Robotic Grasping of Fully-Occluded Objects using RF Perception

Boroushaki, Tara; Leng, Junshan; Clester, Ian; Rodríguez, Alberto; Adib, Fadel

doi:10.1109/icra48506.2021.9560956

Cited by 26 publications

(15 citation statements)

References 32 publications

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“…Radio Frequency IDentification (RFID) is a mature technology that has been widely used in retail, manufacturing and warehousing [56,55,57,58,54,27] for identification due to its low cost, small form factor and batter-free. Recently, we notice that the advances in RFID localization have proliferated its applications in robotic grasping [11,10,42,26,29,18,16,19,17] and localization [57,34,33,47,49]. However, these systems only focus on one robot's manipulation.…”

Section: Related Work a Rfid-based Robotic Applicationsmentioning

confidence: 99%

Enabling Relative Localization for Nanodrone Swarm Platooning

Sun¹

2022

Preprint

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Nanodrone swarm is formulated by multiple lightweight and low-cost nanodrones to perform the tasks in very challenging environments. Therefore, it is essential to estimate the relative position of nanodrones in the swarm for accurate and safe platooning in inclement indoor environment. However, the vision and infrared sensors are constrained by the line-of-sight perception, and instrumenting extra motion sensors on drone's body is constrained by the nanodrone's form factor and energyefficiency.This paper presents the design, implementation and evaluation of RFDrone, a system that can sense the relative position of nanodrone in the swarm using wireless signals, which can naturally identify each individual nanodrone. To do so, each lightweight nanodrone is attached with a RF sticker (i.e., called RFID tag), which will be localized by the external RFID reader in the inclement indoor environment. Instead of accurately localizing each RFID-tagged nanodrone, we propose to estimate the relative position of all the RFID-tagged nanodrones in the swarm based on the spatial-temporal phase profiling. We implement an endto-end physical prototype of RFDrone. Our experimental results show that RFDrone can accurately estimate the relative position of nanodrones in the swarm with average relative localization accuracy of around 0.95 across x, y and z axis, and average accuracy of around 0.93 for nanodrone swarm's geometry estimation.

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Section: Related Work a Rfid-based Robotic Applicationsmentioning

confidence: 99%

Enabling Relative Localization for Nanodrone Swarm Platooning

Sun¹

2022

Preprint

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“…• Scene perception and object avoidance: Seminal work in object detection using image processing [83,132,142], Radar [3,6,14,140], lidar [12,113,129], and ultrasound [8,42,47,67,81,136] have signicantly advanced the eld of study. Since the dominant physical eects change as scale reduces, conventional object avoidance techniques such as electromagnetic motors and robot navigation algorithms cannot be applied on microrobots.…”

Section: Related Workmentioning

confidence: 99%

SPiDR

Bai

Garg

Roy

2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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This paper presents the design and implementation of SPiDR, an ultra-low-power spatial sensing system for miniature mobile robots. This acoustic sensor produces a cross-sectional map of the eld-of-view using only one speaker/microphone pair. While it is challenging to have enough spatial diversity of signal with a single omnidirectional source, we leverage sound's interaction with small structures to create a 3D-printed passive lter, called a stencil, that can project spatially coded signals on a region at a ne granularity. The system receives a linear combination of the reections from nearby objects and applies a novel power-aware depth-map reconstruction algorithm. The algorithm rst estimates the approximate locations of the objects in the scene and then iteratively applies fractional multi-resolution inversion. SPiDR consumes only 10<, of power to generate a depth-map in real-world scenario with over 80% structural similarity score with the scene. CCS CONCEPTS• Computer systems organization ! Embedded and cyberphysical systems; Sensors and actuators.

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“…Most recently, researchers have explored the use of RF perception to address the mechanical search problem [5,6,33]. This research was motivated by recent advances in RF localization, which has enabled locating cheap, passive, widelydeployed RF-tags (called RFIDs tags) with centimeter scale accuracy, even through occlusions [24,32,23].…”

Section: Related Workmentioning

confidence: 99%

“…There has been increasing interest in robotic systems that can find and retrieve occluded items in unstructured environments such as warehouses, retail stores, homes, and manufacturing [8,10,5,16,6]. For example, in e-commerce warehouses, there is a need for robots that can package customer orders from unsorted inventory or process returns from a miscellaneous pile.…”

Section: Introductionmentioning

confidence: 99%

“…While this category of systems can perform well on relatively small piles, they become inefficient in complex scenarios with larger or multiple piles. The second category of systems leverages radio frequency (RF) perception in addition to vision-based perception [5,6,33]. Unlike visible light and infrared, RF signals can go through standard materials like cardboard, wood, and plastic.…”

Section: Introductionmentioning

confidence: 99%

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FuseBot: RF-Visual Mechanical Search

Boroushaki¹,

Dodds²,

Naeem³

et al. 2022

Robotics: Science and Systems XVIII

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Mechanical search is a robotic problem where a robot needs to retrieve a target item that is partially or fullyoccluded from its camera. State-of-the-art approaches for mechanical search either require an expensive search process to find the target item, or they require the item to be tagged with a radio frequency identification tag (e.g., RFID), making their approach beneficial only to tagged items in the environment.We present FuseBot, the first robotic system for RF-Visual mechanical search that enables efficient retrieval of both RFtagged and untagged items in a pile. Rather than requiring all target items in a pile to be RF-tagged, FuseBot leverages the mere existence of an RF-tagged item in the pile to benefit both tagged and untagged items. Our design introduces two key innovations. The first is RF-Visual Mapping, a technique that identifies and locates RF-tagged items in a pile and uses this information to construct an RF-Visual occupancy distribution map. The second is RF-Visual Extraction, a policy formulated as an optimization problem that minimizes the number of actions required to extract the target object by accounting for the probabilistic occupancy distribution, the expected grasp quality, and the expected information gain from future actions.We built a real-time end-to-end prototype of our system on a UR5e robotic arm with in-hand vision and RF perception modules. We conducted over 180 real-world experimental trials to evaluate FuseBot and compare its performance to a state-ofthe-art vision-based system named X-Ray [10]. Our experimental results demonstrate that FuseBot outperforms X-Ray's efficiency by more than 40% in terms of the number of actions required for successful mechanical search. Furthermore, in comparison to X-Ray's success rate of 84%, FuseBot achieves a success rate of 95% in retrieving untagged items, demonstrating for the first time that the benefits of RF perception extend beyond tagged objects in the mechanical search problem.

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Robotic Grasping of Fully-Occluded Objects using RF Perception

Cited by 26 publications

References 32 publications

Enabling Relative Localization for Nanodrone Swarm Platooning

Enabling Relative Localization for Nanodrone Swarm Platooning

SPiDR

FuseBot: RF-Visual Mechanical Search

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