Multimodal Grasp Planner for Hybrid Grippers in Cluttered Scenes

Grasping a variety of objects is still an open problem in robotics, especially for cluttered scenarios. Multimodal grasping has been recognized as a promising strategy to improve the manipulation capabilities of a robotic system. This work presents a novel grasp planning algorithm for hybrid grippers that allows for multiple grasping modalities. In particular, the planner manages two-finger grasps, single or double suction grasps, and magnetic grasps. Grasps for different modalities are geometrically computed based on the cuboid and the material properties of the objects in the clutter. The presented framework is modular and can leverage any 6D pose estimation or material segmentation network as far as they satisfy the required interface. Furthermore, the planner can be applied to any (hybrid) gripper, provided the gripper clearance, finger width, and suction diameter. The approach is fast and has a low computational burden, as it uses geometric computations for grasp synthesis and selection. The performance of the system has been assessed with an experimental campaign in three manipulation scenarios of increasing difficulty using the objects of the YCB dataset and the DLR hybrid-compliant gripper

The Cluttered Environment Picking Benchmark (CEPB) for Advanced Warehouse Automation

Autonomous and reliable robotic grasping is a desirable functionality in robotic manipulation and is still an open problem. Standardized benchmarks are important tools for evaluating and comparing robotic grasping and manipulation systems among different research groups and also for sharing with the community the best practices to learn from errors. An ideal benchmarking protocol should encompass the different aspects underpinning grasp execution, including the mechatronic design of grippers, planning, perception, and control to give information on each aspect and the overall problem. This article gives an overview of the benchmarks, datasets, and competitions that have been proposed and adopted in the last few years and presents a novel benchmark with protocols for different tasks that evaluate both the single components of the system and the system as a whole, introducing an evaluation metric that allows for a fair comparison in highly cluttered scenes taking into account the difficulty of the clutter. A website dedicated to the benchmark containing information on the different tasks, maintaining the leaderboards, and serving as a contact point for the community is also provided

Autonomous pick and place in cluttered environments

The work presents an autonomous collaborative robotics system for pick and place of objects in cluttered environments leveraging on Baxter robot as cobotics platform. One of the robot's arm is equipped with a traditional two-fingered gripper, while the second one embeds a custom Universal Jamming Gripper. The motion of the latest one is determined by a custom depth-based perception algorithm which can identify the grasping point for unknown objects. The other arm with the parallel-jaw gripper is controlled through a state of the art mechanism based on point clouds which searches for antipodal grasping points. The motion of both arms is planned using the Moveit! framework. It allows to bring the arms to the picking point and to place the object into a chest avoiding collisions with the environment. Furthermore, the custom gripper with its vision system is compared with two state of the art systems: the work of the MIT-Princeton team that won the stowing task of the Amazon Picking Challenge in 2017 and the system used on the other Baxter's arm. The comparisons show that the proposed system is competitive for the grasping success rate while outperforms the other systems for the searching time of the grasping points

Supervised stowing as enabling technology for the integration of impaired operators in the industry

Nowadays, the number of jobs accessible to impaired people in industries is limited. However, minimum changes in the plant could allow the employment of such individuals. Technology is the way to assist such workers to perform at the best they could and to avoid disabilities to limit their potentialities. This work proposes a supervised stowing system that will allow impaired operators to perform the task of order-picking in cluttered environments, despite their disabilities. Pick and place is the most repetitive and common task in industrial manufacturing and warehouses, requiring complex cognitive skills for robots. In the proposed system, the task is physically executed by a robot, while the mental process of selecting the correct object to pick is demanded to the operator. This action is performed through an easy to use graphical user interface. An experimental campaign has been conducted to demonstrate the effectiveness of the proposed assistive robotic system

Is Deep Learning ready to satisfy Industry needs?

The impact that Artificial Intelligence is having in modern society is undeniable. Many companies are now using AI to improve the throughput and automate their processes. But the challenge is that Artificial Intelligence is both a source of enthusiasm and skepticism for industries. The manuscript points out the main causes of skepticism giving at the same time some possible technical solutions to exploit at the best the potentialities of AI even in those conditions in which the data are imbalanced and the object classes are not well separated. This work also emphasizes the delicate relationship between artificial intelligence, researchers, and industries, and tries to give an overview of a possible trade-off between the two parties. The document ends up proposing an 'interpretable learning' approach that can be exploited as a common language between the two players. The desirable practice would be to make AI explainable, provable, and easily understandable by the companies

CEPB dataset: a photorealistic dataset to foster the research on bin picking in cluttered environments

Several datasets have been proposed in the literature, focusing on object detection and pose estimation. The majority of them are interested in recognizing isolated objects or the pose of objects in well-organized scenarios. This work introduces a novel dataset that aims to stress vision algorithms in the difficult task of object detection and pose estimation in highly cluttered scenes concerning the specific case of bin picking for the Cluttered Environment Picking Benchmark (CEPB). The dataset provides about 1.5M virtually generated photo-realistic images (RGB + depth + normals + segmentation) of 50K annotated cluttered scenes mixing rigid, soft, and deformable objects of varying sizes used in existing robotic picking benchmarks together with their 3D models (40 objects). Such images include three different camera positions, three light conditions, and multiple High Dynamic Range Imaging (HDRI) maps for domain randomization purposes. The annotations contain the 2D and 3D bounding boxes of the involved objects, the centroids’ poses (translation + quaternion), and the visibility percentage of the objects’ surfaces. Nearly 10K separated object images are presented to perform simple tests and compare them with more complex cluttered scenarios tests. A baseline performed with the DOPE neural network is reported to highlight the challenges introduced by the novel dataset

A UHF Passive RFID Tag Position Estimation Approach Exploiting Mobile Robots: Phase-Only 3D Multilateration Particle Filters With No Unwrapping

Radio-frequency identification is one of the Internet of Things’ most promising technologies and has been recently used in combination with mobile robots for logistics in business and retail applications. This manuscript deals with the localization of passive UHF RFID tags within industrial environments employing receiving antennas mounted on a mobile robot by using multilateration techniques that exploit narrowband phase-delay measurements. Two distinct Particle Filter approaches are presented to solve the 3D multilateration problem online and take advantage of a synthetic aperture created by the motion of the robot in the environment. One of the methods can operate in the presence of acquisition jumps since it does not rely on an unwrapping technique. Experimental results show promising performance concerning the recent literature. Moreover, the presented approach enables robust estimations concerning signal loss due to communication disturbances in noisy environments, typical of the industrial setting