at higher angular velocities. We compare experimental results for two cases: modular relative Jacobian with and without the wrench transformation matrix. The experimental setup is a dual-arm system consisting of two KUKA LWR robots. Two experimental tasks are used: relative end-effector motion and coordinated independent tasks, where a force controller is implemented in both tasks. Furthermore, we show in an experimental design that the use of a relative Jacobian affords less accurate task specifications for a highly complicated task requirement for both end-effectors of the dual-arm. Experimental results on the force control performance are compared and analyzed

Controlling and Learning Constrained Motions for Manipulation in Contact

Many practical tasks in robotic systems involving contact interaction with the environment, such as cleaning windows, writing or grasping, are inherently constrained, in that both the task and the environment impose constraints on the robot’s motion. While constraints from manipulation motions in contact represent a challenge when modelling and controlling such robotic systems, they might also be an opportunity, if exploited for decomposing complex controllers into simpler ones that are easier to design, implement, test and even learn from data. Modelling such systems requires incorporating these constraints in the robot’s dynamic model. In this thesis, I define the class of Task-based Constraints (TbCs) and prove that the forward dynamic models of a constrained system obtained through the Projected Dynamics (PD) and the Operational Space Formulation (OSF) are equivalent. Establishing such equivalence required: reformulating the PD constraint inertia matrix, generalising all its previous distinct algebraic variations; and generalising the OSF to rank deficient constraint Jacobian matrices. This generalization allows us to numerically handle redundant constraints and singular configurations, without having to use different controllers in the vicinity of such configurations. Furthermore, I show that we can recover both operational space control with constraints and the hybrid position/force control in the operational space from a multiple Task-based Constraint abstraction. I then propose a control and trajectory tracking approach for wiping the train cab front panels, using a velocity controlled robotic manipulator and a force/torque sensor attached to its end-effector, without using any surface model or vision-based surface detection. The control strategy consists of a hybrid position/force controller, adapted from the Operational Space Formulation, that aligns the cleaning tool with the surface normal, maintaining a set- point normal force, while simultaneously moving along the surface. The trajectory tracking strategy consists of specifying and tracking a two dimensional path that, when projected onto the train surface, corresponds to the desired pattern of motion. An experiment with the Baxter robot to wipe a highly curved surface with both a spiral and a raster scan motion patterns validates the approach. I also implemented the same approach in a scaled robot prototype, specifically designed to wipe a 1/8 scaled version of a train cab front, using a raster scan pattern. Learning these type of control policies subject to constraints is a challenging problem. This thesis proposes a Constraint-aware Policy Learning (CaPL) method that solves the policy learning problem on redundant robots which execute a policy acting in the null-space of a constraint. This learning approach allows the generalization of learnt control policies across constraints that are unknown during the training phase. The CaPL method splits the combined problem of learning constraints and policies into: first estimating the constraint, and then estimating an unconstrained policy using the remaining degrees of freedom. For a linear parametrization, there is a closed-form solution for the problem of estimating constraints based on Singular Value Decomposition (SVD). In this thesis, I propose another closed-form solution for constraint estimation for the TbC case, which includes estimating the task component without affecting the norm of the constraint matrix, based on Generalized Singular Value Decomposition (GSVD). I also discuss a metric for comparing the similarity of estimated constraints, which is useful to pre-process the trajectories recorded in the demonstrations. An experiment consisting in: learning a wiping task from human demonstration on flat surfaces; and reproducing it on an unknown curved surface using a force/torque based controller, to achieve tool alignment, validates the CaPL method. Despite the differences between the training and validation scenarios, the learnt policy still provides the desired wiping motion

Controlling and learning constrained motions for manipulation in contact

Many practical tasks in robotic systems involving contact interaction with the environment, such as cleaning windows, writing or grasping, are inherently constrained, in that both the task and the environment impose constraints on the robot’s motion. While constraints from manipulation motions in contact represent a challenge when modelling and controlling such robotic systems, they might also be an opportunity, if exploited for decomposing complex controllers into simpler ones that are easier to design, implement, test and even learn from data. Modelling such systems requires incorporating these constraints in the robot’s dynamic model. In this thesis, I define the class of Task-based Constraints (TbCs) and prove that the forward dynamic models of a constrained system obtained through the Projected Dynamics (PD) and the Operational Space Formulation (OSF) are equivalent. Establishing such equivalence required: reformulating the PD constraint inertia matrix, generalizing all its previous distinct algebraic variations; and generalizing the OSF to rank deficient constraint Jacobian matrices. This generalization allows us to numerically handle redundant constraints and singular configurations, without having to use different controllers in the vicinity of such configurations. Furthermore, I show that we can recover both operational space control with constraints and the hybrid position/force control in the operational space from a multiple Task-based Constraint abstraction. I then propose a control and trajectory tracking approach for wiping the train cab front panels, using a velocity controlled robotic manipulator and a force/torque sensor attached to its end-effector, without using any surface model or vision-based surface detection. The control strategy consists of a hybrid position/force controller, adapted from the Operational Space Formulation, that aligns the cleaning tool with the surface normal, maintaining a setpoint normal force, while simultaneously moving along the surface. The trajectory tracking strategy consists of specifying and tracking a two dimensional path that, when projected onto the train surface, corresponds to the desired pattern of motion. An experiment with the Baxter robot to wipe a highly curved surface with both a spiral and a raster scan motion patterns validates the approach. I also implemented the same approach in a scaled robot prototype, specifically designed to wipe a 1/8 scaled version of a train cab front, using a raster scan pattern. Learning these type of control policies subject to constraints is a challenging problem. This thesis proposes a Constraint-aware Policy Learning (CaPL) method that solves the policy learning problem on redundant robots which execute a policy acting in the null-space of a constraint. This learning approach allows the generalization of learnt control policies across constraints that are unknown during the training phase. The CaPL method splits the combined problem of learning constraints and policies into: first estimating the constraint, and then estimating an unconstrained policy using the remaining degrees of freedom. For a linear parametrization, there is a closed-form solution for the problem of estimating constraints based on Singular Value Decomposition (SVD). In this thesis, I propose another closed-form solution for constraint estimation for the TbC case, which includes estimating the task component without affecting the norm of the constraint matrix, based on Generalized Singular Value Decomposition (GSVD). I also discuss a metric for comparing the similarity of estimated constraints, which is useful to pre-process the trajectories recorded in the demonstrations. An experiment consisting in: learning a wiping task from human demonstration on flat surfaces; and reproducing it on an unknown curved surface using a force/torque based controller, to achieve tool alignment, validates the CaPL method. Despite the differences between the training and validation scenarios, the learnt policy still provides the desired wiping motion.James-Watt Scholarshi

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

Neural Network Adaptive Force and Motion Control of Robot Manipulators in the Operational Space Formulation

Ph.DDOCTOR OF PHILOSOPH