Weiwei Wan

Grasping by caging: A promising tool to deal with uncertainty

This paper presents a novel approach to deal with uncertainty in grasping. The basic idea is to initiate a caging manipulation state and then shrink fingers into immobilization to perform a practical grasping. Thanks to flexibility from caging, this procedure is intrinsically safe and gains tolerance towards uncertainty. Besides, we demonstrate that the minimum caging is immobilization and consequently propose using three or four fingers to manipulate planar convex objects in a grasping-by-caging way. Experimental results with physical simulation show the robustness and efficacy of our approach. We expect its leading benefits in saving finger number, conquering low-friction materials and especially, dealing with pose/shape uncertainty.

A new “grasping by caging” solution by using eigen-shapes and space mapping

“Grasping by caging” has been considered as a powerful tool to deal with uncertainty. In this paper, we continue to explore into “grasping by caging” and propose a new solution by using eigen-shapes and space mapping. For one thing, eigen-shapes fix dexterous hands into a series of finger formations and help to reduce dimensionality and computational complexity. For the other, space mapping builds a mapping between rasterized grids in 2-D Work space (W space) and rasterized voxels in 3-D Configuration space (C space) and helps to rapidly reconstruct C space so that we can efficiently measure the robustness of caging and find an optimal caging configuration for grasping. Our algorithm can work rapidly and squeezingly cage any 2-D shapes, including objects with either convex boundaries, concave boundaries, 1-order or high-order boundaries and even objects with inner holes. We implement the algorithm with MATLAB and carry out experiments with WEBOTS simulation to test its robustness to uncertainties. The results show that our algorithm can work well with various object shapes and can be robust to noisy control and noisy perception. It is promising in the power grasping tasks of dexterous hands.

On the caging region of a third finger with object boundary clouds and two given contact positions

This paper presents a caging approach which deals with planar boundary clouds collected from a laser scanner. Given the boundary clouds of a target object and two fixed finger positions, our aim is to find potential third finger positions that can prevent target from escaping into infinity. The major challenge in working with boundary clouds lies in their uncertainty in geometric model fitting and the failure of critical orientations. In this paper, we track canonical motions according to the rotational intersection of Configuration space fingers and rasterize Work space with grids to compute the third caging positions. Our approach can generate the capture region with max(O(np),O(h2)) ≤ O(n2) cost where n denotes the resolution of grid rasterization, p denotes the resolution of canonical rasterization and h denotes the resolution of boundary rasterization or the number of boundary cloud points. Moreover, we propose a rough approximation which measures a subset of the possible positions by contracting rotations, indicating computational complexity of max(O(n),O(h2)). In the experimental part, our proposal is compared with state-of-the-art works and applied to many other objects. The approach makes caging fast and effective.

Cooperative manipulation with least number of robots via robust caging

One problem in multi-robot cooperative manipulation is redundancy. Too many robots are waste of hardware and increase control complexity. This paper solves the problem of redundancy by robust caging. Robust caging calculates caging positions from translational immobilization with respect to translational constraints and rotational constraints. On the one hand, robust caging helps to reduce the necessary number of robots in cooperation. On the other hand, the initial positions of necessary robots in robust caging are optimized to offer large robustness to control errors. Our proposal with robust caging is implemented to transport target objects over slopes. The algorithm can choose least number of robots with respect to shape of target objects and requirements of robustness. At the same time, each robot may endure as much as 256 ms time step and 1 cm control error, showing the superiority of robust caging.

Design of Distributed End-Effectors for Caging-Specialized Manipulator

In this paper, we propose a novel design of end-effectors that is specialized in caging manipulation. Caging manipulation has several advantages comparing with traditional grasping manipulation. For example, caging can allow small gap/margin between end-effectors and a target object, making the manipulator relieved from constant contact and precise control. Therefore, caging manipulator can avoid many problems from dynamics. Regardless of its advantages, intelligent caging manipulators have not be realized. This is because, for one thing, it may demand many actuators to realize flexible geometrical constraint (caging), for the other thing, kinematic constraints of a general purpose manipulator prevents us from applying direct caging approaches.We address this problem by introducing a novel design/framework of end-effectors that is inspired by ROBOTWORLD. The framework utilizes permanent magnet inductive traction method. The method is suitable for coexistence of multiple robots and for reduction of actuator number by sharing the same actuators.We discuss the concept and the basic framework of the proposed caging manipulator and development of a finger component prototype.After that we conduct basic experiments to evaluate the feasibility of caging manipulation and to reveal the obstacles (challenges) for our manipulator.

Multirobot Object Transport via Robust Caging

In this paper, we propose a control algorithm to collectively transport an object using a group of relatively low-cost robots. We address this problem using the robust caging, which features reliable object closure with minimum number of robots, and requires no high-precision control capability on the individual robot. Given a 2-D convex object, the proposed method uses the quality of complete robustness to first optimize the number of robots in the initial formation, and then reorient and move the formation. The method is free of force analysis, and therefore less prone to sensor errors and failures. Compared with state-of-the-art multirobot object transport approaches, which require more robots and rely heavily on high-precision control, such as force and torque feedback control, our method uses fewer robots and has high tolerance to control noises. We performed both simulation and real-time experiments to demonstrate the performance of our method. We conclude that the proposed robust caging is promising under reduced number of robots and a certain level of control noises in multirobot object transport tasks.

Automated Construction System of Robot Locomotion and Operation Platform for Hazardous Environments- Basic System Design and Feasibility Study of Module Transferring and Connecting Motions

Unmanned robot operation is highly anticipated for use in hazardous environments such as a nuclear accident and mine accident sites. We propose an automated construction system for robot locomotion and operation platform in a severely disturbed environment. In such an environment, the sensors and actuators that can be used are restricted. The platform is intended to enable specialized working robots to have access to any cube-diced operation point, and to build a rail both for the platform itself and for specialized working robots. The entire platform structure is modularized, which means that the structure comprises multiple modules. They are assembled and constructed through cooperation of a transfer robot and a constructor robot. This paper describes the development of prototypes and explains experiments conducted to verify our fundamental concept. In particular, the feasibility of module-transfer and connection motions in three prioritized positions is verified using the developed prototypes. The system design and experiments reveal that the most important technique to realize the proposed system is how to use guide structures to reduce the effects of mechanical error and misalignment among robots.

How to manipulate an object robustly with only one actuator (An application of caging)

Caging can offer robustness to uncertainties in grasping. If a robotic hand is designed based on the idea of caging, it would probably work well with noisy perception devices and low-quality control. This paper takes into account these merits and designs and implements a gripping hand based on the idea of caging. The gripping hand is concise and offers a low-cost alternative to co-operate with noisy data and low-quality control. According to previous work, we need four fingers to cage any 2D objects. That is to say, if each finger has one, two or three degree of freedoms, we will totally need four, eight or twelve actuators. The large number of actuators would be costly. This paper simplify the number of actuators into one by quantitatively analyzing finger formations with caging tests conducted on both random objects and objects from MPEG-7 shape database. It successfully lowers costs while maintains high performance. Following the simplified one-actuator design we implement a gripping hand by modifying a SCHUNK RH707 hand and carried out experiments with a manipulator built on the Neuronics Katana arm. The one-actuator gripping hand could work well with common depth cameras (Swiss Ranger) and pick up various objects. It bridges the gap between caging theories and applications and demonstrates the merits of caging.

Finger-position optimization by using caging qualities

Caging aims at constraining objects like a bird cage. It does not need force closure and contacts, and therefore is robust to perception noises. This paper considers these merits of caging and proposes an efficient approach to find the optimized finger positions for robust grasping under perception noises by measuring the quality of caging. It initially uses two quality models (the quality model of translational constraint and the quality model of rotational constraint) to combinatorically represent the quality of caging and finds the optimized finger positions (finger positions that have large robustness to perception noises) by maximizing the margins to caging breaking. Meanwhile, it employs Genetic Algorithm (GA) to accelerate the combinatorial optimization of the two quality models. The encoding rules, the initial procedure, and the stopping criterion of the GA are designed carefully towards the quality models to improve its computational efficiency. Experiments with various object shapes show that with the help of multi-model optimization and GA acceleration, the proposed approach is not only robustness to perception noises but also efficient in computation. HighlightsAn efficient approach to find the optimized finger positions for grasping.The problem is solved by novelly measuring the qualities of caging.The caging breaking margins are constrained by two quality models.Genetic Algorithm (GA) is used to efficiently perform the joint optimization.

From analysis to practice: Three-finger caging of planar convex objects

This paper presents the development of planar caging manipulation. It involves a preliminary conclusion where the targets are limited to convex objects and the finger number is limited to three. Despite the popularity of form or force closure analysis, we prefer caging as it owns merits like requiring little dynamics, reducing kinematics and affording robust breaking margins to tolerate control errors. The analysis part of this paper theoretically discusses optimization procedures that best exploits the merits from caging. The practice part presents implementation details of our proposal in real work space by employing KINECT, a low-cost depth image capture produced by PrimeSense. Especially, some artifice and strategies are discussed and compared in this part to fulfill application requirements. Experimental results show the efficacy of our analysis and its promising future.