Carlos J. Rosales

Synthesizing grasp configurations with specified contact regions

In this paper we present a new method to solve the configuration problem on robotic hands: determining how a hand should be configured so as to grasp a given object in a specific way, characterized by a number of hand—object contacts to be satisfied. In contrast to previous algorithms given for the same purpose, the method presented here allows such contacts to be specified between free-form regions on the hand and object surfaces, and always returns a solution whenever one exists. The method is based on formulating the problem as a system of polynomial equations of special form, and then exploiting this form to isolate the solutions, using a numerical technique based on linear relaxations. The approach is general, in the sense that it can be applied to any grasping mechanism involving lower-pair joints, and it can accommodate as many hand—object contacts as required. Experiments are included that illustrate the performance of the method in the particular case of the Schunk Anthropomorphic hand.

Study of coordinated motions of the human hand for robotic applications

This paper presents an acquisition method that comprehensively looks for the mimic configurations of the human hand. The data obtained through this process is further analyzed, transformed, and then used to synthesize a reduced configuration space of a robot anthropomorphic hand. The method rely on a dimensionality reduction technique that provides a new basis of the full configuration space, from which one can select a subset of the vectors forming that basis, and finally obtaining a simpler configuration subspace. These vectors are called Principal Motion Directions, and represent the coordinated motions captured by a sensorized glove on a human hand and transferred to the robot hand. The characteristics and limitations of the subspace are discussed, as well as its application in several scenarios within robotics such as the motion planning of robot hands, where the subspace has been successfully implemented and executed.

Finding all valid hand configurations for a given precision grasp

Planning a precision grasp for a robot hand is usually decomposed into two main steps. First, a set of contact points over the object surface must be determined, ensuring they allow a stable grasp. Second, the inverse kinematics of the robot hand must be solved to verify whether the contact points can actually be reached. Whereas the first problem has been largely solved in a general posing, the second one has only been tackled with local convergence methods. These methods only provide one solution to the problem, even if many are possible, and depending on the initial estimation they use, they may fail to converge, which results in grasp re-planning in situations where it could be avoided. This paper overcomes both issues by providing a complete method to solve the kinematics of human-like hands. The method is able to find all possible configurations that reach the specified contact points, even when positive-dimensional sets of such configurations are possible.

The CUIK suite: analyzing the motion closed-chain multibody systems

Many situations in robotics require the analysis of the motions of complex multibody systems. These are sets of articulated bodies arising in a variety of devices, including parallel manipulators, multifingered hands, or reconfigurable mechanisms, but they appear in other domains too as mechanical models of molecular compounds or nanostructures. Closed kinematic chains arise frequently in such systems, either due to their morphology or due to geometric or contact constraints to fulfill during operation, giving rise to configuration spaces of an intricate structure. Despite appearing very often in practice, there is a lack of general software tools to analyze and represent such configuration spaces. Existing packages are oriented either to open-chain systems or to specific robot types, which hinders the analysis and development of innovative manipulators. This article describes the CUIK suite, a software toolbox for the kinematic analysis of general multibody systems. The implemented tools can isolate the valid configurations, determine the motion range of the whole multibody system or of some of its parts, detect singular configurations leading to control or dexterity issues, or find collision- and singularity-free paths between configurations. The toolbox has applications in robot design and programming and is the result of several years of research and development within the Kinematics and Robot Design group at IRI, Barcelona. It is available under GPLv3 license from http://www.iri.upc.edu/cuik.

On the synthesis of feasible and prehensile robotic grasps

This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers.

Motion planning for high DOF anthropomorphic hands

The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions. The proposed approach tries to mimic the real human hand motions, but reducing the dimension of the search space in order to obtain results as a compromise between motion optimality and planning complexity (time) by means of the concept of principal motion directions. Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a sampling-based roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand, and some examples are included to illustrate its validity.

On the Problem of Moving Objects With Autonomous Robots: A Unifying High-Level Planning Approach

Moving objects with autonomous robots is a wide topic that includes single-arm pick-and-place tasks, object regrasping, object passing between two or more arms in the air or using support surfaces such as tables and similar. Each task has been extensively studied and many planning solutions are already present in the literature. In this letter, we present a planning scheme which, based on the use of pre-defined elementary manipulation skills, aims to unify solutions which are usually obtained by means of different planning strategies rooted on hard-coded behaviors. Both robotic manipulators and environment fixed support surfaces are treated as end-effectors of movable and non-movable types, respectively. The task of the robot can thus be broken down into elementary building blocks, which are end-effector manipulation skills, that are then planned at the kinematic level. Feasibility is ensured by propagating unforeseen low-level failures at the higher level and by synthesizing different behaviors. The validity of the proposed solution is shown via experiments on a bimanual robot setup and in simulations involving a more complex setup similar to an assembly line.

Grasp Optimization Under Specific Contact Constraints

This paper presents a procedure for synthesizing high-quality grasps for objects that need to be held and manipulated in a specific way, characterized by a prespecified set of contact constraints to be satisfied. Due to the multimodal nature of typical grasp quality measures, approaches that resort to local optimization methods are likely to get trapped into local extrema on such a problem. An additional difficulty is that the set of feasible grasps is a highly dimensional manifold, implicitly defined by a system of nonlinear equations. The proposed procedure finds a way around these issues by focusing the exploration on a relevant subset of grasps of lower dimension and tracing this subset exhaustively using a higher-dimensional continuation technique. A detailed atlas of the subset is obtained as a result, on which the highest quality grasp, according to any desired criterion, or a combination of criteria, can be readily identified. Examples are included that illustrate the application of the method to a three-fingered planar hand and to the Schunk anthropomorphic hand grasping different objects, using several quality indices.

Efficient search of obstacle-free paths for anthropomorphic hands

The planning of collision-free motions of a hand-arm system to reach a grasp or preshape configuration is not a simple issue due to the high number of involved degrees of freedom. This paper presents an efficient sampling-based path planner that copes with this issue by considering a reduced search space. The dimension of this space is not fixed but it is iteratively increased according to the difficulty of the task at hand. Initially the search space is 1-dimensional along the line defined by the initial and goal hand configurations (by construction those configurations always belong to the search space), and then its dimension is increased by iteratively adding principal motion directions (that couple the finger motions), trying in this way to produce hand movements through anthropomorphic natural postures.

Active gathering of frictional properties from objects

This work proposes a representation that comprises both shape and friction, as well as the exploration strategy to gather them from an object. The representation is developed under a common probabilistic framework, particularly it uses a Gaussian Process to approximate the distribution of the friction coefficient over the surface, also represented as a Gaussian Process. The surface model is exploited to compute straight lines (geodesic flows) that guide the exploration. The exploration follows these flows by employing an impedance controller in pursuance of safety, shape accommodation and contact enforcement, while measuring the necessary data to estimate the friction coefficient. The exploratory probes consist of an RGBD camera and an Intrinsic Tactile sensor (ITs) mounted on a robotic arm. Experimental results give evidence for the effectiveness of the algorithm in the friction coefficient gathering and enrichment of the object representation.