Research on vision and trajectory planning system for tomato picking robots

Author(s):  
Xujie Liu ◽  
Huimin Xu ◽  
Feng Chen
2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110027
Author(s):  
Jianqiang Wang ◽  
Yanmin Zhang ◽  
Xintong Liu

To realize efficient palletizing robot trajectory planning and ensure ultimate robot control system universality and extensibility, the B-spline trajectory planning algorithm is used to establish a palletizing robot control system and the system is tested and analyzed. Simultaneously, to improve trajectory planning speeds, R control trajectory planning is used. Through improved algorithm design, a trajectory interpolation algorithm is established. The robot control system is based on R-dominated multi-objective trajectory planning. System stack function testing and system accuracy testing are conducted in a production environment. During palletizing function testing, the system’s single-step code packet time is stable at approximately 5.8 s and the average evolutionary algebra for each layer ranges between 32.49 and 45.66, which can save trajectory planning time. During system accuracy testing, the palletizing robot system’s repeated positioning accuracy is tested. The repeated positioning accuracy error is currently 10−1 mm and is mainly caused by friction and the machining process. By studying the control system of a four-degrees-of-freedom (4-DOF) palletizing robot based on the trajectory planning algorithm, the design predictions and effects are realized, thus providing a reference for more efficient future palletizing robot design. Although the working process still has some shortcomings, the research has major practical significance.


Robotica ◽  
2005 ◽  
Vol 23 (4) ◽  
pp. 467-477 ◽  
Author(s):  
Waldir L. Roque ◽  
Dionísio Doering

This paper discusses the techniques and their applications in the development of a path planning system composed of three modules, namely: global vision (GVM), trajectory planning (TPM) and navigation control (NCM). The GVM captures and processes the workspace image to identify the obstacle and the robot configurations. These configurations are used by the TPM to generate the Voronoi roadmap, to compute the maximal clearance shortest feasible path and the visibility pathway between two configurations. The NCM controls the robot functionalities and navigation. To validate the path planning system, three sets of experiments have been conducted using the Lab robot Khepera, which have shown very good results.


1991 ◽  
Vol 10 (5) ◽  
pp. 396-406 ◽  
Author(s):  
Suk-Hwan Suh ◽  
In-Kee Woo ◽  
Sung-Kee Noh

2020 ◽  
Vol 1575 ◽  
pp. 012143
Author(s):  
Xiaoyu Wang ◽  
Jianqing Hong ◽  
Yueping Sun ◽  
Dean Zhao

2007 ◽  
Vol 31 (4) ◽  
pp. 391-405 ◽  
Author(s):  
Amar KHOUKHI ◽  
Luc BARON ◽  
Marek BALAZINSKI

In this paper, a multi-objective trajectory planning system is developed for redundant manipulators. This system involves kinematic redundancy resolution, as well as robot dynamics, including actuators model. The kinematic redundancy is taken into account through a secondary criterion of joint limits avoidance. The optimization procedure is performed subject to limitations on actuator torques and workspace, while passing through imposed poses. The Augmented Lagrangian with decoupling (ALD) technique is used to solve the resulting constrained non-convex and non-linear optimal control problem. Furthermore, the final state constraint is solved using a gradient projection. Simulations on a three degrees of freedom planar redundant serial manipulator show the effectiveness of the proposed system.


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