112 citations · 552 across the 38 of their papers we have counts for
9 papers · 1 filter
Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem
Stefan Böhm, Martin Neumayer, Oliver Kramer +2
Cutting and Packing problems are occurring in different industries with a direct impact on the revenue of businesses. Generally, the goal in Cutting and Packing is to assign a set…
Multi-Agent Constrained Policy Optimisation
Shangding Gu, Jakub Grudzien Kuba, Munning Wen +6
Developing reinforcement learning algorithms that satisfy safety constraints is becoming increasingly important in real-world applications. In multi-agent reinforcement learning (M…
Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming
Tobias Kessler, Klemens Esterle, Alois Knoll
Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of ev…
A Biologically Inspired Global Localization System for Mobile Robots Using LiDAR Sensor
Genghang Zhuang, Carlo Cagnetta, Zhenshan Bing +2
Localization in the environment is an essential navigational capability for animals and mobile robots. In the indoor environment, the global localization problem remains challengin…
Towards Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network
Zhenshan Bing, Amir EI Sewisy, Genghang Zhuang +4
As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs),…
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions
Corvin Deboeser, Jordan Ivanchev, Thomas Braud +3
This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres. Although a large body of research has bee…
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning
Julian Bernhard, Robert Gieselmann, Klemens Esterle +1
Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. W…
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
Julian Bernhard, Stefan Pollok, Alois Knoll
For highly automated driving above SAE level~3, behavior generation algorithms must reliably consider the inherent uncertainties of the traffic environment, e.g. arising from the v…
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving
Julian Bernhard, Alois Knoll
Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these…