检索结果-内蒙古大学图书馆

I'll tell you what I want: Categorization of Pareto Fronts for Automated Rule-based Decision-Making

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2022年第16期55卷 376-381页

作者： Matthias K. Hoffmann Thomas Schmitt Kathrin Flaßkamp Systems Modeling and Simulation Saarland University Saarbrücken Germany Control Methods & Robotics Lab Technical University of Darmstadt Darmstadt Germany

The application of Pareto optimization in control engineering requires decision-making as a downstream step since one solution has to be selected from the set of computed Pareto optimal points. Economic Model Predictive control (MPC) requires repeated optimization and, in multi-objective optimization problems, selection of Pareto optimal points at every time step. Thus, designing an automated selection strategy is favorable. However, it is challenging to come up with a measure – possibly based on a Pareto front analysis – that characterizes preferred Pareto optimal points uniformly across different Pareto fronts. In this work, we first discuss these difficulties for application within MPC and then suggest a solution based on unsupervised machine learning methods. The approach is based on categorizing Pareto fronts as an intermediate step. This allows generating an individual set of rules for every category. Thereby, the human decision-maker's preferences can be modeled more accurately and the selection of a Pareto optimal solution becomes less time-consuming while breaking down the decision-making process into a selection solely based on the Pareto front's shape. Here, the measures act as anchor points for the decision rules. Lastly, a novel knee point measure, i.e. an approximation of the Pareto front's curvature, is presented and used for a knee point-focused categorization. The proposed algorithm is successfully applied to a case study for an energy management system. Moreover, we compare our method to using singular measures for decision-making in order to show its higher flexibility leading to better performance of the controller.

关键词： Multi-objective Optimization Model predictive control Decision-making Energy Management Systems Optimal control

Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Damerow, Florian Puphal, Tim Flade, Benedict Li, Yuda Eggert, Julian The Control Methods and Robotics Lab Technical University of Darmstadt Darmstadt64283 Germany Europe Carl-Legien-Str. 30 Offenbach63073 Germany

This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure. Based on the area with reduced visibility, we first model scene entities that pose a potential risk without being visually perceivable yet. Then, we predict a worst-case trajectory in the survival analysis for collision risk estimation. Resulting risk indicators are utilized to evaluate the driver's current behavior, to warn the driver in critical situations, to give suggestions on how to act safely or to plan safe trajectories. We validate our approach by applying the resulting intersection warning system on real world scenarios. The proposed system's behavior reveals to mimic the general behavior of a correctly acting human driver. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Gradient play in n-cluster games with zero-order information

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Tatarenko, Tatiana Zimmermann, Jan Adamy, Jürgen Control Methods and Robotics Lab The TU Darmstadt Germany

We study a distributed approach for seeking a Nash equilibrium in n-cluster games with strictly monotone mappings. Each player within each cluster has access to the current value of her own smooth local cost function estimated by a zero-order oracle at some query point. We assume the agents to be able to communicate with their neighbors in the same cluster over some undirected graph. The goal of the agents in the cluster is to minimize their collective cost. This cost depends, however, on actions of agents from other clusters. Thus, a game between the clusters is to be solved. We present a distributed gradient play algorithm for determining a Nash equilibrium in this game. The algorithm takes into account the communication settings and zero-order information under consideration. We prove almost sure convergence of this algorithm to a Nash equilibrium given appropriate estimations of the local cost functions' gradients. © 2021, CC BY.

关键词： Computation theory

Gradient Play in n-Cluster Games with Zero-Order Information

学校读者我要写书评

暂无评论

Gradient Play in n-Cluster Games with Zero-Order Information

IEEE Conference on Decision and control

作者： Tatiana Tatarenko Jan Zimmermann Jürgen Adamy Control Methods and Robotics Lab TU Darmstadt Germany

ISBN: (纸本)9781665436601

We study a distributed approach for seeking a Nash equilibrium in n-cluster games with strictly monotone mappings. Each player within each cluster has access to the current value of its own smooth local cost function estimated by a zero-order oracle at some query point. We assume the agents to be able to communicate with their neighbors in the same cluster over some undirected graph. The goal of the agents in the cluster is to minimize their collective cost. This cost depends, however, on actions of agents from other clusters. Thus, a game between the clusters is to be solved. We present a distributed gradient play algorithm for determining a Nash equilibrium in this game. The algorithm takes into account the communication settings and zero-order information under consideration. We prove almost sure convergence of this algorithm to a Nash equilibrium given appropriate estimations of the local cost functions’ gradients.

关键词： Costs Conferences Clustering algorithms Estimation Games Nash equilibrium Cost function

Non-cooperative, payoff-based game in application to an energy-management-problem between microgrids

学校读者我要写书评

暂无评论

Non-cooperative, payoff-based game in application to an ener...

2020 IEEE Power and Energy Student Summit, PESS 2020

作者： Götzinger, Jonathan Hott, Ruben Kuroptev, Kirill Uetz, Pascal Zimmermann, Jan Darmstadt University of Technology Department of Electrical Engineering and Information Technology Control Methods and Robotics Germany

ISBN: (纸本)9783800753383

The integration of renewable energy into the power grid demands new solutions to decentralize energy networks. For these decentralized energy grids new demand side management systems are needed. In this paper we find a payoff-based algorithm that leads into an optimal buying strategy for microgrids at the day-ahead market. In our approach these microgrids consist of an arbitrary amount of generators and energy storages. We prove that a payoff-based projection algorithm finds a Nash equilibrium for the non-cooperative convex game between the microgrids. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Game theory

Theory of mind based assistive communication in complex human robot cooperation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Buehler, Moritz C. Adamy, Jürgen Weisswange, Thomas H. Control Methods & Robotics Lab Technical University Darmstadt Germany Honda Research Institute Europe GmbH Germany

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 can share information that it knows. However simply communicating everything will annoy and distract humans since they might already be aware of and not all information is relevant in the current situation. The questions when and what type of information the human needs, are addressed through the concept of Theory of Mind based Communication which selects information sharing actions based on evaluation of relevance and an estimation of human beliefs. We integrate this into a communication assistant to support humans in a cooperative setting and evaluate performance benefits. We designed a human robot Sushi making task that is challenging for the human and generates different situations where humans are unaware and communication could be beneficial. We evaluate the influence of the human centric communication concept on performance with a user study. Compared to the condition without information exchange, assisted participants can recover from unawareness much earlier. The approach respects the costs of communication and balances interruptions better than other approaches. By providing information adapted to specific situations, the robot does not instruct but enable the human to make good decision. © 2021, CC BY.

关键词： Information dissemination

Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs

学校读者我要写书评

暂无评论

Hybrid Evolutionary Approach to Multi-objective Path Plannin...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Nikolas Hohmann Mariusz Bujny Jürgen Adamy Markus Olhofer Control Methods & Robotics Lab Technical University of Darmstadt Darmstadt Germany Honda Research Institute Europe GmbH Offenbach Germany

ISBN: (纸本)9781728190495

The goal of Multi-Objective Path Planning (MOPP) is to find Pareto-optimal paths for autonomous agents with respect to several optimization goals like minimizing risk, path length, travel time, or energy consumption. In this work, we formulate a MOPP for Unmanned Aerial Vehicles (UAVs). We utilize a path representation based on Non-Uniform Rational B-Splines (NURBS) and propose a hybrid evolutionary approach combining an Evolution Strategy (ES) with the exact Dijkstra algorithm. Moreover, we compare our approach in a statistical analysis to state-of-the-art exact (Dijkstra's algorithm), gradient-based (L-BFGS-B), and evolutionary (NSGA-II) algorithms with respect to calculation time and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 2D urban path planning scenario based on real-world data exported from OpenStreetMap. The examination's results indicate that our approach is able to find significantly better solutions for the formulated problem than standard Evolutionary Algorithms (EAs). Moreover, the proposed method is able to obtain more diverse sets of trade-off solutions for different objectives than the standard exact approaches. Thus, the method combines the strengths of both approaches.

关键词： Surface reconstruction Energy consumption Statistical analysis Diversity reception Evolutionary computation Path planning Surface topography

Projected Push-Sum Gradient Descent-Ascent for Convex Optimization with Application to Economic Dispatch Problems

学校读者我要写书评

暂无评论

Projected Push-Sum Gradient Descent-Ascent for Convex Optimi...

IEEE Conference on Decision and control

作者： Jan Zimmermann Tatiana Tatarenko Volker Willert Jürgen Adamy Control Methods and Robotics Lab TU Darmstadt Germany

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part of the global objective function. The agents are able to exchange information over a directed, weighted communication graph, which can be represented as a column-stochastic matrix. The algorithm combines an adjusted push-sum consensus protocol for information diffusion and a gradient descent-ascent on the local cost functions, providing convergence to the optimum of their sum. We provide results on a reformulation of the push-sum into single matrix updates and prove convergence of the proposed algorithm to an optimal solution, given standard assumptions in distributed optimization. The algorithm is applied to a distributed economic dispatch problem, in which the constraints can be expressed in local and global subsets.

关键词： Optimization Convergence Cost function Standards Privacy Generators Economics

Projected Push-Sum Gradient Descent-Ascent for Convex Optimization with Application to Economic Dispatch Problems

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zimmermann, Jan Tatarenko, Tatiana Willert, Volker Adamy, Jürgen Control Methods and Robotics Lab TU Darmstadt Germany

We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part of the global objective function. The agents are able to exchange information over a directed, weighted communication graph, which can be represented as a column-stochastic matrix. The algorithm combines an adjusted push-sum consensus protocol for information diffusion and a gradient descent-ascent on the local cost functions, providing convergence to the optimum of their sum. We provide results on a reformulation of the push-sum into single matrix-updates and prove convergence of the proposed algorithm to an optimal solution, given standard assumptions in distributed optimization. The algorithm is applied to a distributed economic dispatch problem, in which the constraints can be expressed in local and global subsets. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods