arXiv subjects

Matthias Mayer

Publications and source records attributed to Matthias Mayer.

5 recordsLinked to original sources

arXiv2025

Holistic Optimization of Modular Robots

Modular robots have the potential to revolutionize automation, as one can optimize their composition for any given task. However, finding optimal compositions is non-trivial. In addition, different compositions require different base positions and trajectories to fully use the potential of modular robots. We address this problem holistically for the first time by jointly optimizing the composition, base placement, and trajectory to minimize the cycle time of a given task. Our approach is evaluated on over 300 industrial benchmarks requiring point-to-point movements. Overall, we reduce cycle time by up to 25 % and find feasible solutions in twice as many benchmarks compared to optimizing the module composition alone. In the first real-world validation of modular robots optimized for point-to-point movement, we find that the optimized robot is successfully deployed in nine out of ten cases in less than an hour.

cs.RO↗

arXiv2025

Smart placement, faster robots-a comparison of algorithms for robot base-pose optimization

Robotic automation is a key technology that increases the efficiency and flexibility of manufacturing processes. However, one of the challenges in deploying robots in novel environments is finding the optimal base pose for the robot, which affects its reachability and deployment cost. Yet, existing research on automatically optimizing the base pose of robots has not been compared. We address this problem by optimizing the base pose of industrial robots with Bayesian optimization (BO), exhaustive search (ES), genetic algorithms (GAs), and stochastic gradient descent (SGD), and we find that all algorithms can reduce the cycle time for various evaluated tasks in synthetic and real-world environments. Stochastic gradient descent shows superior performance with regard to the success rate, solving more than 90% of our real-world tasks, while genetic algorithms show the lowest final costs. All benchmarks and implemented methods are available as baselines against which novel approaches can be compared.

cs.RO↗

arXiv2022

Timor Python: A Toolbox for Industrial Modular Robotics

Modular Reconfigurable Robots (MRRs) represent an exciting path forward for industrial robotics, opening up new possibilities for robot design. Compared to monolithic manipulators, they promise greater flexibility, improved maintainability, and cost-efficiency. However, there is no tool or standardized way to model and simulate assemblies of modules in the same way it has been done for robotic manipulators for decades. We introduce the Toolbox for Industrial Modular Robotics (Timor), a Python toolbox to bridge this gap and integrate modular robotics into existing simulation and optimization pipelines. Our open-source library offers model generation and task-based configuration optimization for MRRs. It can easily be integrated with existing simulation tools - not least by offering URDF export of arbitrary modular robot assemblies. Moreover, our experimental study demonstrates the effectiveness of Timor as a tool for designing modular robots optimized for specific use cases.

cs.RO↗

arXiv2022

CoBRA: A Composable Benchmark for Robotics Applications

Selecting an optimal robot, its base pose, and trajectory for a given task is currently mainly done by human expertise or trial and error. To evaluate automatic approaches to this combined optimization problem, we introduce a benchmark suite encompassing a unified format for robots, environments, and task descriptions. Our benchmark suite is especially useful for modular robots, where the multitude of robots that can be assembled creates a host of additional parameters to optimize. We include tasks such as machine tending and welding in synthetic environments and 3D scans of real-world machine shops. All benchmarks are accessible through https://cobra.cps.cit.tum.de, a platform to conveniently share, reference, and compare tasks, robot models, and solutions.

cs.RO↗

arXiv2001

Continuous wavelet transforms from semidirect products: Cyclic representations and Plancherel measure

Continuous wavelet transforms arising from the quasiregular representation of a semidirect product of a vector group with a matrix group -- the so-called dilation group -- have been studied by various authors. Recently the attention has shifted from the irreducible case to include more general dilation groups, for instance cyclic (more generally: discrete) or one-parameter groups. These groups do not give rise to irreducible square-integrable representations, yet it is possible (and quite simple) to give admissibility conditions for a large class of them. We put these results in a theoretical context by establishing a connection to the Plancherel theory of the semidirect products, and show how the admissibility conditions relate to abstract admissibility conditions which use Plancherel theory.

math-ph↗