Improving local trajectory optimization by enhanced initialization and global guidance

Trajectory optimization is a promising method for planning trajectories of robotic manipulators. With the increasing success of collaborative robots in dynamic environments, the demand for online planning methods grows and offers new opportunities as well as challenges for trajectory optimization. Special requirements in terms of real-time capabilities are one of the greatest difficulties. Optimizing a short planning horizon instead of an entire trajectory is one approach to reduce computation time, which nonetheless separates the optimality of local and global solutions. This contribution introduces, on the one hand, Extended Initialization as a new approach that reduces the risk of local minima and aims at improving the quality of the global trajectory. On the other hand, the particularly critical cases in which local solutions lead to standstills are mitigated by globally guiding local solutions. The evaluation performs four experiments with comparisons to Stochastic Trajectory Optimization for Motion Planning (STOMP) or Probabilistic Roadmap Method (PRM*) and demonstrates the effectiveness of both approaches

A computational method for key-performance-indicator-based parameter identification of industrial manipulators

We present a novel derivative-based parameter identification method to improve the precision at the tool center point of an industrial manipulator. The tool center point is directly considered in the optimization as part of the problem formulation as a key performance indicator. Additionally, our proposed method takes collision avoidance as special nonlinear constraints into account and is therefore suitable for industrial use. The performed numerical experiments show that the optimum experimental designs considering key performance indicators during optimization achieve a significant improvement in comparison to other methods. An improvement in terms of precision at the tool center point of 40% to 44% was achieved in experiments with three KUKA robots and 90 notional manipulator models compared to the heuristic experimental designs chosen by an experimenter as well as 10% to 19% compared to an existing state-of-the-art method

Memory Clustering Using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-Starts

Shooting methods are an efficient approach to solving nonlinear optimal control problems. As they use local optimization, they exhibit favorable convergence when initialized with a good warm-start but may not converge at all if provided with a poor initial guess. Recent work has focused on providing an initial guess from a learned model trained on samples generated during an offline exploration of the problem space. However, in practice the solutions contain discontinuities introduced by system dynamics or the environment. Additionally, in many cases multiple equally suitable, i.e., multi-modal, solutions exist to solve a problem. Classic learning approaches smooth across the boundary of these discontinuities and thus generalize poorly. In this work, we apply tools from algebraic topology to extract information on the underlying structure of the solution space. In particular, we introduce a method based on persistent homology to automatically cluster the dataset of precomputed solutions to obtain different candidate initial guesses. We then train a Mixture-of-Experts within each cluster to predict state and control trajectories to warm-start the optimal control solver and provide a comparison with modality-agnostic learning. We demonstrate our method on a cart-pole toy problem and a quadrotor avoiding obstacles, and show that clustering samples based on inherent structure improves the warm-start quality.Comment: 12 pages, 10 figures, accepted as a regular paper in IEEE Transactions on Robotics (T-RO). Supplementary video: https://youtu.be/lUULTWCFxY8 Code: https://github.com/wxmerkt/topological_memory_clustering The first two authors contributed equall

COMPUTATION OF THE SHORTEST DISTANCE BETWEEN TWO PARAMETRIC DEFINED OBJECTS BY PARTICLE SWARM OPTIMIZATION

The distance computation between objects is an essential component of robot motion planning and controlling the robot to avoid its surrounding obstacles. Distance is used as a measure of how far a robot is from colliding with an obstacle. In this paper a Particle Swarm Optimization algorithm (PSO) for solving the problem of the distance computation between convex objects is presented. Convergence analysis of the suggested method was done via difference equation

A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods

Accurate frictional contact is critical in simulating the assembly of rod-like structures in the practical world, such as knots, hairs, flagella, and more. Due to their high geometric nonlinearity and elasticity, rod-on-rod contact remains a challenging problem tackled by researchers in both computational mechanics and computer graphics. Typically, frictional contact is regarded as constraints for the equations of motions of a system. Such constraints are often computed independently at every time step in a dynamic simulation, thus slowing down the simulation and possibly introducing numerical convergence issues. This paper proposes a fully implicit penalty-based frictional contact method, Implicit Contact Model (IMC), that efficiently and robustly captures accurate frictional contact responses. We showcase our algorithm's performance in achieving visually realistic results for the challenging and novel contact scenario of flagella bundling in fluid medium, a significant phenomenon in biology that motivates novel engineering applications in soft robotics. In addition to this, we offer a side-by-side comparison with Incremental Potential Contact (IPC), a state-of-the-art contact handling algorithm. We show that IMC possesses comparable performance to IPC while converging at a faster rate.Comment: * Equal contribution. A video summarizing this work is available on YouTube: https://youtu.be/g0rlCFfWJ8

Model predictive control of a collaborative manipulator considering dynamic obstacles

Collaborative robots have to adapt its motion plan to a dynamic environment and variation of task constraints. Currently, they detect collisions and interrupt or postpone their motion plan to prevent harm to humans or objects. The more advanced strategy proposed in this article uses online trajectory optimization to anticipate potential collisions, task variations, and to adapt the motion plan accordingly. The online trajectory planner pursues a model predictive control approach to account for dynamic motion objectives and constraints during task execution. The prediction model relates reference joint velocities to actual joint positions as an approximation of built-in robot tracking controllers. The optimal control problem is solved with direct collocation based on a hypergraph structure, which represents the nonlinear program and allows to efficiently adapt to structural changes in the optimization problem caused by moving obstacles. To demonstrate the effectiveness of the approach, the robot imitates pick-and-place tasks while avoiding self-collisions, semistatic, and dynamic obstacles, including a person. The analysis of the approach concerns computation time, constraint violations, and smoothness. It shows that after model identification, order reduction, and validation on the real robot, parallel integrators with compensation for input delays exhibit the best compromise between accuracy and computational complexity. The model predictive controller can successfully approach a moving target configuration without prior knowledge of the reference motion. The results show that pure hard constraints are not sufficient and lead to nonsmooth controls. In combination with soft constraints, which evaluate the proximity of obstacles, smooth and safe trajectories are planned

가변 토폴로지 트러스 로봇의 안정적인 주행 알고리즘 개발

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계공학과, 2020. 8. 김종원.Variable Topology Truss (VTT) is truss structured modular robot that can self-reconfigure its topology and geometric configuration, which can be usefully applied to rescuing work in disaster site. In this thesis, design of VTT is introduced and stable rolling locomotion algorithm for VTT is proposed. To achieve self-reconfiguration feature, VTT are composed specially designed members and nodes. VTTs members consist of Spiral Zippers which are novel linear actuators that has high extension ratio, light weight and high strength. VTTs nodes consist of Passive Member-Ends and Master Member-Ends. Passive Member-Ends are linkage type spherical joint with large angle range that can accommodate many members. Master Member-Ends are spherical manipulators that built in Sphere and it move member to change topology of VTT. Rolling locomotion of VTT is achieved by controlling the center of mass by geometric reconfiguration. However, the locomotion planning is complex problem, because VTT is parallel mechanism with high degree of freedom and many constraints, which makes it difficult to predict and avoid constraints for feasible planning. Thus, it needs stable algorithm that can find locomotion trajectory even in complicated and large environment. In addition, since VTT has many sophisticated components, the algorithm must prevent VTT being damaged from ground by tumbling. To meet the requirements, proposed locomotion algorithm is composed of 3 steps; support polygon planning, center of mass planning and node position planning. In support polygon planning, support polygon path is planned by newly proposed random search algorithm, Polygon-Based Random Tree (PRT). In center of mass planning, trajectory of desired projected center of mass is planned by maximizing stability feature. Planned support polygon path and center of mass trajectory guide VTT to have good-conditioned shape which configuration is far from constraints and makes locomotion planning success even in complex and large environment. In node position planning, Non-Impact Rolling locomotion algorithm was developed to plan position of VTTs nodes that prevent damage from the ground while following planned support polygon path and center of mass trajectory. The algorithm was verified by two case study. In case study 1, locomotion planning and simulation was performed considering actual constraints of VTT. To avoid collision between VTT and obstacle, safety space was defined and considered in support polygon planning. The result shows that VTT successfully reaches the goal while avoiding obstacles and satisfying constraints. In case study 2, locomotion planning and simulation was performed in the environment having wide space and narrow passage. Nominal length of VTT was set to be large in wide space to move efficiently, and set to be small in narrow passage to pass through it. The result shows that VTT successfully reaches the goal while changing its nominal length in different terrain.가변 토폴로지 트러스 (Variable Topology Truss, VTT)는 토폴로지와 기하학적 형상의 재구성이 가능한 트러스 구조의 모듈 로봇이다. 본 논문에서는 VTT의 설계 구조를 소개하고 VTT의 안정적인 주행을 알고리즘을 제안한다. VTT는 토폴로지와 기하학적 형상의 재구성을 위해 특수한 구조의 멤버와 노드를 가진다. VTT의 멤버는 높은 압축비, 가벼운 중량, 높은 강도를 가진 신개념 선형 구동기인 스파이럴 지퍼로 구성되어 있다. VTT의 노드는 패시브 멤버 엔드와 마스터 엔드로 구성되어 있다. 패시브 멤버는 링키지 구조의 3 자유도 관절로, 넓은 각도 구동 범위를 가지고 있고 많은 수의 멤버를 연결할 수 있다. 마스터 멤버 엔드는 노드 부의 내장된 구형 매니퓰레이터로, 토폴로지 재구성 시 멤버를 이동시키는데 사용된다. VTT는 기하학적 형상을 변화하여 구르는 움직임을 통해 주행한다. VTT의 주행 알고리즘은 서포트 폴리곤 계획 단계, 무게 중심 계획 단계, 노드 위치 계획 단계로 이루어진다. 서포트 폴리곤 계획 단계에서는 새롭게 제안된 무작위 탐색 (random search) 알고리즘인 Polygon-Based Random Tree (PRT)을 적용해 서포트 폴리곤의 경로를 계획한다. 무게 중심 계획 단계에서는 안정성을 최대화하는 VTT의 무게 중심 궤적을 계획한다. 계획된 서포트 폴리곤 경로와 무게 중심 궤적을 VTT가 제한 조건으로부터 먼 좋은 상태의 형상을 유지하게 하여 복잡한 환경에 대해서도 경로 계획이 실패하지 않고 안정적으로 이루어질 수 있도록 한다. 노드 위치 계획 단계에서는 서포트 폴리곤 경로와 노드 위치의 궤적을 추종하는 노드 위치 궤적을 계획한다. 이 과정에서 비충격 롤링 이동 알고리즘 (Non-Impact Rolling locomotion algorithm)을 적용하여 지면과의 충돌로 인한 충격이 일어나지 않는 궤적을 계획한다. 실제 VTT의 제한 조건을 반영한 모델에 본 알고리즘을 적용하여 시뮬레이션을 수행한 결과, VTT가 모든 제한 조건을 만족하고 장애물을 회피하면서 목표 지점에 도달할 수 있음을 확인하였다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Previous Truss Type Modular Robot 4 1.3 Previous Research on VTTs Locomotion 8 1.3.1 Heuristic Based Methods 9 1.3.2 Optimization Based Method 10 1.4 Objectives of Locomotion Algorithm 12 1.5 Contribution of Thesis 13 1.5.1 Introduction to Hardware Design of VTT 13 1.5.2 Stable Rolling Locomotion of VTT 15 Chapter 2. Design of Variable Topology Truss 17 2.1 Member Design 18 2.1.1 Spiral Zipper 20 2.1.2 Tensioner 26 2.2 Node Design 28 2.2.1 Passive Member-End and Sphere 29 2.2.2 Master Member-End 36 2.3 Control System 40 2.4 Node Position Control Experiment 44 Chapter 3. Mathematical Model of Variable Topology Truss 47 3.1 Configuration and Terminology 47 3.2 Inverse Kinematics 50 3.3 Constraints 51 3.4 Stability Criteria 64 Chapter 4. Locomotion Algorithm 66 4.1 Concept of Locomotion Algorithm 67 4.1.1 Method for Successful Planning and Obstacle Avoidance 67 4.1.2 Method to Prevent Damage from the Ground 71 4.1.3 Step of Locomotion Algorithm 72 4.2 Support Polygon Planning 73 4.2.1 Polygon-Based Random Tree (PRT) Algorithm 73 4.2.2 Probabilistic Completeness of PRT Algorithm 79 4.3 Center of Mass Planning 85 4.4 Node Position Planning 86 4.4.1 Concept of Non-Impact Rolling Locomotion 86 4.4.2 Planning Algorithm for Non-Impact Rolling Locomotion 89 4.4.3 Optimization Problem of Moving Phase 94 4.4.4 Optimization Problem of Landing Phase 98 4.4.5 Optimization Problem of Transient Phase 99 Chapter 5. Experimental Verification 100 5.1 Case Study 1: Actual VTT Prototype 101 5.1.1 Simulation Condition 101 5.1.2 Obstacle Avoidance Method 103 5.1.3 Simulation Result 104 5.2 Case Study 2: Environment with Narrow Passage 111 5.2.1 Simulation Condition 111 5.2.2 Support Polygon Planning with Varying Nominal Length 114 5.2.3 Simulation Result 117 Chapter 6. Conclusion 126 Bibliography 129 Abstract in Korean 134Docto

Application of Particle Transfer by Dipping Using Polymer Binder

The demand for particle transfer is increasing in various industries, such as manufacturing, metal joining, microfluidics, roller lubrication process, fuel cells, super-capacitors, hybrid coating, and protective layer applications. As a result, the importance of efficient transfer of solid micron-size particles is becoming more crucial. Submicron-sized particles can easily adhere to solid substrates due to negligible gravitational force, while micron-sized or larger particles require a binder to overcome the gravitational effects. This thesis aims to investigate the interactions between microparticles and polymer thin film on cylindrical substrates using particle transfer methods. The process parameters are optimized and demonstrated two applications of this process: sorting particles based on their size from poly-disperse particle mixtures and controlling the friction force of the rods. To transfer particles into a substrate, a density-mismatching heterogeneous suspension is utilized, where kinetic energy is supplied by a magnetic stirrer’s rotation to keep the particles suspended during transfer. Initially, the effect of magnetic stirrer rotation and binder concentration on the optimal particle transfer was investigated. As a result of optimizing process parameters, a novel technique was developed for filtering poly-disperse particles from density mismatching heterogeneous mixtures at the solid-liquid interface (submerged condition) using entrapment instead of the conventional entrainment approach used in dip-coating processes. The polymer layer thickness formed over the substrate is controlled by controlling the binder concentration in the suspension. The binder concentration is varied from ϕb = 1% to ϕb = 13% at different intervals and the particle concentration is kept fixed ϕp = 10%. The viscosity is measured at room temperature (25 ºC) to observe the behavior of the suspension using a rotational rheometer. The variation in the polymer layer thickness controls the size of the entrapped particles. This work successfully showed the size-based separation of particles from a poly-disperse particle mixture. Another aspect of this thesis involved the systematic control of frictional forces between elastic rods in contact by transferring particles via dip-coating. Non-spherical particles adhere to the rods using a polymeric binder. A custom continuous dip-coating setup was constructed in the laboratory to coat the elastic rods. The particle delivery over the rods is regulated by controlling the concentration of particles in the suspension. Particle concentration in the suspension is varied from ϕp = 1% to ϕp = 13% at different intervals to observe the effect of variation of particle concentration keeping the binder concentration fixed (ϕb = 5%). The coated rods are dried in the oven to overcome the effect of the solvent during the friction force measurement. Table-top experimental setup with a push-pull digital force gauge is used to measure the variation friction force at different pulling lengths of overhand knots with a variety of unknotting numbers. This work successfully demonstrates a novel method of controlling the friction force of elastic rods by controlling the particle concentration in the suspension

Gesture Recognition and Control for Semi-Autonomous Robotic Assistant Surgeons

The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This thesis explores the solutions adopted in pursuing automation in robotic minimally-invasive surgeries (R-MIS) and presents a novel cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller