A study on picking objects in cluttered environments: Exploiting depth features for a custom low-cost universal jamming gripper

D’Avella, Salvatore; Tripicchio, Paolo; Avizzano, Carlo Alberto

doi:10.1016/j.rcim.2019.101888

Cited by 51 publications

(34 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several reviews of soft grippers can be found in the literature [19,33,[182][183][184][187][188][189], presenting various approaches to classify existing technologies. One of the most widely used approaches is the one that classifies the soft gripping technologies according to three different categories [190,191]: (i) actuation, (ii) control stiffness and (iii) controlled adhesion. However, it is currently possible to find devices whose designs simultaneously combine characteristics from several of these categories.…”

Section: Soft Grippersmentioning

confidence: 99%

“…Several authors [32] report difficulties in controlling certain types of FEA soft actuators due to their deflection around the object. This is especially intricate in anthropomorphic grippers in terms of achieving speed, flexibility and dexterity [191]; not only in FEA actuators but also in passive structures actuated by external motors or tendon motors. This disadvantage can be partially solved by sensing the actuator or by real-time control using FEM [187,262].…”

Section: Controlmentioning

confidence: 99%

See 1 more Smart Citation

Soft Grippers for Automatic Crop Harvesting: A Review

Navas

Fernández

Sepúlveda

et al. 2021

Sensors

119

View full text Add to dashboard Cite

Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.

show abstract

Section: Soft Grippersmentioning

confidence: 99%

Section: Controlmentioning

confidence: 99%

Soft Grippers for Automatic Crop Harvesting: A Review

Navas

Fernández

Sepúlveda

et al. 2021

Sensors

119

View full text Add to dashboard Cite

show abstract

“…As a theoretical framework, the existing literature can be structured in four interconnecting areas affecting each other regarding the efficiency of order picking systems (see Figure 1): First, the technology applied as well as the continuing technology development play an essential role in order picking. As a series of authors is outlining, concurrent technical developments are affecting order picking processes and results [9][10][11][12][13][14][15]. Third, planning and organization questions are discussion, especially in operational research contexts regarding order picking systems.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Order Picking and E-Commerce: Introducing Non-Parametric Efficiency Measurement for Sustainable Retail Logistics

Klumpp

Loske

2021

JTAER

View full text Add to dashboard Cite

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations.

show abstract

“…Le and Lin 13 proposed a 2D localization algorithm for a robot based on deep learning, which enabled an out-of-order grasping of a specific object base on the deep neural network. Literature 14 proposed a cooperative robotics pick and place system for different objects. It solving the perceptual issues posed by cluttered environments though a custom algorithm that selects a suitable picking point.…”

Section: Related Workmentioning

confidence: 99%

Robot visual measurement and grasping strategy for roughcastings

Wan

Wang

Xing

et al. 2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

To overcome the challenging problem of visual measurement and grasping of roughcasts, a visual grasping strategy for an industrial robot is designed and implemented on the basis of deep learning and a deformable template matching algorithm. The strategy helps realize the positioning recognition and grasping guidance for a metal blank cast in complex backgrounds under the interference of external light. The proposed strategy has two phases: target detection and target localization. In the target detection stage, a deep learning algorithm is used to recognize the combined features of the surface of an object for a stable recognition of the object in nonstructured environments. In the target localization stage, high-precision positioning of metal casts with an unclear contour is realized by combining the deformable template matching and LINE-MOD algorithms. The experimental results show that the system can accurately provide visual grasping guidance for robots.

show abstract

A study on picking objects in cluttered environments: Exploiting depth features for a custom low-cost universal jamming gripper

Cited by 51 publications

References 29 publications

Soft Grippers for Automatic Crop Harvesting: A Review

Soft Grippers for Automatic Crop Harvesting: A Review

Order Picking and E-Commerce: Introducing Non-Parametric Efficiency Measurement for Sustainable Retail Logistics

Robot visual measurement and grasping strategy for roughcastings

Contact Info

Product

Resources

About