The ability to perform activities of daily living and mobility-related activities of daily living are substantial indicators of one's ability to live at home and to participate in one's community. Technologies to assist with mobility and manipulation are among the most important tools that clinicians can provide to people with disabilities to promote independence and community participation. For people with severe disabilities involving both the upper and lower extremities, there are few systems that provide practical and coordinated assistance with mobility and manipulation tasks. The personal mobility and manipulation appliance (PerMMA) was created in response to goals set forth by a team of clinicians and people with disabilities.

In this paper, we investigate the enhanced ability of manipulation with the newly developed Personal Mobility and Manipulation Appliance (PerMMA). PerMMA is a new assistive device that integrates bimanual manipulation with smart mobility to assist people with severe physical disabilities and enhance their quality of lives. Different from the fixed mounting method used in most existing systems, a novel mounting system was designed on PerMMA to enhance its capability of manipulation assistance. With a workspace characterized by essential daily living tasks, we evaluated PerMMA's performance of manipulation using a comparative study between PerMMA and classic design, such as single arm and fixed mounting, used in most existing systems. Simulation results demonstrate significant improvements with PerMMA in both of its reachability and manipulability.

Accurate insertion of needles to targets in 3D anatomy is required for numerous medical procedures. To reduce patient trauma, a "fireworks" needle insertion approach can be used in which multiple needles are inserted from a single small region on the patient's skin to multiple targets in the tissue. In this paper, we explore motion planning for "fireworks" needle insertion in 3D environments by developing an algorithm based on Rapidly-exploring Random Trees (RRTs). Given a set of targets, we propose an algorithm to quickly explore the configuration space by building a forest of RRTs and to find feasible plans for multiple steerable needles from a single entry region. We present two path selection algorithms with different optimality considerations to optimize the final plan among all feasible outputs. Finally, we demonstrate the performance of the proposed algorithm with a simulation based on a prostate cancer treatment environment.

Finger gait is necessarily needed in order to relocate fingers of the robotic hand when a dextrous manipulation task cannot be accomplished only by the rolling and sliding motions of the fingers. In this paper, a novel modeling framework for the multifingered manipulation with finger gaits is proposed. Through classifying the fingers into grasping fingers and free fingers, an alternate representation of a grasp is introduced. With the consideration of both its discrete and continuous characteristics, the kinematics model of a fingered manipulation with finger gaits is formulated into a hybrid automaton. Finally, simulation results verify the validity of the proposed modeling framework.

Under a whole hand grasp, it may not be possible to generate grasping forces in all directions. Thus, the traditional techniques developed based on fingertip contacts is inadequate. In this paper, we decompose the contact force space into four orthogonal subspaces, each with a clear physical interpretation. Based on linear matrix inequalities (LMI's) representations of grasping constraints, we address and formulate the active force closure and the active grasp feasibility problems as LMI feasibility problems. Combining the effects of both active and passive forces, we propose a new cost index for the whole hand grasping force optimization problem. We further simply the force optimization problem for a whole hand grasp, which is active force closure.

A robotic hand may change its grasp status and relocate some of its fingers in order to perform a large scale manipulation. Such a strategy is called a finger gait. In this paper, a randomized manipulation planning algorithm is proposed to solving the finger gait planning problem. One of the most used finger gaiting primitives, finger substitution, is introduced. Because of its discrete-continuous characteristics, the kinematics model of a finger substitution is formulated into a hybrid automaton. Considering the discrete and continuous topology of the automaton, both the discrete metric and continuous metric are defined on the state space. An improved RRT based planner is proposed to find a feasible finger substitution. Finally, simulation results verify the validity of the proposed finger gait planner.

In tasks of grasping and manipulation, the hand sometimes uses not only fingertips but also fingers' inner links and the palm to achieve more robust grasp. This kind of grasp is called whole hand grasp, or power grasp. One property of whole hand grasp is that the hand may not be able to generate grasping forces in any directions, so previous fingertip grasping analysis is no longer suitable for whole hand grasp. In this paper, concepts of active force and passive force are introduced. With these concepts, the contact force space is decomposed into four orthogonal subspaces. Considering the roles of both active force and passive force, a new cost index is proposed for the whole hand grasping force optimization, which is then reformulated into a convex optimization problem involving LMIs. Finally, numerical example and simulation results verify the validity and performance of our formulation of the problem with that new proposed cost index

Large-scale motion of the grasped object is one of the tasks, which is involved in practical dextrous manipulation of multifingered robotic hand. When the large-scale motion can not be accomplished only by rolling and sliding of the finger, finger gaiting, or regrasping, is used. In this paper, two primitives of finger gaits are introduced. Based on the characteristic of finger gaits, we model finger gaits as a hybrid automaton. Finally, we do simulations on a three fingered hand to verify the validity of our model

Large-scale motion of the grasped object is one of the tasks, which is involved in practical dexterous manipulation of multifingered robotic hand. When the large-scale motion can not be accomplished only by rolling and sliding of the finger, finger gaiting, or regrasping, is used. In this paper, we propose a joint space representation of grasps, which represents a stable grasp by a set of joints values with several grasping constraints. Using primitives describing the regrasping process with the joint space representation, we build a kinematic model of finger gaiting as a hybrid automaton, by using which all grasping constraints are involved. Then, a simple but representative simulation is setup, whose results verify the validity and efficiency of the model. Finally, we state several interesting future works, which can greatly be extended based on the hybrid automaton we proposed in this paper.

This paper deals with the problem of optimal geometry design of parallel manipulators. In order to reduce the main drawbacks of parallel manipulators, relatively small workspace and more singularities, two requirements, workspace and condition number, are considered. The design problem is thus formulated to find a parallel mechanism such that its Cartesian workspace contains a prescribed workspaces with good condition numbers in it. By observing that those requirements can be locally cast into Linear Matrix Inequalities (LMIs), we formulate the design problem locally as a convex optimization problem subject to LMIs with a max-det function as its objective function. Hence, at each node of discretized space of design parameters, there is an LMI-based convex optimization problem. A two-level algorithm can be applied to solve for a set of optimal design parameters: (1) Discretize the space of design parameters into a set of discrete nodes; (2) At each node the Newton algorithm is applied to solve the max-det optimization problem. By comparing all the locally optimal costs, we can obtain a corresponding set of globally optimal design parameters correspondingly. Simulation results verify the effectiveness of the proposed approach.

Planning a proper set of contact points on a given object/workpiece so as to satisfy a certain optimality criterion is a common problem in grasp synthesis for multi-fingered robotic hands and in fixture planning for manufacturing automation. We formulate the grasp-planning problem as optimization problems with respect to several grasp quality functions. For real-time computation, a simplified min-analytic-center problem is proposed. Simulation and experimental results illustrate the validity of the proposed approach for optimal grasp planning.

Planning a proper set of contact points on a given object/workpiece so as to satisfy a certain optimality criterion is a common problem in grasp synthesis for multifingered robotic hands and in fixture planning for manufacturing automation. In this paper, we formulate the grasp planning problem as optimization problems with respect to three grasp quality functions. The physical significance and properties of each quality function are explained, and computation of the corresponding gradient flows is provided. One noticeable property of some of these quality functions is that the optimal solutions are also force-closure grasps if they do exist for the given object. Furthermore, when specialized to two-fingered or three-fingered grasps on a spherical object, the optimal solutions become the familiar antipodal grasp, or the symmetric grasp, respectively. Thus, by following the gradient flows with arbitrary initial conditions, the optimal grasp synthesis problem is solved for objects with smooth geometries manipulated by hands with any number of fingers. Also, note that our solutions do not involve linearization of the friction cones. We discuss two simplified versions of these problems when real-time solutions are needed, e.g. coordinated manipulation of a robotic hand with contact points servoing. We give simulation and experimental results illustrating validity of the proposed approach for optimal grasp planning. Note to Practitioners: This paper presents three new quality functions for comparing and planning grasps and fixtures. These measures improve on the traditional measure of force closure. We propose a method for computing the optimal solutions of these functions, and a method for reducing their computation time through reasonable simplification/approximation. Preliminary experiments with a three-fingered robotic hand demonstrate that the proposed functions can be used to optimize the grasp quality during manipulation/manufacturing, and keep the optimal grasp configuration once it is reached. However, we only obtain the local optimal solutions for the functions without simplification except for some special cases. We also assume that the object/workpiece is ideally rigid in all three functions. In future research, we will improve these limitations through a compliance model.

Real-time grasping force optimization problem can be naturally formulated as a convex optimization problem on the Riemannian manifold of positive definite matrices subject to linear constraints for which many algorithms, including gradient algorithms, Newton algorithms, and interior point algorithms, have been developed. In all these algorithms we need to specify a step size in every iteration. In this paper we propose several strategies for selecting such a step size according to the properties of each algorithm. By investigating the structure of the affine-scaling vector fields associated with the optimization problem, we give a detailed convergence analysis of these algorithms. Experimental results show the different performance of these algorithms from convergence rates.

Real-time grasping force optimization problem can be naturally formulated as a convex optimization problem on the Riemannian manifold of positive definite matrices subject to linear constraints for which many algorithms, including gradient algorithms, Newton algorithms, and interior point algorithms, have been developed. In all these algorithms we need to specify a step size in every iteration and a valid initial point to start the recursion. In this paper we propose several strategies for selecting such a step size according to the properties of each algorithm and one method for searching a valid initial point. Simulation and experimental results show the different performances of these algorithms from computation time and convergence rates.

This paper provided a unified geometric framework for kinematic analysis and synthesis of parallel manipulators. We gave a strict definition on motion types of a mechanism based on distributions on a Lie group. We derived conditions for parallel manipulators with Lie subgroup motions using the intersection of the permissible velocity spaces, or the direct sum of the constraint force spaces of each subchain, and the integration theory on a Lie group. Several practical examples were studied in detail to verify our approach.

Planning a proper set of contact points on a given object/workpiece so as to satisfy a certain optimality criterion is a common problem in grasp synthesis for multifingered robotic hands and in fixture planning for manufacturing automation. In this paper, we formulate the grasp planning problem as optimization problems with respect to two grasp quality functions. For real-time computation, a simplified Min-analytic-center problem is proposed. Simulation and experimental results illustrate the validity of the proposed approach for optimal grasp planning.

In this paper we propose several strategies for selecting such a step size according to the properties of each algorithm and a method for searching a valid initial point. By investigating the structure of the affine-scaling vector fields associated with the optimization problem, we give a detailed convergence analysis of these algorithms. Simulation and experimental results show the different performance of these algorithms from computation time and convergence rates.

This article deals with the problem of real-time grasping force optimization for multifingered manipulation. Based on a review of existing approaches, the BHM and the HTL algorithms, a need for a strictly possible initial solution is found to be a common problem. Two approaches, the max-det approach and min-max approach, are proposed to resolve this problem. The first approach, although efficient in most cases, suffers from the problem of singularity. The latter can resolve the singularity problem, but is relatively slow compared with the former. The two approaches are combined in real implementations to efficiently compute a strictly initial solution, which is in turn used in the HTL algorithm (also the BHM algorithm). The whole algorithm is shown to be fast, fully automatic, and applicable to a wide class of manipulation tasks irrespective of the number of fingers and also the geometry of the manipulated objects. Experiments on the HKUST hand demonstrate the convergence and speed of the algorithm.