Survey on model-based manipulation planning of deformable objects

A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin

Quality and productivity driven trajectory optimisation for robotic handling of compliant sheet metal parts in multi-press stamping lines

This paper investigates trajectory generation for multi-robot systems that handle compliant parts in order to minimise deformations during handling, which is important to reduce the risk of affecting the part’s dimensional quality. An optimisation methodology is proposed to generate deformation-minimal multi-robot coordinated trajectories for predefined robot paths and cycle-time. The novelty of the proposed optimisation methodology is that it efficiently estimates part deformations using a precomputed Response Surface Model (RSM), which is based on data samples generated by Finite Element Analysis (FEA) of the handled part and end-effector. The end-effector holding forces, plastic part deformations, collision-avoidance and multi-robot coordination are also considered as constraints in the optimisation model. The optimised trajectories are experimentally validated and the results show that the proposed optimisation methodology is able to significantly reduce the deformations of the part during handling, i.e. up to 12% with the same cycle-time in the case study that involves handling compliant sheet metal parts. This investigation provides insights into generating specialised trajectories for material handling of compliant parts that can systematically minimise part deformations to ensure final dimensional quality

Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201

Particle Computation: Complexity, Algorithms, and Logic

We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal (such as gravity or a magnetic field). We show that a maze of obstacles to the environment can be used to create complex systems. We provide a wide range of results for a wide range of questions. These can be subdivided into external algorithmic problems, in which particle configurations serve as input for computations that are performed elsewhere, and internal logic problems, in which the particle configurations themselves are used for carrying out computations. For external algorithms, we give both negative and positive results. If we are given a set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial configuration of unit-sized particles can be transformed into a desired target configuration. Moreover, we show that finding a control sequence of minimum length is PSPACE-complete. We also work on the inverse problem, providing constructive algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations. For internal logic, we investigate how arbitrary computations can be implemented. We demonstrate how to encode dual-rail logic to build a universal logic gate that concurrently evaluates and, nand, nor, and or operations. Using many of these gates and appropriate interconnects, we can evaluate any logical expression. However, we establish that simulating the full range of complex interactions present in arbitrary digital circuits encounters a fundamental difficulty: a fan-out gate cannot be generated. We resolve this missing component with the help of 2x1 particles, which can create fan-out gates that produce multiple copies of the inputs. Using these gates we provide rules for replicating arbitrary digital circuits.Comment: 27 pages, 19 figures, full version that combines three previous conference article

Grasp plannind under task-specific contact constraints

Several aspects have to be addressed before realizing the dream of a robotic hand-arm system with human-like capabilities, ranging from the consolidation of a proper mechatronic design, to the development of precise, lightweight sensors and actuators, to the efficient planning and control of the articular forces and motions required for interaction with the environment. This thesis provides solution algorithms for a main problem within the latter aspect, known as the {\em grasp planning} problem: Given a robotic system formed by a multifinger hand attached to an arm, and an object to be grasped, both with a known geometry and location in 3-space, determine how the hand-arm system should be moved without colliding with itself or with the environment, in order to firmly grasp the object in a suitable way. Central to our algorithms is the explicit consideration of a given set of hand-object contact constraints to be satisfied in the final grasp configuration, imposed by the particular manipulation task to be performed with the object. This is a distinguishing feature from other grasp planning algorithms given in the literature, where a means of ensuring precise hand-object contact locations in the resulting grasp is usually not provided. These conventional algorithms are fast, and nicely suited for planning grasps for pick-an-place operations with the object, but not for planning grasps required for a specific manipulation of the object, like those necessary for holding a pen, a pair of scissors, or a jeweler's screwdriver, for instance, when writing, cutting a paper, or turning a screw, respectively. To be able to generate such highly-selective grasps, we assume that a number of surface regions on the hand are to be placed in contact with a number of corresponding regions on the object, and enforce the fulfilment of such constraints on the obtained solutions from the very beginning, in addition to the usual constraints of grasp restrainability, manipulability and collision avoidance. The proposed algorithms can be applied to robotic hands of arbitrary structure, possibly considering compliance in the joints and the contacts if desired, and they can accommodate general patch-patch contact constraints, instead of more restrictive contact types occasionally considered in the literature. It is worth noting, also, that while common force-closure or manipulability indices are used to asses the quality of grasps, no particular assumption is made on the mathematical properties of the quality index to be used, so that any quality criterion can be accommodated in principle. The algorithms have been tested and validated on numerous situations involving real mechanical hands and typical objects, and find applications in classical or emerging contexts like service robotics, telemedicine, space exploration, prosthetics, manipulation in hazardous environments, or human-robot interaction in general

The Development of a Sensitive Manipulation End Effector

This thesis designed and realized a two-degree of freedom wrist and two finger manipulator that completes the six-degree of freedom Sensitive Manipulation Platform, the arm of which was previously developed. This platform extends the previous research in the field of robotics by covering not only the end effector with deformable tactile sensors, but also the links of the arm. Having tactile sensors on the arm will improve the dynamic model of the system during contact with its environment and will allow research in contact navigation to be explored. This type of research is intended for developing algorithms for exploring dynamic environments. Unlike traditional robots that focus on collision avoidance, this platform is designed to seek out contact and use it to gather important information about its surroundings. This small desktop platform was designed to have similar proportions and properties to a small human arm. These properties include compliant joints and tactile sensitivity along the lengths of the arms. The primary applications for the completed platform will be research in contact navigation and manipulation in dynamic environments. However, there are countless potential applications for a compliant arm with increased tactile feedback, including prosthetics and domestic robotics. This thesis covers the details behind the design, analysis, and evaluation of the two degrees of the Wrist and two two-link fingers, with particular attention being given to the integration of series elastics actuators, the decoupling of the fingers from the wrist, and the incorporation of tactile sensors in both the forearm motor module and fingers

Improving Strength and Stability in Continuum Robots

Continuum robots, which are bio-inspired ’trunk-like’ robots, are characterized for their inherent compliance and range of motion. One of the key challenges in continuum robotics research is developing robots with sufficient strength and stability without adding additional weight or complexity to the design. The research conducted in this dissertation encompasses design and modeling strategies that address these challenges in strength and stability. This work improves three continuum robot actuation paradigms: (1) tendon-driven continuum robots (TDCR), (2) concentric tube robots (CTR), and (3) concentric push-pull robots (CPPR). The first chapter of contribution covers strategies for improving strength in TDCRs. The payload capacity and torsional stiffness of the robot can be improved by leveraging the geometry of the backbone design and tendon routing, with design choices experimentally validated on a robot prototype. The second chapter covers a new bending actuator, concentric precurved bellows (CPB), that are based upon CTR actuation. The high torsional stiffness of bellows geometry virtually eliminates the torsional compliance instability found in CTRs. Two bellows designs are developed for 3D printing and the mechanical properties of these designs are characterized through experiments on prototypes and in static finite element analysis. A torsionally rigid kinematic model is derived and validated on 3D printed prototypes. The third chapter of contribution covers the development and validation of a mechanics-based CPPR kinematics model. CPPRs are constructed from concentrically nested, asymmetrically patterned tubes that are fixed together at their distal tips. Relative translations between the tubes induces bending shapes from the robot. The model expands the possible design space of CPPRs by enabling the modeling of external loads, non-planar bending shapes, and CPPRs with more than two tubes. The model is validated on prototypes in loaded and unloaded experiments