We present a new method for generating collisionfree paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph. After presenting the approach, we address a number of performance issues via extensive simulation results for robots with as many as twenty degrees of freedom. We evaluate memory requirements, preprocessing time, and the time to dynamically modify the graph and replan, all as a function of the number of degrees of freedom of the robot.

In the absence of vision, grasping an object often relies on tactile feedback from the fingertips. As the finger pushes the object, the fingertip can feel the contact point move. If the object is known in advance, from this motion the finger may infer the location of the contact point on the object and thereby the object pose. This paper primarily investigates the problem of determining the pose (orientation and position) and motion (velocity and angular velocity) of a planar object with known geometry from such contact motion generated by pushing. A dynamic analysis of pushing yields a nonlinear system that relates through contact the object pose and motion to the finger motion. The contact motion on the fingertip thus encodes certain information about the object pose. Nonlinear observability theory is employed to show that such information is sufficient for the finger to “observe ” not only the pose but also the motion of the object. Therefore a sensing strategy can be realized as an observer of the nonlinear dynamical system. Two observers are subsequently introduced. The first observer, based on the result of [15], has its “gain ” determined by the solution of a Lyapunov-like equation; it can be activated at any time instant during a push. The second observer, based on Newton’s method, solves for the initial (motionless) object pose from three intermediate contact points during a push. Under the Coulomb friction model, the paper copes with support friction in the plane and/or contact friction between the finger and the object. Extensive simulations have been done to demonstrate the feasibility of the two observers. Preliminary experiments (with an Adept robot) have also been conducted. A contact sensor has been implemented using strain gauges. 1

In the absence of vision, grasping an object often relies on tactile feedback from the fingertips. Before force closure is formed, where on the object a fingertip touches can usually be felt from the motion of contact on the fingertip during a small amount of pushing. In this paper we investigate the first stage of such "blind" grasping. More specifically, we study the problem of determining the pose of a known planar object by pushing. Assuming sliding friction in the plane, a dynamic analysis of pushing results in a numerical algorithm that computes the object pose from three instantaneous contact positions on a pusher. Simulations and experiments (with an Adept robot) have been conducted to demonstrate the sensing feasibility. Inspired by the way a human hand grasps, this work can be viewed as a primitive step in exploring interactive sensing in grasping tasks. 1 Introduction Part sensing and grasping are two fundamental operations in automated assembly. Traditionally, they are per...

We present a new method to reconstruct the shape of an unknown object using tactile sensors without requiring object immobilization. Instead, the robot manipulates the object without prehension. The robot infers the shape, motion and center of mass of the object based on the motion of the contact points as measured by tactile sensors. Our analysis is supported by simulation and experimental results.

This paper considers the problem of inferring local contact geometry from passive tactile information. The term "passive" means that the information results from motions of the object that are not necessarily under the control of the robot. To juxtapose, generally the term "active sensing" means that a robot actively explores an object, for instance with visual and/or tactile sensors that move around the object. This paper is not concerned with such active exploration. Instead, our goal is to determine how local shape geometry may be inferred from purely passive information. Shape recovery based on passive information will be useful both for dealing with unexpected events, as in the slippery grasping example above, and for more elaborate manipulation strategies, such as active exploration. 1.1 Motivation and Context

Abstract While complex hands seem to offer generality, simple hands are more practical for most robotic and telerobotic manipulation tasks, and will remain so for the foreseeable future. This raises the question: how do generality and simplicity trade off in the design of robot hands? This paper explores the tension between simplicity in hand design and generality in hand function. It raises arguments both for and against simple hands; it considers several familiar examples; and it proposes a concept for a simple hand design with associated strategies for grasping and object localization. The central idea is to use knowledge of stable grasp poses as a cue for object localization. This leads to some novel design criteria, such as a desire to have only a few stable grasp poses. We explore some of the design implications for a binpicking task, and then examine some experimental results to see how this approach might be applied in an assistive object retrieval task. 1

Automatic recognition of parts is an important problem in many industrial applications. One model of the problem is: Given a finite set of polygonal parts, use a set of "width" measurements taken by a parallel-jaw gripper to determine which part is present. We study the problem of computing efficient strategies ("grasp plans"), with the goal to minimize the number of measurements necessary in the worst case. We show that finding a minimum length grasp plan is Af7-hard, and give a polynomial time approximation algorithm that is simple and produces a solution that is within a log factor from optimal.

this paper we present a model that integrates manipulation and tactile sensing. We derive equations for the shape and motion of an unknown object as a function of the motion of the manipulators and the sensor readings

We present a new method to reconstruct the shape of an unknown object using tactile sensors, without requiring object immobilization. Instead, sensing and nonprehensile manipulation occur simultaneously. The robot infers the shape, motion and center of mass of the object based on the motion of the contact points as measured by the tactile sensors. We present analytic results and simulation results assuming quasistatic dynamics. We prove that the shape and motion are observable in both the quasistatic and the fully dynamic case.

Abstract — Iteration is often sufficient for a simple hand to accomplish complex tasks, at the cost of an increase in the expected time to completion. In this paper, we minimize that overhead time by allowing a simple hand to abort early and retry as soon as it realizes that the task is likely to fail. We present two key contributions. First, we learn a probabilistic model of the relationship between the likelihood of success of a grasp and its grasp signature—the trace of the state of the hand along the entire grasp motion. Second, we model the iterative process of early abort and retry as a Markov chain and optimize the expected time to completion of the grasping task by effectively thresholding the likelihood of success. Experiments with our simple hand prototype tasked with grasping and singulating parts from a bin show that early abort and retry significantly increases efficiency. I.