Robotic bin-picking: Benchmarking robotics grippers with modified YCB object and model set

Robotic bin-picking is increasingly important in the order-picking process in intralogistics. However, many aspects of the robotic bin-picking process (object detection, grasping, manipulation) still require the research community\u27s attention. Established methods are used to test robotic grippers, enabling comparability of the research community\u27s results. This study presents a modified YCB Robotic Gripper Assessment Protocol that was used to evaluate the performance of four robotic grippers (two-fingered, vacuum, gecko, and soft gripper). During the testing, 45 objects from the modified YCB Object and Model Set from the packaging categories, tools, small objects, spherical objects, and deformable objects were grasped and manipulated. The results of the robotic gripper evaluation show that while some robotic grippers performed substantially well, there is an expressive grasp success variation over diverse objects. The results indicate that selecting the object grasp point next to selecting the most suitable robotic gripper is critical in successful object grasping. Therefore, we propose grasp point determination using mechanical software simulation with a model of a two-fingered gripper in an ADAMS/MATLAB co-simulation. Performing software simulations for this task can save time and give comparable results to real-world experiments

Single-Motor Robotic Gripper With Three Functional Modes for Grasping in Confined Spaces

This study proposes a novel robotic gripper driven by a single motor. The main task is to pick up objects in confined spaces. For this purpose, the developed gripper has three operating modes: grasping, finger-bending, and pull-in modes. Using these three modes, the developed gripper can rotate and translate a grasped object, i.e., can perform in-hand manipulation. This in-hand manipulation is effective for grasping in extremely confined spaces, such as the inside of a box in a shelf, to avoid interference between the grasped object and obstacles. To achieve the three modes using a single motor, the developed gripper is equipped with two novel self-motion switching mechanisms. These mechanisms switch their motions automatically when the motion being generated is prevented. An analysis of the mechanism and control methodology used to achieve the desired behavior are presented. Furthermore, the validity of the analysis and methodology are experimentally demonstrated. The gripper performance is also evaluated through the grasping tests

A Practical Approach for Picking Items in an Online Shopping Warehouse

Commercially viable automated picking in unstructured environments by a robot arm remains a difficult challenge. The problem of robot grasp planning has long been around but the existing solutions tend to be limited when it comes to deploy them in open-ended realistic scenarios. Practical picking systems are called for that can handle the different properties of the objects to be manipulated, as well as the problems arising from occlusions and constrained accessibility. This paper presents a practical solution to the problem of robot picking in an online shopping warehouse by means of a novel approach that integrates a carefully selected method with a new strategy, the centroid normal approach (CNA), on a cost-effective dual-arm robotic system with two grippers specifically designed for this purpose: a two-finger gripper and a vacuum gripper. Objects identified in the scene point cloud are matched to the grasping techniques and grippers to maximize success. Extensive experimentation provides clues as to what are the reasons for success and failure. We chose as benchmark the scenario proposed by the 2017 Amazon Robotics Challenge, since it represents a realistic description of a retail shopping warehouse case; it includes many challenging constraints, such as a wide variety of different product items with a diversity of properties, which are also presented with restricted visibility and accessibility.This paper describes research conducted at the UJI Robotic Intelligence Laboratory. Support for this laboratory is provided in part by Ministerio de Economía y Competitividad (DPI2015-69041-R, DPI2017-89910-R), by Universitat Jaume I (P1-1B2014-52) and by Generalitat Valenciana (PROMETEO/2020/034). The first author was recipient of an Erasmus Mundus scholarship by the European Commission for the EMARO+ Master Program

Implementation and testing of point cloud based grasping algorithms for objetct picking

Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Codi: SJD024. Curs acadèmic 2016-2017The purpose of this study is to investigate the most effective methodologies for the grasping of items in an environment where success, robustness and time of the algorithmic computation and its implementation are a key constraint. The study originates from the Amazon Robotics Challenge 2017 (ARC’17) which addresses the problem of automating the picking process in online shopping warehouses. In a real warehouse environment the robot has to deal with restricted visibility and accessibility. The proposed solution to grasping was to retrieve a final position and orientation of the end effector given only sensory information without mesh reconstruction. Two grippers were used: a two finger gripper with a narrow opening width and a vacuum gripper. Antipodal Grasp Identification and Learning (AGILE) and Height Accumulated Features (HAF) methods were chosen for implementation on a two finger gripper due to their ease of applicability, same type of input, and reportedly high success rate. One major contribution of this work was the creation of the Centroid Normals Approach (CNA) method for the vacuum gripper that chooses the most central point cloud grasp location on the flattest part of the object. Since it does not include calculation of orientation, its computation time is faster than the other approaches. It was concluded that CNA should be used on as many objects as possible with both the vacuum gripper and the two finger gripper. A final scheme has been devised to pick up the maximum number of items by combining algorithms on the two different grippers, given the hardware restrictions, to cater to different objects in the challenge

Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system

Control techniques for mechatronic assisted surgery

The treatment response for traumatic head injured patients can be improved by using an autonomous robotic system to perform basic, time-critical emergency neurosurgery, reducing costs and saving lives. In this thesis, a concept for a neurosurgical robotic system is proposed to perform three specific emergency neurosurgical procedures; they are the placement of an intracranial pressure monitor, external ventricular drainage, and the evacuation of chronic subdural haematoma. The control methods for this system are investigated following a curiosity led approach. Individual problems are interpreted in the widest sense and solutions posed that are general in nature. Three main contributions result from this approach: 1) a clinical evidence based review of surgical robotics and a methodology to assist in their evaluation, 2) a new controller for soft-grasping of objects, and 3) new propositions and theorems for chatter suppression sliding mode controllers. These contributions directly assist in the design of the control system of the neurosurgical robot and, more broadly, impact other areas outside the narrow con nes of the target application. A methodology for applied research in surgical robotics is proposed. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers. However, the lack of conformity to the criteria in the top tier, combined with the inability to conclusively prove increased clinical benefit, particularly symptomatic benefit, is shown to be hampering the potential of surgical robotics in gaining wide establishment. A control scheme for soft-grasping objects is presented. Grasping a soft or fragile object requires the use of minimum contact force to prevent damage or deformation. Without precise knowledge of object parameters, real-time feedback control must be used to regulate the contact force and prevent slip. Moreover, the controller must be designed to have good performance characteristics to rapidly modulate the fingertip contact force in response to a slip event. A fuzzy sliding mode controller combined with a disturbance observer is proposed for contact force control and slip prevention. The robustness of the controller is evaluated through both simulation and experiment. The control scheme was found to be effective and robust to parameter uncertainty. When tested on a real system, however, chattering phenomena, well known to sliding mode research, was induced by the unmodelled suboptimal components of the system (filtering, backlash, and time delays). This reduced the controller performance. The problem of chattering and potential solutions are explored. Real systems using sliding mode controllers, such as the control scheme for soft-grasping, have a tendency to chatter at high frequencies. This is caused by the sliding mode controller interacting with un-modelled parasitic dynamics at the actuator-input and sensor-output of the plant. As a result, new chatter-suppression sliding mode controllers have been developed, which introduce new parameters into the system. However, the effect any particular choice of parameters has on system performance is unclear, and this can make tuning the parameters to meet a set of performance criteria di cult. In this thesis, common chatter-suppression sliding mode control strategies are surveyed and simple design and estimation methods are proposed. The estimation methods predict convergence, chattering amplitude, settling time, and maximum output bounds (overshoot) using harmonic linearizations and invariant ellipsoid sets