Robotic polishing with force controlled end effector and multi-step path planning

Mohsin, Imran; He, Kai; Cai, Jiannan; Chen, Hao; Du, Ruxu

doi:10.1109/icinfa.2017.8078931

Cited by 17 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A disadvantage of the Jaco arm, especially in open-loop planning tasks, is its limited precision (Kinova, 2019d). A common workaround for this problem is the use of visual servoing (Chaumette & Hutchinson, 2006) based on force-feedback (Andersson & Johansson, 2001;Mohsin et al, 2017;Xie & Sun, 2016). This works quite well if the surface of interest is rigid, but has clear limitations if the surface is more flexible.…”

Section: Trimming Pipeline Approachmentioning

confidence: 99%

“…Furthermore, the trimming will modify the reference surface, such that it is harder to properly track the motions of the end‐effector. For other destructive manipulation tasks, such as grinding or cleaning, already some methods exist to perform closed‐loop control, mostly based on force‐feedback (Andersson & Johansson, ; Mohsin et al, ; Xie & Sun, ). This works quite well if the surface of interest is rigid, but has clear limitations if the surface is more flexible.…”

Section: Introductionmentioning

confidence: 99%

“…With these requirements, bush trimming also has similarities to other robotic tasks that deal with soft or flexible objects, such as grasping agricultural produce (Henten, Slot, Hol, & Willigenburg, 2009;Li, Li, Yang, & Wang, 2013;Zhang et al, 2018), or destructive manipulation, like grinding or cutting surfaces (Andersson & Johansson, 2001;Lipton, Manchester, & Rus, 2017;Mohsin, He, Cai, Chen, & Du, 2017;Tian, Lv, Li, & Liu, 2016). Still, bush trimming has some specific properties that demand special attention in the path planning.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Coverage trajectory planning for a bush trimming robot arm

Kaljaca

Vroegindeweij

Henten

2019

Journal of Field Robotics

View full text Add to dashboard Cite

A novel motion planning algorithm for robotic bush trimming is presented. The algorithm is based on an optimal route search over a graph. Differently from other works in robotic surface coverage, it entails both accuracy in the surface sweeping task and smoothness in the motion of the robot arm. The proposed method requires the selection of a custom objective function in the joint space for optimal node traversal scheduling, as well as a kinematically constrained time interpolation. The algorithm was tested in simulation using a model of the Jaco arm and three target bush shapes. Analysis of the simulated motions showed how, differently from classical coverage techniques, the proposed algorithm is able to ensure high tool positioning accuracy while avoiding excessive arm motion jerkiness. It was reported that forbidding manipulation posture changes during the cutting phase of the motion is a key element for task accuracy, leading to a decrease of the tool positioning error up to 90%. Furthermore, the algorithm was validated in a real‐world trimming scenario with boxwood bushes. A target of 20 mm accuracy was proposed for a trimming result to be considered successful. Results showed that on average 82% of the bush surface was affected by trimming, and 51% of the trimmed surface was cut within the desired level of accuracy. Despite the fact that the trimming accuracy turned out to be lower than the stated requirements, it was found out this was mainly a consequence of the inaccurate, early stage vision system employed to compute the target trimming surface. By contrast, the trimming motion planning algorithm generated trajectories that smoothly followed their input target and allowed effective branch cutting.

show abstract

Section: Trimming Pipeline Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Coverage trajectory planning for a bush trimming robot arm

Kaljaca

Vroegindeweij

Henten

2019

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Similarly, the different force control methods and process control techniques have been used for better-controlled interaction during the polishing [10][11][12]. A fuzzy vector method was suggested for the grinding and polishing, which enables the controller to deal with force sensor signals caused by the working tool, to search the direction of the constraint surface of an unknown object [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, tool path planning plays a significant role in achieving the good surface quality during the polishing process in the complex structures and requires high reachability, but it offers some relaxation in terms of accuracy [11]. Due to the flexibility and dexterity of the articulated robot, it is a better preference for polishing the non-plainer surfaces as compared to other types of industrial robots.…”

Section: Introductionmentioning

confidence: 99%

Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces

Mohsin

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Surface polishing is required in many manufacturing sectors. Currently, it demands a large amount of manual work, which is time-consuming, error-prone, and costly. Additionally, it creates hazards for the workers as it may result in many deadly respiratory diseases. Robotic polishing is the solution to these problems. It can improve productivity, eliminate the defects, and provides consistent product quality. In this paper, an effective approach is presented for the robotic polishing of the complex curved surfaces. The key part of the presented method is the tool path planning with controlled force and polishing parameters optimization evaluated using design of experiments (DOE). The tool path planning is aimed at improving the surface quality and the contact area per path. The constraints of joint limits and productivity are also considered. Moreover, its jerk avoidance strategy allows the robot to move swiftly while ensuring a smooth trajectory. The presented method is verified for the polishing of an eyeglass frame. A considerable improvement of 90% on the average roughness is achieved with the maximum acceptable roughness set at 0.02 µm. The polishing operation takes just 79 secs and the average glossiness of 76 G is achieved on the final product along with the successful elimination of scratches on the surface.

show abstract

DOREP 2.0: An Upgraded Version of Robot Control Teaching Experimental Platform with Reinforcement Learning and Visual Analysis

Wang

Zhang

et al. 2021

Intelligent Robotics and Applications

View full text Add to dashboard Cite

Robotic polishing with force controlled end effector and multi-step path planning

Cited by 17 publications

References 13 publications

Coverage trajectory planning for a bush trimming robot arm

Coverage trajectory planning for a bush trimming robot arm

Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces

DOREP 2.0: An Upgraded Version of Robot Control Teaching Experimental Platform with Reinforcement Learning and Visual Analysis

Contact Info

Product

Resources

About