Iteratively Learned and Temporally Scaled Force Control with application to robotic assembly in unstructured environments

Bos, J.F.T.; Wahrburg, Arne; Listmann, Kim D.

doi:10.1109/icra.2017.7989344

Cited by 13 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it requires constant tracking of the end effector relative to the final destination point, which may be problematic in some applications, e.g., robotic surgery [15]. Active compliance control based on feedback from force/torque (F/T) sensors is another technique commonly used in part assembly [16][17][18]. Active control based on external sensors was also used for automated welding of aerospace components [19].…”

Section: Introductionmentioning

confidence: 99%

Using locally adjustable hand-eye calibrations to reduce robot localization error

Franaszek

Cheok

2020

SN Appl. Sci.

View full text Add to dashboard Cite

Accurate pose of a robot end effector is required in many applications. Typically, this is achieved by robot calibration and then, registration of the robot frame to the world frame. In this paper, the registration of a poorly calibrated robot to a world frame was performed using many local hand-eye registrations and using one global registration. Both approaches were evaluated using a set of target poses. The use of the properly chosen local transformation applied to each target led to a tenfold reduction in the orientation and position error in comparison with a use of one global transformation. The median orientation error was reduced to 0.029° and the median position error was reduced to 0.221 mm which is approximately four times larger than robot specified position repeatability.

show abstract

Section: Introductionmentioning

confidence: 99%

Using locally adjustable hand-eye calibrations to reduce robot localization error

Franaszek

Cheok

2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Force and position information is usually needed for successfully addressing the robotic assembly problem. Traditional robotic assembly manipulation is realized using a force/position hybrid control and impedance control [1]- [6]. Most methods are suitable for the assembly of rigid parts.…”

mentioning

confidence: 99%

Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Li¹,

Jiang²,

Quan³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Automatic assembly of elastic components is difficult because of the potential deformation of parts during the assembly process. Consequently, robots cannot adapt their manipulation to dynamic changes. Designing systems that learn assembly skills can help in alleviating the uncertain factor for industrialgrade assembly robots. This study proposes a skill acquisition method based on multi-modal information description to realize the assembly of systems with elastic components. This multi-modal information includes two-dimensional images, poses, forces/torques, and robot joint parameters. In this method, robots acquire searching, location determination, and pose adjustment skills using these multi-modal information parameters. As a result, robots can reach the assembly target by analyzing two-dimensional images with no position constraint. While acquiring pose adjustment skills, the reward function with depth and assembly steps is used to improve the learning efficiency. The deep deterministic policy gradient (DDPG) algorithm is applied for acquiring skills. Experiments using a KUKA iiwa robot demonstrated the effectiveness and conciseness of our method. Our results indicate that the robot acquired searching, location determination, and pose adjustment skills that allowed it to successfully complete elastic assembly. INDEX TERMS Industrial robots, acquisition of manipulation skills, deep reinforcement learning, multi-modal information description. I. INTRODUCTION A. MOTIVATION Robots are used for more efficient production assembly. Research on automation of assembly systems has focused on intelligent, automatic, and flexible robots. During an actual assembly process, force measurement data are not available owing to elastic deformation between workpieces. Elastic deformation can be neglected when dealing with assembly of rigid parts. However, in the case of weakly rigid components, such deformation causes a series of uncertain changes, such as position, posture, and depth changes. There are methods based on force-control and compliance control that are already facing the problem of assembly of elastic The associate editor coordinating the review of this article and approving it for publication was Okyay Kaynak.

show abstract

Improving Automated Insertion Task in Robotics by Reducing Registration Error

Cheok¹,

Franaszek²,

Wyk³

et al. 2021

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

A peg-in-hole operation is representative of common tasks performed in assembly lines by robots. It requires registration of the coordinate frame where a part resides to the robot frame where it is acted upon. Poor registration causes misalignment of the peg and the hole which will result in a failed insertion of the peg. In this study, the dependence of the outcome of the insertion task on the quality of registration is investigated. It was shown in earlier studies that Restoring Rigid-Body Condition (RRBC) leads to decreased target registration error. This study quantifies the improvement in the Pass/Fail rate when the target registration error is reduced for a given peg-hole tolerance. A robot arm operated in position control mode was used to perform the insertion without performing any search algorithms or force/torque sensing. Results show that use of the RRBC method yielded substantial reduction in failed insertions, especially for tighter tolerances.

show abstract

Iteratively Learned and Temporally Scaled Force Control with application to robotic assembly in unstructured environments

Cited by 13 publications

References 15 publications

Using locally adjustable hand-eye calibrations to reduce robot localization error

Using locally adjustable hand-eye calibrations to reduce robot localization error

Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Improving Automated Insertion Task in Robotics by Reducing Registration Error

Contact Info

Product

Resources

About