Johannes Varga, Guenther Raidl, Steffen Limmer,
"Computational Methods for Scheduling the Charging and Assignment of an On-Site Shared Electric Vehicle Fleet",
IEEE Access, vol. 10, pp. 105786-105806, 2022.
Abstract
We investigate a fleet scheduling problem arising when a company has to manage its own fleet of electric vehicles. Aim is to assign given usage reservations to these vehicles and to devise a suitable
charging plan for all vehicles while minimizing a cost function. We formulate the problem as a compact mixed integer linear program, which we strengthen in several ways. As this model is hard to solve in practice, we perform a Benders decomposition,...
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Heike Brock, Thomas Weisswange, Serge Thill, Malte Jung, Aaron Horowitz,
"Exploring the Roles of Robots for Embodied Mediation
A Full-Day Workshop for ICRA 2022",
IEEE International Conference on Robotics and Automation (ICRA 2022), 2022.
Abstract
As technology increasingly permeates our lives
and demands more and more attention, it alters the very
foundation of how we form and maintain our relationships
with others. For example, robots and other embodied agents
may attract social attention away from others as they are often
particularly compelling to interact with. To envision technology
that connects us, and enriches rather than disrupts our social
lives, this workshop aims to de...
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Anna Belardinelli, Chao Wang, Michael Gienger,
"Explainable Human Robot Interaction for Imitation Learning in Augmented Reality",
Horizons of an Extended Robotics Reality (XR2) (workshop @IROS2022), 2022.
Abstract
Imitation learning could enable non-expert users
to teach new skills to robots in an interactive and intuitive
way. Still, it is often diffi cult to grasp what the robot knows
or to assess if a correct representation of the task is being
formed. Here, we introduce an Explainable AI (XAI) design
for human-robot interaction during learning by demonstration
of simple kitchen tasks in Augmented Reality. AR-XAI cues
are used to visualize the p...
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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao,
"A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems",
IEEE Transactions on Evolutionary Computation, 2022.
Abstract
The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from many real-world applications, such as waste collection, road gritting and mail delivery. However, few studies considered dynamic changes during the vehicles’ service, which can cause the original schedule infeasible or obsolete. The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorith...
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Radu Stoican, Angelo Cangelosi, Christian Goerick, Thomas Weisswange,
"Learning Human-Robot Interactions to improve Human-Human Collaboration",
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) - Workshop on Human Theory of Machines and Machine Theory of Mind for Human-Agent Teams (TOM4HAT), 2022.
Abstract
Most research in human-robot interaction focuses
on either the single-human case or the multi-human case where
there is direct interaction between the robot and each human.
The multi-human scenario in which some of the humans depend
on the robot, but do not interact with it directly, is currently
less studied. In this paper, we introduce a human-human-robot
collaboration task, in which the robot interacts directly with
only one of the hum...
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Moritz Bühler, Jürgen Adamy, Thomas Weisswange,
"Theory of Mind Based Assistive Communication in Complex Human Robot Cooperation",
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) - Workshop on Human Theory of Machines and Machine Theory of Mind for Human-Agent Teams (TOM4HAT), 2022.
Abstract
When cooperating with a human, a robot should
not only care about its environment and task but also develop an
understanding of the partner’s reasoning. To support its human
partner in complex tasks, the robot should share information.
However simply communicating everything it knows might
annoy and distract humans as not all information might be
relevant or novel in a given situation. To decide when and
what type of information should b...
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Patricia Wollstadt, Mariusz Bujny, Satchit Ramnath, Jami Shah, Duane Detwiler, Stefan Menzel,
"CarHoods10k: An Industry-grade Data Set for Representation Learning and Design Optimization in Engineering Applications",
IEEE Transactions on Evolutionary Computation Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software (BENCH), 2022.
Abstract
Research and development of cutting-edge optimization frameworks that exploit the advances in novel machine learning methods is dependent on the availability of large data sets resembling the targeted application. Especially in the engineering domain such high quality data sets are rare due to confidentiality concerns and generation costs, be it computational or manual efforts. Here, we introduce the OSU-Honda Automobile Hood Dataset (CarHoods10k...
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Felix Lanfermann, Sebastian Schmitt, Patricia Wollstadt,
"Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces",
IEEE International Conference on Data Mining Workshops (ICDMW), 2022.
Abstract
Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems.
Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features.
These subsets usually comprise features that characterize a design with respect to one specific context, for example, construc...
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Yali Wang,
"Multi-objective Evolutionary Algorithms for Optimal Scheduling",
Leiden University, 2022.
Abstract
The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; a...
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Sneha Saha, Leandro Minku, Xin Yao, Bernhard Sendhoff, Stefan Menzel,
"Exploiting 3D Variational Autoencoders For Interactive Vehicle Design ",
17th International Design Conference (Design 2022), 2022.
Abstract
In automotive digital development, 3D prototype creation is a team effort of designers and engineers, each contributing with creative ideas and technical design evaluations through means of computer simulations. To support the team in the 3D design ideation and exploration task, we propose an interactive 3D cooperative design system for assisted design explorations and faster performance estimations. We utilize the advantage of geometric deep lea...
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