Finding patterns in time series is crucial to understanding physical or physiological phenomena monitored with sensors. Convolutional sparse coding (CSC) methods, which approximate signals by a sparse combination of short signal templates (also called atoms), are well-suited for this task. Nevertheless, sparsity results in intractable non-convex optimization problems. This paper introduces an algorithm, based on Multi-path Matching Pursuit, which is novel in convolutional settings, to efficiently and accurately estimate atoms’ localizations in time series. We describe a principled way to improve this greedy procedure by returning several candidate solutions instead of one. Our approach yields better localization and signal reconstruction on simulated data and in a real-world use case, which consists in automatically detect damages, such as cracks and broken wires, in overhead power lines.

In this paper, we propose a novel idea to address the problem of fast computation of stable force-closure grasp configurations for a multifingered hand and a 3-D rigid object represented as a polygonal soup model. The proposed method performs a low-level shape exploration by wrapping multiple cords around the object in order to quickly isolate promising grasping regions. Around these regions, we compute grasp configurations by applying a variant of the close-until-contact procedure to find the contact points. The finger kinematics and the contact information are then used to filter out unstable grasps. Through many simulated examples with three different anthropomorphic hands, we demonstrate that, compared with previous grasp planners such as the generic grasp planner in Simox, the proposed grasp planner can synthesize grasps that are more natural-looking for humans (as measured by the grasp quality measure skewness) for objects with complex geometries in a short amount of time. Unlike many other planners, this is achieved without costly model preprocessing such as segmentation by parts and medial axis extraction.

In this paper, we propose a novel idea to address the problem of fast computation of enveloping grasp configurations for a multi-fingered hand with 3D polygonal models represented as polygon soups. The proposed method performs a low-level shape matching by wrapping multiple cords around an object in order to quickly isolate promising grasping spots. From these spots, hand palm posture can be computed followed by a standard close-until-contact procedure to find the contact points. Along with the contacts information, the finger kinematics is then used to filter the unstable grasps. Through multiple simulated examples with a twelve degrees-of-freedom anthropomorphic hand, we demonstrate that our method can compute good grasps for objects with complex geometries in a short amount of time. Best of all, this is achieved without complex model preprocessing like segmentation by parts and medial axis extraction.

This paper presents a new method to compute enveloping grasps with a multi-fingered robotic hand. The method is guided by the idea that a good grasp should maximize the contact surface between the held object and the hand's palmar surface. Starting from a given hand pregrasp configuration, the proposed method finds the hand poses that maximize this surface similarity. We use a surface descriptor that is based on a geodesic measure and on a continuous representation of the surfaces, unlike previous shape matching methods that rely on the Euclidean distance and/or discrete representation (e.g. random point set). Using geodesic contours to describe local surfaces enables us to detect details such as a handle or a thin part. Once the surface matching returns a set of hand poses, sorted by similarity, a second step is performed to adjust the hand configuration with the purpose of eliminating penetration of the object. Lastly, the grasp stability is tested in order to definitely validate the candidate grasps.

This paper proposes a planning framework to deal with the problem of computing the motion of a robot with dual arm/hand, during an object pick-and-place task. We consider the situation where the start and goal configurations of the object constrain the robot to grasp the object with one hand, to give it to the other hand, before placing it in its final configuration. To realize such a task, the proposed framework treats the grasp computation, for one or two multi-fingered hands, of an arbitrarily-shaped object, the exchange configuration and finally the motion of the robot arms and body. In order to improve the planner performance, a context-independent grasp list is computed offline for each hand and for the given object as well as computed offline roadmap that will be adapted according to the environment composition. Simulation results show the planner performance on a complex scenario.

In this paper, we propose a new method for the motion planning problem of rigid object dexterous manipulation with a robotic multi-fingered hand, under quasi-static movement assumption. This method computes both object and finger trajectories as well as the finger relocation sequence. Its specificity is to use a special structuring of the research space that allows to search for paths directly in the particular subspace GSnwhich is the subspace of all the grasps that can be achieved with n grasping fingers. The solving of the dexterous manipulation planning problem is based upon the exploration of this subspace. The proposed approach captures the connectivity of GSnin a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method.

This paper addresses the motion planning problem of the dexterous manipulation of 3D rigid objects by a robotic multi-fingered hand. We propose a novel approach based on probabilistic roadmap techniques. Inspired by the theory developed by Alami et al. (1994), Simeon et al. (2003), the planner relies on a topological property that characterizes the existence of solutions in GSn, a specific manifold of the configuration space. This property leads to reduce the problem by structuring the search-space. It allows us to design a manipulation planner that directly captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. The proposed method allows a global planning - both object and fingers trajectories are computed - that can cope with the obstacle presence in the environment. Collisions between different fingers or between object and fingers elsewhere than fingertips are avoided. Force closure constraints are taken into account to ensure the computed paths physical feasibility, under quasi-static motion assumption. First experiments demonstrate the feasibility and the efficiency of the approach

This paper presents a new solution for solving the grasping force optimization problem, fundamental in dexterous manipulation by multifingered robotic hands. Several methods have been proposed in the literature, yielding optimal solutions, with either recursive or non linear programming techniques. However, most of them involve many computations and cannot be used online. Furthermore, they do not offer a smooth solution regarding to possible changes in the contact conditions due to finger rolling or gaiting, or in the desired resultant force to be exerted on the grasped object. The more recent ones are fast and smooth enough for real-time computation but the method we present here is faster, easier to implement and provide very satisfying results, even though the solution is sub-optimal. The method is based on the minimization of a cost function that gives an analytical solution but does not ensure by itself the satisfaction of the static frictional constraints. An associated iterative adjustment modifies this function until the internal forces enter the friction cone. The minimal solution is found within a few iterations. Force determination is therefore included in the simulation of a hybrid position/force controller to prove the effectiveness of such an approach for updating the force references during the grasped object motion.