scholarly journals A Look-Ahead Trajectory Planning Algorithm for Spray Painting Robots with Non-spherical Wrists

2018 ◽  
pp. 235-242 ◽  
Author(s):  
Giulio Trigatti ◽  
Paolo Boscariol ◽  
Lorenzo Scalera ◽  
Daniele Pillan ◽  
Alessandro Gasparetto
Keyword(s):  
Trajectory Planning ◽  
Spray Painting ◽  
Look Ahead ◽  
Planning Algorithm

scholarly journals Control system of 4-DOF palletizing robot based on improved R control multi-objective trajectory planning

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110027
Author(s):  
Jianqiang Wang ◽  
Yanmin Zhang ◽  
Xintong Liu
Keyword(s):  
Control System ◽  
Trajectory Planning ◽  
Robot Control ◽  
Machining Process ◽  
Planning System ◽  
Positioning Accuracy ◽  
Multi Objective ◽  
Planning Algorithm ◽  
Function Testing ◽  
Robot Control System

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.

Trajectory planning for coordinately operating robots

1990 ◽  
Vol 4 (3) ◽  
pp. 165-177 ◽  
Author(s):  
Y. P. Chien ◽  
Qing Xue
Keyword(s):  
Trajectory Planning ◽  
Three Dimensional ◽  
Initial Position ◽  
Minimum Time ◽  
Future Research ◽  
Rigid Object ◽  
Redundant Robots ◽  
Planning Algorithm ◽  
The Common ◽  
Joint Velocity

An efficient locally minimum-time trajectory planning algorithm for coordinately operating multiple robots is introduced. The task of the robots is to carry a common rigid object from an initial position to a final position along a given path in three-dimensional workspace in minimum time. The number of robots in the system is arbitrary. In the proposed algorithm, the desired motion of the common object carried by the robots is used as the key to planning of the trajectories of all the non-redundant robots involved. The search method is used in the trajectory planning. The planned robot trajectories satisfy the joint velocity, acceleration and torque constraints as well as the path constraints. The other constraints such as collision-free constraints, can be easily incorporated into the trajectory planning in future research.

Control Strategy and Trajectory Planning for Reconfiguration of a vA Based Metamorphic Parallel Manipulator

2013 ◽  
Author(s):  
Evangelos Emmanouil ◽  
Ketao Zhang ◽  
Jian S. Dai
Keyword(s):  
Phase Change ◽  
Trajectory Planning ◽  
Control Strategy ◽  
Parallel Manipulator ◽  
Redundant Actuation ◽  
Planning Algorithm ◽  
Source Phase

Mechanisms with reconfigurability have become a trend in development of multi-functional robots which can adapt to unexpected environments and perform complicated tasks. This paper presents a novel metamorphic parallel manipulator with the ability to change its mobility through the phase change of a variable-axis (vA) joint integrated in each limb. The platform has 6 DOFs in the source phase and can reconfigure its mobility to 5, 4 and 3 resorting to redundant actuation. This leads to reconfigurability and multi-functionality of the parallel manipulator characterized by the mobility configuration variation. A control strategy and a trajectory planning algorithm for reconfiguring the mobility configuration of the manipulator are proposed and simulations are carried out to identify a proper way of reconfiguration.

Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

2020 ◽  
Author(s):  
Jing Huang ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Quadratic Programming ◽  
Real Time ◽  
Trajectory Planning ◽  
Autonomous Driving ◽  
Curse Of Dimensionality ◽  
Planning Problem ◽  
Feasible Set ◽  
Planning Algorithm ◽  
Vehicle Trajectory

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.

Sign in / Sign up

Export Citation Format

Share Document