
Christof BüskensUniversity of Bremen | Uni Bremen · Center for Industrial Mathematics (ZeTeM)
Christof Büskens
Prof. Dr.
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235
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2,107
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Introduction
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April 2004 - present
Education
August 1987 - December 1993
Publications
Publications (235)
In light of the recent increase in polar shipping and potential future increase with continued climate change reliable routing in ice-covered waters becomes increasingly important for environmental, economic and safety concerns. Dependable route suggestions have the potential to reduce travel times through polar waters significantly. We apply the A...
Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A* path planning have proven their ability to sol...
Forecast-based energy management can play a large role in a smarter and more efficient use of renewable energies based on demand side management. Using approaches such as model predictive control, individual consumption devices can be shifted within operation constraints so that their electricity consumption optimally matches generation. In agricul...
Maritime transport in narrow canals and crowded harbours has received considerable attention during the past few years due to the incident in the Suez Canal. Maneuvering a large container ship effectively, ensuring safety in a confined space, is a challenging problem. One way of overcoming this challenge is using tugboats in critical areas under th...
In the realm of autonomous driving, ensuring a secure halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving detected system boundaries of crucial modules. This article presents a novel methodology for swiftly calculating safe stop trajectories. We utilize a clustering method to ca...
The Complete Coverage Path Planning (CCPP) problem is a subfield of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil...
Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields. This work offers a comprehensive approach to CCPP for agricultural vehicles with curvature constraints. Our metho...
A series of technical and social aspects have led the pressure to increase energy efficiency and reduce the level of environmental impact in agricultural production. Crop producers and equipment manufacturers have identified computerized assistance in operation management as an indispensable tool to optimize agricultural processes. A central place...
Light detection and ranging (LiDAR) sensors have proven to be a valuable tool to gather spatial information about the environment and are a crucial component in perception of autonomous systems. In the agricultural domain, state‐of‐the‐art algorithms for detection, classification, and tracking often utilize a combination of LiDAR and camera by fusi...
Accurate models are crucial for simulating, optimizing, and controlling real‐world processes. Parameter identification—the task of estimating the unknown parameters of a dynamical system based on measurements—is challenging and there exist various methods to approach it. Integration‐based methods, such as shooting methods and full discretization, a...
Traffic forecasting is an important task for transportation engineering as it helps authorities to plan and control traffic flow, detect congestion, and reduce environmental impact. Deep learning techniques have gained traction in handling such complex datasets, but require expertise in neural architecture engineering, often beyond the scope of tra...
In the transition away from fossil energy, the use of renewable energy is gaining more importance. Agricultural businesses have a high energy demand as well as space for the installation of renewable energies, such as photovoltaic systems (PV). However, with the growing difference between electricity prices and feed‐in tariffs, the profitability of...
Renewable energy sources generated locally are becoming increasingly popular in order to achieve carbon neutrality in the near future. Some of these sources are being used in neighbourhood (local, or energy communities) grids to achieve high levels of self-sufficiency. However, the objectives of the local grid and the distribution grid to which it...
Nonlinear optimization problems arise in the context of many engineering processes and other real‐world applications. Depending on the complexity of modeled phenomena and constraints, such problems can feature multiple local solutions of different nature. Primarily, the quality of a solution is measured by the value of an objective function, and he...
With the transition towards renewable energy underway, demand‐side management together with the local generation of renewable energy is receiving growing attention. Optimizing the self‐consumption of locally produced renewable energy can not only have financial benefits for the respective household or business (and improve their autarky from increa...
The Complete Coverage Path Planning (CCPP) problem is a sub-field of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soi...
Autonomous trajectory generation plays an essential role in the navigation of vehicles in space as well as in terrestrial scenarios, i.e. in the air, on solid ground, or water. For the latter, the navigation of ships in ports has specific challenges since ship dynamics are highly nonlinear with limited agility, while the manoeuvre space in ports is...
Locally generated renewable energy resources are gaining popularity with the aim of reaching carbon-neutral energy in the foresee-
able future. Some of these sources have been deployed within the neighbourhood (local) grids to achieve high autarchy. However,
the main objectives of the local grid and the objectives of the distribution grid where the...
Short-term flow forecast is a fundamental key in intelligent transportation planning. Often accurate predictions are provided by the predictive models the most adapted to the nature of the addressed problem. In this paper we present a k-Nearest Neighbor approach (E-KNN) enhanced by taking advantage of traffic attributes. The proposed model is appli...
Optimization problems in the context of real-world applications often suffer from having multiple local solutions. When parameters within such a problem change, the corresponding solutions might differ significantly. However, it can be of interest for a practitioner to remain close to a nominal solution. In this work, we propose a novel concept to...
Data-driven models have recently proved to be a very powerful tool to extract relevant information from different kinds of datasets. However, datasets are often subject to multiple anomalies, including the loss of important parts of entries. In the context of intelligent transportation, we examine in this paper the impact of data loss on the behavi...
In many application areas that involve solving parametric optimization problems, it is desirable that solutions of perturbed problems do not deviate too much from the original ones. In this work, we develop a criterion which characterizes the stability of a local solution of an unconstrained parametric nonlinear program with respect to parameter pe...
Exploring unknown environments is one of the main applications of mobile robotic systems. Since explorative trajectories can be used to gather information on the environment as well as on the internal dynamics of the robotic system, we propose a combined parameter estimation and mapping approach consisting of three steps: first, the parameter estim...
An increased use of renewable energy could significantly reduce greenhouse gas emissions but is difficult to realize since most renewable energy sources underlie volatile availability. Making use of storage devices and scheduling consumers to times when energy is available allows to increase the amount of renewable energy that can be used. For this...
In this contribution we consider linear quadratic regulator problems subject to parameter perturbations within the system dynamics. Due to the perturbations, optimality of the regulator would be lost. Iteratively recomputing the optimal feedback matrix is likely to be too costly for real‐time applications, but approximated updates using parametric...
The digital revolution, especially in the field of manufacturing, has great potential to change the economy sustainably. In this work, the development of methods for predictive maintenance and condition monitoring is a central focus. Data‐based models for drive technology in automotive production are investigated in order to generate adequate model...
Due to their inherently complex structure, bilevel optimization problems are often transformed into single‐level problems in order to make them numerically solvable. In this work, we present a novel reformulation strategy, in which we introduce sets of variables and constraints that represent the process of numerically solving the lower‐level probl...
In this work, we formulate and solve the problem of finding the ball of maximum radius around a local minimum of a nonlinear optimization problem, which is invariant with respect to the gradient descent method. This problem arises in the context of solving sequences of nonlinear optimization problems, in which one usually strives to converge to qua...
In many modern applications, a central task is to model a dynamical behavior with ordinary differential equations. A common way to identify parameters within such a model is to fit its output against given measurements. Since it can be difficult to understand the connection between measurements and the parameter identification result, it is desirab...
The development of maritime transportation, in terms of the size and traffic volume of container ships, is increasingly posing technical challenges especially in crowded harbors and narrow canals. To ensure safe and efficient operation, tugboats are used for assisting large ships in such critical areas under the supervision of the responsible pilot...
Der Ausbau erneuerbarer Energien ist eine zentrale Säule der Energiewende. Das Projekt SmartFarm2 des Steinbeis-Forschungszentrums Optimierung, Steuerung und Regelung zeigt gemeinsam mit Projektpartnern die Potenziale für den Eigenverbrauch selbsterzeugter Energie auf. Für die Entwicklung automatisierter Software wird ein Testfeld von über hundert...
WORHP is a mathematical software library for solving continuous large-scale nonlinear optimization problems numerically.
In the transfer from fossil fuels to renewable energies, grid operators, companies and farms develop an increasing interest in smart energy management systems which can reduce their energy expenses. This requires sufficiently detailed models of the underlying components and forecasts of generation and consumption over future time horizons. In this...
Grasping objects under water is even today one of the biggest challenges when operating robotic systems let it be tele-operated or autonomous. Currently, most of the manipulation tasks under water are performed using remotely operated vehicles (ROVs) which handle all industrial maintenance and inspection tasks where there is intervention involved....
The very core of an active underwater intervention is the ability for manipulation. Precise, dexterous and autonomous underwater manipulation requires extremities and end effectors, that are so robust that they withstand the environmental conditions and are able to apply enough force to perform the usually heavy work. At the same time, the systems...
Explaining concepts of optimization or even relevant algorithms to outsiders, like pupils or contacts from the industry, is a difficult task. Based on the idea of an augmented reality sandbox explaining geoscience processes we developed an exhibition piece to visually demonstrate fundamental ideas of numerical algorithms in the field of optimizatio...
The development of driving functions for autonomous vehicles in urban environments is still a challenging task. In comparison with driving on motorways, a wide variety of moving road users, such as pedestrians or cyclists, but also the strongly varying and sometimes very narrow road layout pose special challenges. The ability to make fast decisions...
We consider the design and control problem of concentric tubes used in stereotactic neurosurgery. The goal is to optimally reach a configuration of the cannula linking an entry point on the skullcap to a pre-specified region inside the brain. Key issues related to this task are the mechanical behaviour of the cannula and the topography of the brain...
When applying the single shooting approach to solve nonlinear dynamic parameter identification problems, difficulties like undesired minima can occur. To circumvent this, we apply a homotopy continuation to find global minima. In contrast to the literature, we reduce the resulting series of optimization problems to a single one which leads us to th...
The development of driving functions for autonomous vehicles in urban environments is still a challenging task. In comparison with driving on motorways, a wide variety of moving road users, such as pedestrians or cyclists, but also the strongly varying and sometimes very narrow road layout pose special challenges. The ability to make fast decisions...
The publication on hand presents the ongoing developments of a networked control system for maritime application within the joint project GALILEOnautic 2. It is based on the joint project GALILEOnautic, where a system was developed which enables cooperative maneuvering of networked vessels in a harbor environment. The present paper focuses on the p...
We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy opti...
Algorithms for controlling fully autonomous systems must meet especially high requirements with respect to safety and robustness. A particularly challenging example are autonomous deep space missions, which we investigated in several projects. In this context, we showed that a safe and robust autonomous system can be realized through nonlinear mode...
In this article, an approach for parking with trailer is designed. The path planning for backward parking of a truck and trailer vehicle is a central aspect, especially the path optimization. For calculating the path a polynomial approach is applied. The used kinematic single-track model and assumed smooth signals lead to a polynomial approach, whe...
We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and
steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy opti...
In many industrial applications solutions of optimal control problems are used, where the need for low computational times outweighs the need for absolute optimality. For solutions of fully discretized optimal control problems we propose two methods to approximate the solutions of problems with modified parameter values in real-time by using sensit...
This paper presents the development of a networked control system for maritime autonomous shipping within the joint project GALILEOnautic 2 (grant 50NA1808). The project aims for autonomous navigation and optimal maneuvering of cooperative vessels in a harbor environment. In this publication, the networked vehicles dynamically estimate position, ve...
In this paper, we present a strategy for incorporating occupancy grid maps into local trajectory optimization methods to allow for safe autonomous navigation. We construct polygons as surface representations of the locally visible area and merge them with the aid of third-party software to form connected free-spaces. The method can be applied to an...
In this article, an approach for parking with trailer is designed. The path planning for backward parking of a truck and trailer vehicle is a central aspect, especially the path optimization. For calculating the path a polynomial approach is applied. The used kinematic single-track model and assumed smooth signals lead to a polynomial approach, whe...
In this work, we apply transcription methods known from optimal control to nonlinear dynamic parameter identification problems. We analyze and compare the methods with the help of an application example from robotics. More specifically, we investigate their robustness against varying initial parameters and show numerically, that an increasing numbe...
Nowadays Parameter Identification Problems (PIP) are one of the main tasks in many engineering applications. For example, a model for the dynamic behavior of a robotic system has to be found. Before we can work with a system, e.g. solve an optimal control problem (OCP) to optimally perform an assigned task, we have to identify unknown parameters wi...
Numerical methods for parameter identification of dynamical systems are based on matching model outputs to measurements. Computing the model output requires numerical integration techniques, though. In this contribution, we study the role of integration methods for mechanical systems. Since mechanical systems have characteristic properties, using s...
To achieve true autonomy during critical mission phases in deep space, it is imperative to know which states are attainable from a given starting point using only admissible control inputs. These attainable states form the reachable set. If the latter is computed in real time, this can provide a system for Guidance, Navigation and Control on‐board...
This paper shows how a discrete approximation of a Pareto front can be refined with polynomial interpolation. For this we exploit the information given by the discrete samples of the Pareto front and in addition we use parametric sensitivity information from these samples. The pararmetric sensitivities are afterwards used to ensure feasibility of t...
https://indico.esa.int/event/224/contributions/3888/
Online optimization and trajectory planning are key aspects of autonomous deep space missions. Taking into account individual target criteria, such as time or energy optimality, any spacecraft maneuver can be traced back to a general problem definition of the form "move the spacecraft from its initial state to a desired final state, while consideri...
The ongoing changes in power supply systems cause, besides all technical challenges, increasing costs for electrical energy. Households and small or medium-sized companies and farms with high energy demand are interested in decreasing their expenses by installing renewable generation devices, e.g. solar panels or wind turbines, together with energy...
For large-scale nonlinear programming most state-of-the-art solvers apply an interior-point strategy. These very efficient algorithms usually suffer the drawback that the optimal solution of a previous run cannot be exploited for warmstart the solver for the re-optimization of a similar optimization problem. In this paper, we therefore propose a se...
We apply direct methods from numerical optimal control to solve nonlinear dynamic parameter identification problems. The focus is on comparing shooting methods and a full discretization approach regarding approximation quality and efficiency. For an idealized robotic system, we show that the full discretization approach is beneficial, since it is m...
Interior-point methods have been shown to be very efficient for large-scale nonlinear programming. The combination with penalty methods increases their robustness due to the regularization of the constraints caused by the penalty term. In this paper a primal–dual penalty-interior-point algorithm is proposed, that is based on an augmented Lagrangian...
Nonlinear optimization problems that arise in real-world applications usually depend on parameter data. Parametric sensitivity analysis is concerned with the effects on the optimal solution caused by changes of these. The calculated sensitivities are of high interest because they improve the understanding of the optimal solution and allow the formu...
We present an optimization-based approach for trajectory planning and control of a maneuverable melting probe with a high number of binary control variables. The dynamics of the system are modeled by a set of ordinary differential equations with a priori knowledge of system parameters of the melting process. The original planning problem is handled...
Solving an engineering problem starts with the description of the problem in mathematical formulas and the identification of parameters. The generated model is then used to simulate the behavior of the underlying system. However, most systems are not static. They change over time. Thus, the model needs to be adapted to these activities otherwise th...
This paper shows how an electrical distribution network can be modeled as constraints of a continuous nonlinear optimization problem. The objective is to minimize the deviation from a given nominal voltage. We explain how this setup can be used to optimize wire diameters within the network. Thus we find the optimal expansion of the network by ident...
The goal of the project GALILEOnautic is to develop a system for autonomous navigation and optimal manoeuvring of cooperative ships within safety-critical areas. In this context, many challenges arise in the field of optimization and optimal control. The research presented here addresses one of them, namely, the calculation of optimal trajectories...
Mechanical systems have structural properties, e.g. symplecticity, symmetry, and a specific energy behavior, which get lost in standard integration methods. Therefore, symplectic integration methods are used in simulation and control of mechanical systems. This paper combines two methods of the class of structure-preserving control methods, namely...
In the project SmartFarm we develop a method that automatically decides which renewable energies are profitable to use at the farm for the next hours. To this means optimization methods are used on various levels. Initially, all necessary data is measured on a demonstration object. This data forms the basis for data based learning methods for the m...
In dry machining of high precision parts shape deviations mainly arise due to thermo-elastic expansion during cutting and machining induced residual stresses. In order to meet higher quality standards when applying the ecologically and economically favourable concept of dry machining, predictive compensation strategies have to be developed which ta...
Using the application of a container crane in a high rack warehouse, the problem class of tracking problems is introduced, where the system has to reach a possible time dependent reference value while maintaining stability and safety.
These problems are a subclass of nonlinear model predictive control problems, and can be solved numerically as opti...
Autonomous driving is no longer a subject of science fiction. Instead it has become a field of highly topical developments and has already reached numerous milestones. The Audi Autonomous Driving Cup provides a stage for students to participate in this development process. This competition, carried out in Germany, Austria and Switzerland, provides...