Two-Fingered In-Hand Object Handling Based on Force/Tactile Feedback

Costanzo, Marco; Maria, G. De; Natale, Ciro

doi:10.1109/tro.2019.2944130

Cited by 49 publications

(33 citation statements)

References 34 publications

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“…It exploits the Limit Surface (LS) theory [15] that describes the translational and rotational slippage at the same time. The algorithm will be briefly described in this section but the interested reader can find more details in [8] and [10].…”

Section: A Slipping Controllermentioning

confidence: 99%

Manipulation Planning and Control for Shelf Replenishment

Costanzo

Stelter

Natale

et al. 2020

IEEE Robot. Autom. Lett.

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Manipulation planning and control are relevant building blocks of a robotic system and their tight integration is a key factor to improve robot autonomy and allows robots to perform manipulation tasks of increasing complexity, such as those needed in the in-store logistics domain. Supermarkets contain a large variety of objects to be placed on the shelf layers with specific constraints, doing this with a robot is a challenge and requires a high dexterity. However, an integration of reactive grasping control and motion planning can allow robots to perform such tasks even with grippers with limited dexterity. The main contribution of the paper is a novel method for planning manipulation tasks to be executed using a reactive control layer that provides more control modalities, i.e., slipping avoidance and controlled sliding. Experiments with a new force/tactile sensor equipping the gripper of a mobile manipulator show that the approach allows the robot to successfully perform manipulation tasks unfeasible with a standard fixed grasp.

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Section: A Slipping Controllermentioning

confidence: 99%

Manipulation Planning and Control for Shelf Replenishment

Costanzo

Stelter

Natale

et al. 2020

IEEE Robot. Autom. Lett.

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“…Kanoulas et al proposed a key strategy in their algorithm which is also used in this paper: if the force readings lie outside the expected range, the robot releases the object being grasped [17]. Costanzo et al presented the concept of manipulating a grasped object using force feedback [18]. In their study, they used force/tactile sensors to adjust a parallel-jaw gripper's force so that it could rotate around its axis whilst maintaining the object's orientation.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Solving Tube Entanglement in Bin Picking Operations

et al. 2020

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Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.

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“…The latter has been presented in [ 15 ], by reporting a calibration procedure that allows to use it as a 6-axes force/torque sensor by means of a suitably trained neural network. It has been used in [ 16 , 17 ] to implement a slipping avoidance algorithm using the force/tactile feedback, by showing how in-hand manipulation is possible even with simple parallel grippers by exploiting tactile feedback in pivoting maneuvers. Despite the good results reached with the previously designed sensors, the obtained performances present limitations from a mechanics robustness and electronics design point of view.…”

Section: Introductionmentioning

confidence: 99%

Tactile Sensors for Parallel Grippers: Design and Characterization

Cirillo

Costanzo

Laudante

et al. 2021

Sensors

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Tactile data perception is of paramount importance in today’s robotics applications. This paper describes the latest design of the tactile sensor developed in our laboratory. Both the hardware and firmware concepts are reported in detail in order to allow the research community the sensor reproduction, also according to their needs. The sensor is based on optoelectronic technology and the pad shape can be adapted to various robotics applications. A flat surface, as the one proposed in this paper, can be well exploited if the object sizes are smaller than the pad and/or the shape recognition is needed, while a domed pad can be used to manipulate bigger objects. Compared to the previous version, the novel tactile sensor has a larger sensing area and a more robust electronic, mechanical and software design that yields less noise and higher flexibility. The proposed design exploits standard PCB manufacturing processes and advanced but now commercial 3D printing processes for the realization of all components. A GitHub repository has been prepared with all files needed to allow the reproduction of the sensor for the interested reader. The whole sensor has been tested with a maximum load equal to 15N, by showing a sensitivity equal to 0.018V/N. Moreover, a complete and detailed characterization for the single taxel and the whole pad is reported to show the potentialities of the sensor also in terms of response time, repeatability, hysteresis and signal to noise ratio.

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Two-Fingered In-Hand Object Handling Based on Force/Tactile Feedback

Cited by 49 publications

References 34 publications

Manipulation Planning and Control for Shelf Replenishment

Manipulation Planning and Control for Shelf Replenishment

Detecting and Solving Tube Entanglement in Bin Picking Operations

Tactile Sensors for Parallel Grippers: Design and Characterization

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