Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor

Moreira, Eduardo; Rocha, Luís F.; Pinto, Andry Maykol; Moreira, António Paulo; Veiga, Germano

doi:10.1109/lra.2016.2524043

Cited by 17 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Haptic feedback is widely adopted in industrial robots for failure detection. Moreira et al [42] utilized a force sensor to assess the success of picking operations. Hegemann et al [43] proposed a failure detection algorithm for grasping based on both visual and haptic inputs.…”

Section: Multiple Sensing In Bin Pickingmentioning

confidence: 99%

“…Such a paradigm relies on the full knowledge of the objects and may suffer from cumulative perception errors due to heavy occlusion or self-occlusion of an individual complexshaped object. Other studies utilized force and torque sensors to classify if the robot grasps multiple objects [15]. The entanglement detection naturally leads to the necessity for exploring separation strategies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Zhang

Domae

Wan

et al. 2023

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due to high occlusion in the clutter, elusive entanglement phenomena, and the need for skilled manipulation planning. In this paper, we propose an autonomous, effective and general approach for picking up tangled-prone objects for industrial bin picking. First, we learn PickNet -a network that maps the visual observation to pixel-wise possibilities of picking isolated objects or separating tangled objects and infers the corresponding grasp. Then, we propose two effective separation strategies: Dropping the entangled objects into a buffer bin to reduce the degree of entanglement; Pulling to separate the entangled objects in the buffer bin planned by PullNet -a network that predicts position and direction for pulling from visual input. To efficiently collect data for training PickNet and PullNet, we embrace the self-supervised learning paradigm using an algorithmic supervisor in a physics simulator. Realworld experiments show that our policy can dexterously pick up tangled-prone objects with success rates of 90%. We further demonstrate the generalization of our policy by picking a set of unseen objects. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/tangle.

show abstract

Section: Multiple Sensing In Bin Pickingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Zhang

Domae

Wan

et al. 2023

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…For this reason, companies spend much money on regular quality checks for their products and manufacturing pipelines. Force/torque sensors have been used several times to detect if pick-and-place operations were successful [14][15][16]. Moreira et al focused on picking a set of entangled flexible tubes from a vehicle's air conditioning system [15].…”

Section: Related Workmentioning

confidence: 99%

“…Force/torque sensors have been used several times to detect if pick-and-place operations were successful [14][15][16]. Moreira et al focused on picking a set of entangled flexible tubes from a vehicle's air conditioning system [15]. They assumed that grasp and motion planning routines already existed and that the mass of the tubes was known.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Solving Tube Entanglement in Bin Picking Operations

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The robot movement itself takes place only in the x-y-plane. Other approaches focus on detecting entanglements with force-torque sensors [7,8]. To the best of the authors' knowledge, the approach presented in this paper is the first supervised learning approach to systematically separate entangled workpiece situations in a pile in random bin picking with six degrees of freedom motion planning.…”

mentioning

confidence: 99%

Using Deep Neural Networks to Separate Entangled Workpieces in Random Bin Picking

Moosmann

Spenrath

Mönnig

et al. 2021

Advances in Automotive Production Technology – Theory and Application

View full text Add to dashboard Cite

Entanglements can cause robots to pick multiple parts within random bin picking applications. Previous approaches cope with this problem by shaking the gripped workpiece above the bin. However, these methods increase the cycle time and may decrease the robustness of the application. Therefore we propose a new method to separate entangled workpiece situations by using deep supervised learning. To generate annotated training data for a convolutional neural network we set up a simulation scene. In this scene, bins are filled with different amounts of sorted workpieces in several entangled situations. Each workpiece is then moved into different directions to path poses which are evenly distributed along the surface of a hemisphere. The emerging dataset consists of cropped depth images of entangled workpiece situations and several path poses. A serial connection of convolutional neural networks is trained on this dataset and proposes a sequence of poses yielding the general departure path. Finally, the performance of this method is validated on simulated data. To the best of our knowledge, our proposed method is the first systematic approach to find the best extraction strategy to separate entangled workpieces in a pile while decreasing the effective cycle time for gripping entangled workpieces and increasing the robustness significantly.

show abstract

Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor

Cited by 17 publications

References 13 publications

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Detecting and Solving Tube Entanglement in Bin Picking Operations

Using Deep Neural Networks to Separate Entangled Workpieces in Random Bin Picking

Contact Info

Product

Resources

About