Simulation of Multi-Robot Teams with Flexible Level of Detail

Friedmann, Martin; Petersen, Kai; Stryk, Oskar von

doi:10.1007/978-3-540-89076-8_7

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…For an analysis of the different levels of abstraction see Friedmann (2010). In microscopic models, the behavior of each individual robot is also explicitly modeled.…”

Section: Microscopic Modelsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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“…For an analysis of the different levels of abstraction see Friedmann (2010). In microscopic models, the behavior of each individual robot is also explicitly modeled.…”

Section: Microscopic Modelsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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“…To allow for arbitrary structures, these modeling entities can be combined in any order, so that one is not limited to the structure described above. Similar approaches have been used successfully for the simulation of industrial robots [10], biomechanical systems [13] and autonomous mobile robots [9].…”

Section: Model Of Robot Dynamics and Milling Forcementioning

confidence: 99%

Model-based off-line compensation of path deviation for industrial robots in milling applications

Reinl¹,

Friedmann²,

Bauer³

et al. 2011

2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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The scope of applications for industrial robots is limited in cases with strong forces at the end effector and high positioning and path accuracies required. Thus, their use in machining applications as a cost-saving, flexible alternative for machining tools is restricted due to mechanical compliance. A model-based off-line concept is presented to analyze, predict, and compensate the resulting path deviation of the robot under process force in milling applications. For this purpose a rigid multi-body dynamics model of the robot extended with additional joint elasticities and tilting effects is coupled with a material removal simulation providing the process forces. After systematically adjusting model parameters, an efficient simulation-based path correction strategy shows significant improvements of path accuracy. The general framework is applicable to any tree structured robots and allows for sensitivity analysis with respect to arbitrary model parameters. I. MOTIVATIONMajor fields of machining applications for industrial robots are automated pre-machining, deburring and fettling of cast parts or trimming of carbon fiber reinforced laminate. Due to a kinematic structure with usually six axes industrial robots can cover a large working space and are able to reach difficult work piece positions, so that they can be applied to perform complex machining operations. Therefore, compared to standard machine tools, industrial robots on the one hand offer an economic machining while they do only reach a limited absolute and repeat accuracy on the other hand; e.g. the repeat accuracy of the industrial robot used for the research presented in this paper is ±0.06 mm [11].Under high process load, as appears in milling operations, an additional deviation of the tool center point (TCP) occurs. Measured deflections of 0.25 mm under loads of 100 N in earlier tests [2] confirmed the expected compliance, which is resulting from the low structural stiffness of the serial robot kinematics.In milling applications, the effective process forces lead to significant trajectory deviations that are resulting in a variation of the cutting condition at the cutter. Thus, milling with industrial robots is characterized by the strong interaction of the cutting process and the mechanical robot structure. It is observed, that deviations mainly consist of a static offset

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“…Additionally, custom modeling elements like nonlinear springs, Hill-type muscles, or specific joints can be created and added to the library. Similar approaches to the modeling of MBS have been applied successfully to industrial robots [1], biomechanical systems [2] and autonomous robots [3].…”

Section: Modeling Of Multi-body-systems and Drivesmentioning

confidence: 99%

A modular and efficient approach to computational modeling and sensitivity analysis of robot and human motion dynamics

Friedmann

Wojtusch

Stryk

2012

Proc Appl Math and Mech

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In this paper a new class library for the computation of the forward multi-body-system (MBS) dynamics of robots and biomechanical models of human motion is presented. By the developed modular modeling approach the library can be flexibly extended by specific modeling elements like joints with specific geometry or different muscle models and thus can be applied efficiently for a number of dynamic simulation and optimization problems. The library not only provides several methods for solving the forward dynamics problem (like articulated body or composite rigid body algorithms) which can transparently be exchanged. Moreover, the numerical solution of optimal control problems, like in the forward dynamics optimization of human motion, is significantly facilitated by the computation of the sensitivity matrix with respect to the control variables.Examples are given to demonstrate the efficiency of the approach.

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Simulation of Multi-Robot Teams with Flexible Level of Detail

Cited by 12 publications

References 9 publications

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics: a review from the swarm engineering perspective

Model-based off-line compensation of path deviation for industrial robots in milling applications

A modular and efficient approach to computational modeling and sensitivity analysis of robot and human motion dynamics

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