A representation for comparing simulations and computing the purpose of geometric features

2001 ◽  
Vol 15 (2) ◽  
pp. 189-201
Author(s):  
THOMAS F. STAHOVICH ◽  
LEVENT BURAK KARA
Keyword(s):  
Computer Program ◽  
Dynamic Simulation ◽  
Directed Graphs ◽  
Kinematic Chain ◽  
The Other ◽  
Geometric Features ◽  
Causal Process ◽  
Rigid Body Dynamic ◽  
Causal Processes ◽  
Body Dynamic

We present a new representation that allows a rigid-body dynamic simulation to be described as a set of “causal-processes.” A causal-process is an interval of time during which both the behavior and the causes of the behavior remain qualitatively uniform. The representation consists of acyclic, directed graphs that are isomorphic to the flow of causality through the kinematic chain. Forces are the carriers of causality in this domain; thus they are central to the representation. We use this representation to compute the purposes of the geometric features on the parts of a device. To compute the purpose of a particular feature, we simulate the behavior of the device with and without the feature present. We then re-represent the two simulations as causal-processes and identify any causal-processes that exist in one simulation but not the other. Such processes are indicative of the feature's purpose. Because they are already causal descriptions of behavior, they can be directly translated into natural language descriptions of the feature's purpose. We have implemented our approach in a computer program called ExplainIT II.

scholarly journals Dynamic Simulation-Guided Design of Tumbling Magnetic Microrobots

2021 ◽  
pp. 1-26
Author(s):  
Jiayin Xie ◽  
Chenghao Bi ◽  
David J. Cappelleri ◽  
Nilanjan Chakraborty
Keyword(s):  
Dynamic Simulation ◽  
Point Contact ◽  
Small Scale ◽  
Incline Angle ◽  
Performance Simulation ◽  
Rigid Body Dynamic ◽  
Simulation Tools ◽  
Different Shapes ◽  
Magnetic Microrobots ◽  
Body Dynamic

Abstract Design of robots at the small scale is a trial-and-error based process, which is costly and time-consuming. There are few dynamic simulation tools available to accurately predict the motion or performance of untethered microrobots as they move over a substrate. At smaller length scales, the influence of adhesion and friction, which scales with surface area, becomes more pronounced. Thus, rigid body dynamic simulators, which implicitly assume that contact between two bodies can be modeled as point contact are not suitable. In this paper, we present techniques for simulating the motion of microrobots where there can be intermittent and non-point contact between the robot and the substrate. We use these techniques to study the motion of tumbling microrobots of different shapes and select shapes that are optimal for improving locomotion performance. Simulation results are verified using experimental data on linear velocity, maximum climbable incline angle, and microrobot trajectory. Microrobots with improved geometry were fabricated, but limitations in the fabrication process resulted in unexpected manufacturing errors and material/size scale adjustments. The developed simulation model is able to incorporate these limitations and emulate their effect on the microrobot's motion, reproducing the experimental behavior of the tumbling microrobots, further showcasing the effectiveness of having such a dynamic model.

scholarly journals Towards Dynamic Simulation Guided Optimal Design of Tumbling Microrobots

2019 ◽  
Author(s):  
Jiayin Xie ◽  
Chenghao Bi ◽  
David J. Cappelleri ◽  
Nilanjan Chakraborty
Keyword(s):  
Optimal Design ◽  
Dynamic Simulation ◽  
Point Contact ◽  
Small Scale ◽  
Trial And Error ◽  
Rigid Body Dynamic ◽  
Simulation Tools ◽  
Different Shapes ◽  
Body Dynamic ◽  
Two Bodies

Abstract Design of robots at the small scale is a trial-and-error based process, which is costly and time-consuming. There are no good dynamic simulation tools to predict the motion or performance of a microrobot as it moves against a substrate. At smaller length scales, the influence of adhesion and friction, which scales with surface area, becomes more pronounced. Thus, rigid body dynamic simulators, which implicitly assume that contact between two bodies can be modeled as point contact are not suitable. In this paper, we present techniques for simulating the motion of microrobots where there can be intermittent and non-point contact between the robot and the substrate. We use this simulator to study the motion of microrobots of different shapes and select shapes that are most promising for performing a given task.

Dynamic Simulation and Structure Optimization of the Folding Hoistable Mast Based on Adams

2014 ◽  
Vol 556-562 ◽  
pp. 1159-1164
Author(s):  
Qi Sheng Gao ◽  
Hai Tao Gu ◽  
Zhi Qiang Hu ◽  
Rong Zheng ◽  
Yang Lin
Keyword(s):  
Dynamic Stability ◽  
Dynamic Simulation ◽  
Parametric Optimization ◽  
Structure Optimization ◽  
Maximum Force ◽  
Rigid Body Dynamic ◽  
Simulation Results ◽  
In The Beginning ◽  
Body Dynamic ◽  
Structure Layout

In order to improve the safety and the dynamic stability of the hoistable mast, the method of parametric optimization was introduced and the dynamic model was established by the multi-rigid-body dynamic analysis soft of Adams in the beginning of the product design. Then, the dynamic simulation and the structure optimization were carried out. It was shown that the maximum force on the primary oil cylinder was reduced by 10%, the maximum force on the secondary oil cylinder was reduced by 8%, the structure layout of the mast was more reasonable and the dynamic stability were improved. Also, it was proved that the optimized structure of the hoistable mast was reasonable and feasible by simulation results.

The development of body and organ shape

BMC Zoology ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ansa E. Cobham ◽  
Christen K. Mirth
Keyword(s):  
Relative Position ◽  
Body Shape ◽  
Single Cells ◽  
The Other ◽  
Geometric Features ◽  
Main Text ◽  
Size Characterization ◽  
Organ Shape ◽  
The Many

Abstract Background Organisms show an incredibly diverse array of body and organ shapes that are both unique to their taxon and important for adapting to their environment. Achieving these specific shapes involves coordinating the many processes that transform single cells into complex organs, and regulating their growth so that they can function within a fully-formed body. Main text Conceptually, body and organ shape can be separated in two categories, although in practice these categories need not be mutually exclusive. Body shape results from the extent to which organs, or parts of organs, grow relative to each other. The patterns of relative organ size are characterized using allometry. Organ shape, on the other hand, is defined as the geometric features of an organ’s component parts excluding its size. Characterization of organ shape is frequently described by the relative position of homologous features, known as landmarks, distributed throughout the organ. These descriptions fall into the domain of geometric morphometrics. Conclusion In this review, we discuss the methods of characterizing body and organ shape, the developmental programs thought to underlie each, highlight when and how the mechanisms regulating body and organ shape might overlap, and provide our perspective on future avenues of research.

scholarly journals Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

2021 ◽  
Vol 11 (12) ◽  
pp. 5398
Author(s):  
Tomáš Kot ◽  
Zdenko Bobovský ◽  
Aleš Vysocký ◽  
Václav Krys ◽  
Jakub Šafařík ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Dynamic Simulation ◽  
Robot Manipulator ◽  
The Other ◽  
Robotic Arm ◽  
Robotic Cell ◽  
Wear Reduction ◽  
Fixed End ◽  
Robot Placement ◽  
Collaborative Robot

We describe a method for robotic cell optimization by changing the placement of the robot manipulator within the cell in applications with a fixed end-point trajectory. The goal is to reduce the overall robot joint wear and to prevent uneven joint wear when one or several joints are stressed more than the other joints. Joint wear is approximated by calculating the integral of the mechanical work of each joint during the whole trajectory, which depends on the joint angular velocity and torque. The method relies on using a dynamic simulation for the evaluation of the torques and velocities in robot joints for individual robot positions. Verification of the method was performed using CoppeliaSim and a laboratory robotic cell with the collaborative robot UR3. The results confirmed that, with proper robot base placement, the overall wear of the joints of a robotic arm could be reduced from 22% to 53% depending on the trajectory.

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