Object handling in cluttered indoor environment with a mobile manipulator

Militaru, Cristian; Mezei, Ady-Daniel; Tamás, Levente

doi:10.1109/aqtr.2016.7501382

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…In the whole process, it is necessary to calculate the trajectory of the robotic arm, and a controller with superior performance to complete the precise control of the robotic arm ( Liu et al, 2018a ; Shyam et al, 2019 ). Similar robotic arm simulation tasks, for example: in Militaru, Mezei & Tamas (2016) is a 7-DOF robotic arm and a commercial sensor. In order to complete the trajectory control they used MoveIt ( Coleman et al, 2014 ) to test different planners.…”

Section: Introductionmentioning

confidence: 99%

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Zhang¹,

Liu²

2021

PeerJ Computer Science

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The robot controller plays an important role in controlling the robot. The controller mainly aims to eliminate or suppress the influence of uncertain factors on the control robot. Furthermore, there are many types of controllers, and different types of controllers have different features. To explore the differences between controllers of the same category, this article studies some controllers from basic controllers and advanced controllers. This article conducts the benchmarking of the selected controller through pre-set tests. The test task is the most commonly used pick and place. Furthermore, to complete the robustness test, a task of external force interference is also set to observe whether the controller can control the robot arm to return to a normal state. Subsequently, the accuracy, control efficiency, jitter and robustness of the robot arm controlled by the controller are analyzed by comparing the Position and Effort data. Finally, some future works of the benchmarking and reasonable improvement methods are discussed.

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Section: Introductionmentioning

confidence: 99%

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Zhang¹,

Liu²

2021

PeerJ Computer Science

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“…Autonomous novel object grasping and handling is a wide-ranging, high-impact field with many implications, especially within domestic and industrial application. Some instances where automatic grasping has been studied include an automated checkout robot [1], garbage sorting [2], cloth manipulation [3], bed making [4], dishwasher unloading [5], automated cooking [6], [7], service robotics [8]- [11], general household-related grasping [12]- [14], clutter clearing [15], [16] and stowing, picking and packing for warehouse automation [17]- [20]-which has gained significant traction since the 2017 Amazon Robotics Challenge [21].…”

Section: Introductionmentioning

confidence: 99%

A 3-Stage Machine Learning-Based Novel Object Grasping Methodology

et al. 2020

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The automatic grasping of objects previously unseen by a robotic system is a difficult task-of which there is currently no robust solution. The research presented in this article improves upon previous works that employ depth data and learning techniques to generate and select from a pool of hypothesised grasps by focusing on the pruning and selection process. In this work, a vision-based, sampling methodology that generates candidate grasps through a convolutional neural network is proposed. Each candidate grasp is assessed using scores derived from the candidate itself and other related input modalities-such as the centre of gravity of the object. The final selection is determined by a learning algorithm. To overcome human bias, objective measures of grasp performance are established that comprehensively measure the error introduced by the grasp trial itself. The proposed metrics are empirically demonstrated to quantify grasp quality, offer useful criteria for network training and provide better descriptive power than traditional measures of grasp outcome. Experimentation showed that the proposed methodology can generate a meaningful, final grasp within 1.3 seconds. Trials quantitatively demonstrate a small-object-in-isolation performance of 99%. For unknown objects, this equates to a 10% improvement relative to other similar methodologies. Testing also showed that grasp performance was improved by 5% when implementing the proposed metrics-compared to the baseline.

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“…Robots in open environments, such as those that arise in the Industry 4.0 paradigm, collaborative, or domestic robotics [1][2][3][4], are affected by significant uncertainty in perceiving their environment. A way to handle this is to use active perception [5][6][7][8][9], which closes the loop between the sensing and control modules of the robot: control actions are chosen to maximise the information acquired from the sensor and thereby reduce uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Sorting Objects from a Conveyor Belt Using POMDPs with Multiple-Object Observations and Information-Gain Rewards

Mezei

Tamás

Buşoniu

2020

Sensors

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We consider a robot that must sort objects transported by a conveyor belt into different classes. Multiple observations must be performed before taking a decision on the class of each object, because the imperfect sensing sometimes detects the incorrect object class. The objective is to sort the sequence of objects in a minimal number of observation and decision steps. We describe this task in the framework of partially observable Markov decision processes, and we propose a reward function that explicitly takes into account the information gain of the viewpoint selection actions applied. The DESPOT algorithm is applied to solve the problem, automatically obtaining a sequence of observation viewpoints and class decision actions. Observations are made either only for the object on the first position of the conveyor belt or for multiple adjacent positions at once. The performance of the single- and multiple-position variants is compared, and the impact of including the information gain is analyzed. Real-life experiments with a Baxter robot and an industrial conveyor belt are provided.

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Object handling in cluttered indoor environment with a mobile manipulator

Cited by 11 publications

References 22 publications

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

A 3-Stage Machine Learning-Based Novel Object Grasping Methodology

Sorting Objects from a Conveyor Belt Using POMDPs with Multiple-Object Observations and Information-Gain Rewards

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