A Closed-Form Solution for Minimizing the Cycle Time in Motion Programs With Constant Velocity Segments

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Forrest W. Flocker ◽  
Ramiro H. Bravo

An important need for some dynamic systems is to design a periodic motion program that has a constant velocity segment for a specified time. A few examples of such systems are cam-follower systems used in continuous motion manufacturing, linear actuators, space-based scanners, and industrial robots. In this paper, the closed-form solution is given for a motion program that minimizes the cycle time subject to user-specified limits on positive and negative acceleration and jerk. The main benefit of minimizing cycle time is to maximize the throughput. Two motion programs that address the problem are presented and critically examined. For general applicability, the solution is presented in dimensionless form and an example is given to show its implementation to a typical problem. Conclusions regarding the profiles are drawn and given.

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Ramiro H. Bravo ◽  
Forrest W. Flocker

Cam-follower systems are ideally suited for many machine applications that require a specific and an accurate output motion. The required follower motion is achieved by carefully designing the shape or profile of the cam. Modern profiles are typically synthesized by piecing together a set of trigonometric and/or polynomial functions that satisfy the constraints. For most problems, there are many profile solutions that satisfy the constraints. In this paper, a relatively new optimization technique known as particle swarm optimization (PSO) is applied to the optimization of two different cam problems. The first example is a single-dwell cam in which the magnitude of the negative acceleration is minimized. The second example is a cam with a constant velocity segment in which the cycle time is optimized. The intent is to show the method in two different settings so that the reader can extend it to the optimization of any cam-follower problem. To illustrate the method, first the PSO method is applied to a mathematical function with two independent variables. Then, the method is used to find the cam profile that provides the minimum acceleration in a single-dwell cam using three independent variables. Finally, it is applied to obtain the minimum cycle time of a cam with a constant velocity segment using cubic interpolations and seven or more independent variables. The PSO method was very successful in all the optimization problems discussed in this paper. In the first cam problem, it significantly lowered the level of negative acceleration, while maintaining the positive acceleration at a constrained upper limit. The optimization procedure for the second cam problem found a very elegant five-segment solution. This solution results no matter how many initial segments or independent variables are chosen so long as there are at least five segments. Presented in this paper is the particle swarm optimizing technique that is applicable to many aspects of cam design. Two diverse examples were presented that illustrate how the PSO method can be used effectively in the optimization of cam-follower problems. In both illustrative examples, the PSO method proved to be robust, easy to implement, and suitable for minimizing a wide variety objective functions applicable to the design of cam-follower systems.


2013 ◽  
Vol 40 (2) ◽  
pp. 106-114
Author(s):  
J. Venetis ◽  
Aimilios (Preferred name Emilios) Sideridis

2021 ◽  
Vol 10 (7) ◽  
pp. 435
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.


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