2 research outputs found
Engineering the Hardware/Software Interface for Robotic Platforms - A Comparison of Applied Model Checking with Prolog and Alloy
Robotic platforms serve different use cases ranging from experiments for
prototyping assistive applications up to embedded systems for realizing
cyber-physical systems in various domains. We are using 1:10 scale miniature
vehicles as a robotic platform to conduct research in the domain of
self-driving cars and collaborative vehicle fleets. Thus, experiments with
different sensors like e.g.~ultra-sonic, infrared, and rotary encoders need to
be prepared and realized using our vehicle platform. For each setup, we need to
configure the hardware/software interface board to handle all sensors and
actors. Therefore, we need to find a specific configuration setting for each
pin of the interface board that can handle our current hardware setup but which
is also flexible enough to support further sensors or actors for future use
cases. In this paper, we show how to model the domain of the configuration
space for a hardware/software interface board to enable model checking for
solving the tasks of finding any, all, and the best possible pin configuration.
We present results from a formal experiment applying the declarative languages
Alloy and Prolog to guide the process of engineering the hardware/software
interface for robotic platforms on the example of a configuration complexity up
to ten pins resulting in a configuration space greater than 14.5 million
possibilities. Our results show that our domain model in Alloy performs better
compared to Prolog to find feasible solutions for larger configurations with an
average time of 0.58s. To find the best solution, our model for Prolog performs
better taking only 1.38s for the largest desired configuration; however, this
important use case is currently not covered by the existing tools for the
hardware used as an example in this article.Comment: Presented at DSLRob 2013 (arXiv:cs/1312.5952
From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation
Context: Competitions for self-driving cars facilitated the development and
research in the domain of autonomous vehicles towards potential solutions for
the future mobility.
Objective: Miniature vehicles can bridge the gap between simulation-based
evaluations of algorithms relying on simplified models, and those
time-consuming vehicle tests on real-scale proving grounds.
Method: This article combines findings from a systematic literature review,
an in-depth analysis of results and technical concepts from contestants in a
competition for self-driving miniature cars, and experiences of participating
in the 2013 competition for self-driving cars.
Results: A simulation-based development platform for real-scale vehicles has
been adapted to support the development of a self-driving miniature car.
Furthermore, a standardized platform was designed and realized to enable
research and experiments in the context of future mobility solutions.
Conclusion: A clear separation between algorithm conceptualization and
validation in a model-based simulation environment enabled efficient and
riskless experiments and validation. The design of a reusable, low-cost, and
energy-efficient hardware architecture utilizing a standardized
software/hardware interface enables experiments, which would otherwise require
resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table