Vision-Based Motion Planning of Cable-Driven Parallel Robots in the Presence of Moving Obstacle Using Sampling-Based Method

Author(s):  
Jiajun Xu ◽  
Kyoung-Su Park

Abstract In the past decades, cable-driven parallel robots (CDPRs) have been proven the extraordinary performance for various applications. However, the multiple cables lead the robot easy to interfere with environments. Especially the large workspace of CDPR may introduce unknown moving obstacles. In this study, a sampling-based path planning method is presented for a CDPR to find the collision-free path in the presence of the moving obstacle. The suggested method is based on rapidly exploring random tree (RRT) algorithm which gives CDPRs advantages to handle complex constraints such as cable collision and dynamic feasible workspace (DFW). Moreover, we conduct the forward simulation to check the feasibility in a closed-loop system. The moving parts of both CDPRs and the moving obstacle are assumed as convex bodies, so that Gilbert-Johnson-Keerthi (GJK) algorithm is adopted to detect collision in real-time. Finally, the related simulation is carried out to illustrate the algorithm. The experiment is also presented using the drone as a moving obstacle and YOLO vision algorithm to detect the drone. The experiment results demonstrate the reliability of the suggested method.

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141881995
Author(s):  
Francisco G Salas ◽  
Jorge Orrante-Sakanassi ◽  
Raymundo Juarez-del-Toro ◽  
Ricardo P Parada

Parallel robots are nowadays used in many high-precision tasks. The dynamics of parallel robots is naturally more complex than the dynamics of serial robots, due to their kinematic structure composed by closed chains. In addition, their current high-precision applications demand the innovation of more effective and robust motion controllers. This has motivated researchers to propose novel and more robust controllers that can perform the motion control tasks of these manipulators. In this article, a two-loop proportional–proportional integral controller for trajectory tracking control of parallel robots is proposed. In the proposed scheme, the gains of the proportional integral control loop are constant, while the gains of the proportional control loop are online tuned by a novel self-organizing fuzzy algorithm. This algorithm generates a performance index of the overall controller based on the past and the current tracking error. Such a performance index is then used to modify some parameters of fuzzy membership functions, which are part of a fuzzy inference engine. This fuzzy engine receives, in turn, the tracking error as input and produces an increment (positive or negative) to the current gain. The stability analysis of the closed-loop system of the proposed controller applied to the model of a parallel manipulator is carried on, which results in the uniform ultimate boundedness of the solutions of the closed-loop system. Moreover, the stability analysis developed for proportional–proportional integral variable gains schemes is valid not only when using a self-organizing fuzzy algorithm for gain-tuning but also with other gain-tuning algorithms, only providing that the produced gains meet the criterion for boundedness of the solutions. Furthermore, the superior performance of the proposed controller is validated by numerical simulations of its application to the model of a planar three-degree-of-freedom parallel robot. The results of numerical simulations of a proportional integral derivative controller and a fuzzy-tuned proportional derivative controller applied to the model of the robot are also obtained for comparison purposes.


2021 ◽  
Vol 2 (3) ◽  
pp. 324-336
Author(s):  
Dhyna Apriyanti Walidi

A rich ecological value area within East Kalimantan, Kutai Kartanegara, represents a specific-particular ecological system of an island in a tropical country. Covered by the evergreen forest in the past, it has a closed-loop system formed naturally due to its metabolism. This system maintains the high diversity of nature which provides abundant resources both renewable and non-renewable. Owing to its ecological system value, the earth has been numerous beneficial the economic sector both for country and region for decades. Let say coal is one of the attractive resources for the energy sector, which has been contributing over 80% of the GDRP of Kutai Kartanegara in 2010. 


Author(s):  
Jiajun Xu ◽  
Kyoung-su Park

Abstract In this study, the improved vision-based rapidly exploring random tree (RRT) algorithm is proposed to address moving obstacle avoidance for cable-driven parallel robots (CDPRs). The improved RRT algorithm is goal-biased with dynamic stepsize makes it possible to implement in dynamic environments. For the implementation of algorithm on CDPRs, the improved RRT considers various collisions caused by the cable. The axis-aligned bounding box (AABB) algorithm is used for the fast re-planning during the RRT process. Additionally, the improved RRT algorithm premeditates the complex constrains include force feasible workspace (FFW) and the segment-to-segment angle. The related simulation is given in order to illustrate the algorithm. An experimental setup is also presented using the drone as a moving obstacle and the Faster-RCNN vision algorithm to obtain the coordinate of the drone. The experiment result shows that the proposed algorithm can apply in CDPRs with the dynamic environment validly.


2010 ◽  
Vol 40 (4) ◽  
pp. 42-43
Author(s):  
MIRIAM E. TUCKER

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1376-P
Author(s):  
GREGORY P. FORLENZA ◽  
BRUCE BUCKINGHAM ◽  
JENNIFER SHERR ◽  
THOMAS A. PEYSER ◽  
JOON BOK LEE ◽  
...  

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1066-P
Author(s):  
HALIS K. AKTURK ◽  
DOMINIQUE A. GIORDANO ◽  
HAL JOSEPH ◽  
SATISH K. GARG ◽  
JANET K. SNELL-BERGEON

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 207-OR
Author(s):  
BRUCE A. BUCKINGHAM ◽  
JENNIFER SHERR ◽  
GREGORY P. FORLENZA ◽  
THOMAS A. PEYSER ◽  
JOON BOK LEE ◽  
...  

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 2318-PUB
Author(s):  
MAHTA SADEGHZADEH

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