Martin Buss

• Technical University of Munich

This paper presents a model predictive control (MPC) method for redundant robots controlling multiple hierarchical tasks formulated as multi-layer constrained optimal control problems (OCPs). The proposed method, named hierarchical incremental MPC (HIMPC), is robust to dynamic uncertainties, untethered from kinematic/algorithmic singularities, and...

Grasping and manipulating various kinds of objects cooperatively is the core skill of a dual-arm robot when deployed as an autonomous agent in a human-centered environment. This requires fully exploiting the robot's versatility and dexterity. In this work, we propose a general framework for dual-arm manipulators that contains two correlative module...

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application toward continuous robotic tracking control, especially for high-dimensional robots. This article proposes a data-informed residual reinforcement learning (DR-RL)-based robotic tracking control scheme applicable to robots with high dimensionality....

Model predictive control (MPC) is one of the few control frameworks allowing to systematically integrate input and/or state constraints to realize safe control. Nonetheless, traditional MPC requires a full model of the system dynamics and model mismatch degrades performance. In this article, a data-driven MPC scheme is developed for robot manipulat...

Mobile robots are desired with resilience to safely interact with prior-unknown environments and finally accomplish given tasks. This paper utilizes instantaneous local sensory data to stimulate the safe feedback motion planning (SFMP) strategy with adaptability to diverse prior-unknown environments without building a global map. This is achieved b...

The presentation at the 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023, in Bilbao, Spain, on Sept 28, 2023

The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the driving styles of its nearby AVs can reliably evaluate the risk of collisions and make more reasonable driving decisions. However, there has not been a consistent defin...

The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the driving styles of its nearby AVs can reliably evaluate the risk of collisions and make more reasonable driving decisions. However, there has not been a consistent defin...

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of...

A novel procedure for the online identification of a class of discrete-time switched linear systems, which simultaneously estimates the parameters and switching manifolds of the systems, is proposed in this paper. Firstly, to estimate the parameters of the subsystems, a discrete-time concurrent learning-based recursive parameter estimator is design...

Stochastic Model Predictive Control (SMPC) has attracted increasing attention for autonomous driving in recent years, since it enables collision-free maneuvers and trajectory planning and can deal with uncertainties in a non-conservative way. Many promising strategies have been proposed on how to use SMPC to select appropriate maneuvers and plan sa...

Autonomous systems are desired to safely accomplish predetermined tasks with guaranteed performance despite uncertainties. This paper proposes a safe planning and performance-guaranteed control (SP-PGC) scheme to accomplish safe execution of autonomous systems suffering from uncertainties and disturbances. This is realized by investigating mutual i...

In this note, we develop an adaptive observer for a class of nonlinear systems with switched unknown parameters to estimate the states and parameters simultaneously. The main challenge lies in how to eliminate the disturbance effect of zero-input responses caused by the switching on the parameter estimation. These responses depend on the unknown st...

Maneuver planning, which plays a key role in selecting desired lanes and speeds, is an essential element of autonomous driving. Generally, for a vehicle driving on a multilane road, there are several potential maneuvers in both longitudinal and lateral directions. Selecting the best maneuver from the various options represents a significant challen...

An adaptive incremental sliding mode control (AISMC) scheme for a robot manipulator is presented in this paper. Firstly, an incremental backstepping (IBS) controller is designed using time-delay estimation (TDE) to reduce dependence on the mathematical model. After substituting IBS controller into the nonlinear system, a linear system w.r.t. tracki...

In this note, we develop an adaptive observer for a class of nonlinear systems with switched unknown parameters to estimate the states and parameters simultaneously. The main challenge lies in how to eliminate the disturbance effect of zero-input responses caused by the switching on the parameter estimation. These responses depend on the unknown st...

This article presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), utilizes measured input-state data to allow the design of the approximate optimal incremental control strategy, sta...

This article proposes an off-policy risk-sensitive reinforcement learning (RL)-based control framework to jointly optimize the task performance and constraint satisfaction in a disturbed environment. The risk-aware value function, constructed using the pseudo control and risk-sensitive input and state penalty terms, is introduced to convert the ori...

In this paper, we propose a novel indirect model reference adaptive control approach for uncertain piecewise affine systems. This approach exploits a barrier Lyapunov function to construct novel adaptation laws and average dwell time constraints for switching. Compared to the previous research, where closed-loop stability and asymptotic tracking ca...

In this article, we investigate the adaptive output tracking control for multiinput-multioutput piecewise affine systems with prescribed performance. Both direct and indirect adaptation approaches are studied. Given a desired trajectory, both control approaches ensure the output tracking error to be confined within a performance bound, which prescr...

This paper proposes a new formulation for the optimal tracking control problem (OTCP) of Euler-Lagrange systems. This formulation extends the incremental adaptive dynamic programming (IADP) technique, a reinforcement learning based method for solving the robust optimal regulation control problem (RORCP), to learn the approximate solution to the OTC...

Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing safe reinforcement learning on highly nonlinear and high-dimensional dynamical systems, one possible approach is to find a low-dimensional safe region via data-driven featur...

For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for...

Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexi...

Piecewise affine (PWA) systems are attractive models that can represent various hybrid systems with local affine subsystems and polyhedral regions due to their universal approximation properties. The identification problem of PWA systems amounts to estimating the number of subsystems, parameters of each subsystem, and the corresponding polyhedral p...

This article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete‐valued variable (switching signal). Specifically, we consider the continuous‐time switched nonlinear systems in the state‐space form in our article. The...

This paper presents a new formulation for provable safety under partial model uncertainty with guaranteed performance. A collision-free control strategy is developed for an uncertain multi-agent system that navigates through a prior unknown environment populated with static and dynamic obstacles. Our novel instantaneous local control barrier functi...

Aiming at tracking control with bounded torque inputs of the flexible-joint robot manipulators, we propose a generalized saturated adaptive controller based on backstepping control, singular perturbation decoupling and neural networks. First, by using the singular perturbation theory, the full-order rigid-flexible dynamics of the robot manipulator...

This article investigates the tracking problem of an uncertain n-link robot manipulator with guaranteed safety and performance. To tackle parametric uncertainties, the torque filtering-augmented concurrent learning (CL) method is introduced for online identification of the unknown system without requirements of joints acceleration. By using CL, the...

In this article, we propose an optimal control scheme for information epidemics with stochastic uncertainties aiming at maximizing information diffusion and minimizing the control consumption. The information epidemic dynamics is represented by a network Susceptible-Infected-Susceptible (SIS) model contaminated by both process and observation noise...

The potential of large elastic deformations in control applications, e.g., robotic manipulation, is not yet fully exploited, especially in dynamic contexts. Mainly because essential geometrically exact continuum models are necessary to express these arbitrarily large deformation dynamics, they typically result in a set of nonlinear, coupled, partia...

For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and is used to provide safety estimates for learning al...

In this paper, we investigate the distributed link removal strategy for networked meta-population epidemics. In particular, a deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. In order to curb the spread of epidemics, we present the spectrum-based optimization problem involvi...

In this paper an online single artificial neural network adaptive critic algorithm is proposed to address the constrained robust control problem of a nonlinear system subject to full state constraints, input saturation and mismatched disturbance. Firstly, the constrained robust control problem of the original system is transformed into an equivalen...

In social systems, the evolution of interpersonal appraisals and individual opinions are not independent processes but intertwine with each other. Despite extensive studies on both opinion dynamics and appraisal dynamics separately, no previous work has ever combined these two processes together. In this paper, we propose a novel and intuitive mode...

Although the state-of-the-art learning approaches exhibit impressive results for dynamical systems, only a few applications on real physical systems have been presented. One major impediment is that the intermediate policy during the training procedure may result in behaviors that are not only harmful to the system itself but also to the environmen...

In social systems, the evolution of interpersonal appraisals and individual opinions are not independent processes but intertwine with each other. Despite extensive studies on both opinion dynamics and appraisal dynamics separately, no previous work has ever combined these two processes together. In this paper, we propose a novel and intuitive mode...

Piecewise affine (PWA) models are attractive frameworks that can represent various hybrid systems with local affine submodels and polyhedral regions due to their universal approximation properties. The PWA identification problem amounts to estimating both the submodel parameters and the polyhedral partitions from data. In this paper, we propose a n...

Networked epidemic models have been widely adopted to describe propagation phenomena. The endemic equilibrium of these models is of great significance in the field of viral marketing, innovation dissemination, and information diffusion. However, its stability conditions have not been fully explored. In this paper we study the stability of the endem...

In this paper, we investigate the optimal control problems of heterogeneous node-based information epidemics. A node-based Susceptible-Infected-Recovered-Susceptible (SIRS) model is introduced to describe the information diffusion processes taking into account heterogeneities in both network structures and individual characters. Aiming at guiding i...

In this paper, we propose a concurrent learning-based indirect model reference adaptive control approach for multivariable piecewise affine systems as an enhancement of our previous work. The main advantage of the concurrent learning-based approach is that the linear independence condition of the recorded data suffices for the convergence of the es...

Networked epidemic models have been widely adopted to describe propagation phenomena. The endemic equilibrium of these models is of great significance in the field of viral marketing, innovation dissemination, and information diffusion. However, its stability conditions have not been fully explored. In this paper, we study the stability of the ende...

This paper is concerned with fully distributed consensus control of linear multiagent systems with undirected graphs. Two kinds of reduced-order adaptive output-feedback protocols are proposed. For the edge-based protocol, each edge is adapted by a scalar that is determined by the output information of the associated two agents; for the node-based...

This paper addresses three control implementation issues for trajectory tracking of robotic manipulators: unmodeled dynamics, unknown input saturation and peaking effects during the transient phase. A model-free first-order robust-adaptive control method is used to deal with the unmodeled dynamics. Robust optimality and stability of the controller...

In this paper the robust optimal control of deterministic information epidemics is inspected taking into consideration the noisy transition rates. Distinct from conventional works, the heterogeneous susceptible–infected–susceptible (SIS) model is adopted where both the heterogeneities in the network topology and the individual diversity are conside...

In underactuated systems, a transition between two periodic orbits is generally characterized by slow convergence. This is due to the fact that the unactuated degree of freedom (DoF) hinders the state of the system to enter the domain of attraction of the target orbit close to the fixed point of the Poincaré Map. In this paper, we introduce an opti...

This paper proposes a model-free robust-adaptive controller for Euler-Lagrange systems with a quantitative performance analysis in terms of state-errors. The controller has only few parameters, and the procedure of finding the controller parameters is intuitive and easy to implement. The controller acts as an adaptive computed-torque controller and...

When manipulating an object with multiple effectors such as in multidigit grasping or multiagent collaboration, forces and torques (i.e., wrench) applied to the object at different contact points generally do not fully contribute to the resultant object wrench, but partly compensate each other. The current literature, however, lacks a physically pl...

A novel approach to stability analysis of linear time-invariant (LTI) time-delay systems of retarded type with incommensurate time delays and polynomial dependence on parametric uncertainties is presented. Using a branch and bound algorithm, which relies on Taylor Models and polynomials in Bernstein form, we first determine the stability-crossing s...

In Kapitel 17 werden die dafür notwendigen Voraussetzungen vorgestellt. Zentraler Aspekt ist dabei die beständige Anregung des Systems, welche jedoch in der Praxis aufgrund negativer Effekte wie z.B. erhöhtem Verschleiß häufig unerwünscht ist. Mit dem kürzlich etablierten Konzept namens Concurrent Learning lässt sich die beständige Anregung durch e...

The planning and execution of real-world robotic tasks largely depends on the ability to generate feasible motions online in response to changing environment conditions or goals. A spline deformation method is able to modify a given trajectory so that it matches the new boundary conditions, e.g., on positions, velocities, accelerations, etc. At the...

Piecewise affine systems constitute a popular framework for the approximation of non-linear systems and the modelling of hybrid systems. This paper addresses the recursive subsystem estimation in continuous-time piecewise affine systems. Parameter identifiers are extended from continuous-time state-space models to piecewise linear and piecewise aff...

Distributing nominal models in multiple-models applications constitutes a long standing problem. The set of models needs to be distributed in such a way that their corresponding controllers can stabilize all possible system configurations in a large uncertainty set. This paper presents a systematic solution by phrasing the distribution as coverage...

The presentation slides for the conference paper Protective Control for Robot Manipulator by Sliding Mode Based Disturbance Reconstruction Approach at the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Munich, Germany, 3-7 July 2017.

This paper presents a protective control framework for robot manipulators using sliding mode based state estimation and disturbance reconstruction. Specifically, the nonlinear dynamic state-space model of the robot is transformed into a descriptor form, allowing the design of a sliding mode observer and a sliding mode based trajectory tracking cont...

Cooperative dynamic manipulation enlarges the manipulation repertoire of human–robot teams. By means of synchronized swinging motion, a human and a robot can continuously inject energy into a bulky and flexible object in order to place it onto an elevated location and outside the partners’ workspace. Here, we design leader and follower controllers...

The efficiency of human-robot collaborative task completion benefits from seamless interactions. Robots that act in a shared environment with humans, can improve the initiation of such interactions by externalizing the intention of accompanying motions. Especially within robot locomotion the ability to convey intentions non-verbally is beneficial f...

This article proposes direct and indirect model reference adaptive control strategies for multivariable piecewise affine systems, which constitute a popular tool to model hybrid systems and approximate nonlinear systems. A chosen reference model, which can be linear or also piecewise affine, describes the desired closed-loop system behavior that is...

Balancing motions are usually designed using simplified models of the Center of Mass (CoM) and feedback control without accounting for energy efficiency. In order to tackle this shortcoming, we introduce a Motion Primitive switching methodology where samples of optimal motions (Motion Primitives) are chosen online based on a Euclidean distance metr...

Cooperative dynamic object manipulation increases the manipulation repertoire of multiagent teams. As a first step toward cooperative dynamic object manipulation, we present an energy-based controller for cooperative swinging of two-agent pendulum-like objects. Projection of the complex underactuated mechanism onto an abstract cart-pendulum allows...

Being aware of mutual influences between individuals is a major requirement a robot to efficiently operate in human populated environments. This is especially true for the navigation among humans with its mutual avoidance maneuvers. While humans easily manage this task, robotic systems are still facing problems. Most of the recent approaches concen...

This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guarant...

The objective of this paper is to develop a datadriven model of laser intensities and investigate its usage for Simultaneous Localization and Mapping (SLAM) in the field of robotics. In contrast to the standard usage of laser scanners in SLAM to generate geometric models of the environment, the research work on the applications of laser intensities...

Spatial reasoning and semantic environment understanding is a fundamental ability of robots navigating in unstructured dynamic environments. Since spatial and semantic reasoning is tightly linked to the sensor perception information, it is desirable that it is directly integrated into the environment model. This paper presents the interplay of a no...

Die allgemeine Problemstellung der statischen Optimierung lautet: Minimiere $$f(\mathbf{x}),\quad{\boldsymbol{\mathrm{x}}}\in\mathbb{R}^{n}$$ unter Berücksichtigung von (u. B. v.) $$\mathbf{c(x)}=\boldsymbol{\mathrm{0}},\quad{\boldsymbol{\mathrm{c}}}\in\mathbb{R}^{m}$$ (2.1) und $$\mathbf{h(x)}\leq\boldsymbol{\mathrm{0}},\quad{\boldsymbol{\mathrm{h...

In diesem Kapitel wird das unrestringierte Problem aus Kap. 4, $$\min_{{\boldsymbol{\mathrm{x}}}\in\mathbb{R}^{n}}f({\boldsymbol{\mathrm{x}}})\> ,$$ (5.1) in zwei Schritten zur allgemeinen Problemstellung aus Kap. 2 erweitert. Erst wird dazu ein durch Gleichungsnebenbedingungen (GNB) definierter zulässiger Bereich berücksichtigt $$\min_{{\boldsymbo...

Wir werden in diesem Kapitel einige Problemstellungen ansprechen, deren Bedeutung für entsprechende Anwendungen zwar kontinuierlich wächst, deren detaillierte Darlegung aber außerhalb des Rahmens dieses Buches fällt.

Wir werden in diesem Kapitel ein besonderes Problem der stochastischen optimalen Regelung mit unvollständiger Information behandeln, das für praktische Anwendungen aufgrund seiner relativ einfachen, selbst bei hochdimensionalen Aufgabenstellungen in Echtzeit ausführbaren Lösung eine große Bedeutung erlangt hat. Es handelt sich um die Minimierung de...

Das Grundprinzip der Methode der kleinsten Quadrate wurde zu Beginn des 19. Jahrhunderts von C. F. Gauß im Zusammenhang mit der Berechnung von Planetenbahnen formuliert. Es handelt sich um einen Spezialfall der im letzten Kapitel behandelten Problemstellung, der wegen seiner großen praktischen Bedeutung in diesem Kapitel getrennt behandelt werden s...

Vektoren \({\boldsymbol{\mathrm{x}}}\in\mathbb{R}^{n}\) und Matrizen \(\mathbf{A}\in\mathbb{R}^{n\times m}\) $${\boldsymbol{\mathrm{x}}}=\left[\begin{array}[]{c}x_{1}\\ \vdots\\ x_{n}\end{array}\right],\quad\mathbf{A}=\left[\begin{array}[]{ccc}a_{11}&\cdots&a_{1m}\\ \vdots&\ddots&\vdots\\ a_{n1}&\cdots&a_{nm}\end{array}\right]$$ (19.1) werden durch...

Die bisherige Behandlung dynamischer Optimierungsaufgaben basierte in erster Linie auf der klassischen Variationsrechnung und den bahnbrechenden Arbeiten von L.S. Pontryagin und seinen Mitarbeitern. Parallel dazu entwickelte aber R.E. Bellman eine alternative Vorgehensweise, die sich auf dem von ihm im Jahr 1952 formulierten Optimalitätsprinzip stü...

Wir haben uns bereits im zweiten Teil dieses Buches mit Problemstellungen der optimalen Regelung befasst, allerdings haben wir bisher angenommen, dass die auf den Prozess wirkenden Störungen klein sind und keine bekannte Stochastik aufweisen. Bei vielen praktischen Anwendungen ist aber die Problemumgebung innerhalb bestimmter Grenzen ungewiss. In s...

Ein wichtiger Spezialfall der in Kap. 5 behandelten Problemstellung entsteht, wenn alle beteiligten Funktionen \(f({\boldsymbol{\mathrm{x}}})\), \({\boldsymbol{\mathrm{c}}}({\boldsymbol{\mathrm{x}}})\), \(\mathbf{h}({\boldsymbol{\mathrm{x}}})\) linear sind. Dieser Spezialfall, der unter dem Namen lineare Programmierung (LP) bekannt ist, stellt die...

Wir haben uns bis jetzt in den Kap. 10 und 11 mit der optimalen Steuerung zeitkontinuierlicher dynamischer Systeme befasst. Die numerische Auswertung dieser Problemstellungen sowie die praktische Implementierung der resultierenden optimalen Steuertrajektorien bzw. Regelgesetze erfordern aber in der Regel den Einsatz elektronischer Rechner, die – be...

In diesem Kapitel wird die Problemstellung der optimalen Steuerung dynamischer Systeme von Kap. 10 um die Berücksichtigung von Ungleichungsnebenbedingungen erweitert, die für praktische Anwendungen äußerst wichtig sind. Außerdem werden in Abschn. 11.3.1 Erweiterungen der Problemstellung der optimalen Steuerung durch die Berücksichtigung weiterer Ar...

Wir haben zu verschiedenen Anlässen festgestellt, so z. B. in den Abschn. 12.6 und 16.6, dass die Schätzung des Systemzustandes unter Nutzung verfügbarer (unvollständiger) Messinformation eine Voraussetzung für den Einsatz effektiver Regelungs- und Steuerstrategien sein kann. Darüber hinaus ist die Problemstellung der Zustandsschätzung für Zwecke d...

Die in diesem Kapitel behandelte Problemstellung lautet $$\min_{x\in{X}}f(x),\quad\text{wobei}\quad X\subset\mathbb{R}\> .$$ (3.1) Bei der Bestimmung des zulässigen Bereichs X dürfen hier keine Gleichungsnebenbedingungen berücksichtigt werden. Der zulässige Bereich X kann durch \(\mathbf{h}(x)\leq\mathbf{0}\) oder aber durch \(X=[a_{1},b_{1}]\cup[a...

Das in diesem Kapitel behandelte Problem lautet $$\min_{{\boldsymbol{\mathrm{x}}}\in\mathbb{R}^{n}}f({\boldsymbol{\mathrm{x}}})\;.$$ (4.1) Da die Optimierungsvariablen \(x_{1},\ldots,x_{n}\) der kürzeren Schreibweise wegen in einem n-dimensionalen Vektor \({\boldsymbol{\mathrm{x}}}\) zusammengefasst werden, spielen Vektor- und Matrixoperationen bei...

Bei den bisher betrachteten Problemstellungen waren die Entscheidungsvariablen \({\boldsymbol{\mathrm{x}}}\) Werte aus dem euklidischen Raum \(\mathbb{R}^{n}\) oder aus einem Unterraum \(X\subset\mathbb{R}^{n}\). Bei der dynamischen Optimierung werden Funktionen \({\boldsymbol{\mathrm{x}}}(t)\) einer unabhängigen Variable t, d. h. Elemente des allg...

In diesem Kapitel wollen wir uns einem wichtigen Spezialfall des Abschn. 9.4.1, nämlich dem Problem der optimalen Steuerung dynamischer Systeme zuwenden. Einen Sonderfall von Nebenbedingungen im Sinne von (9.50) stellt die Berücksichtigung der Zustandsgleichung eines dynamischen Systems dar (s. Abschn. 19.2) $$\boldsymbol{\mathrm{\dot{x}}}(t)=\math...

Die in den Kap. 10, 11 und 13 abgeleiteten notwendigen Optimalitätsbedingungen erlauben die analytische Lösung bestimmter Klassen von dynamischen Optimierungsproblemen. So hat man beispielsweise in Kap. 12 für Probleme mit LQ-Struktur und beliebigen Dimensionen optimale Steuer- und Regelgesetze ableiten können. Ebenso ist uns in Kap. 11 sowie bei e...

Dieses Kapitel behandelt einen wichtigen Spezialfall der optimalen Steuerung dynamischer Systeme. Es handelt sich um Probleme mit linearen Zustandsgleichungen und quadratischen Gütefunktionalen, die selbst bei hochdimensionalen Systemen die Ableitung optimaler Regelgesetze zulassen. Die Bedeutung der Linearen-Quadratischen (LQ-)Optimierung für die...

Usually the identification of piecewise affine systems consists of two steps. First, subsystem parameters are identified before the state space partition is reconstructed. For the later step, it is common to label the states of a recorded trajectory and separate differently labeled states with the help of linear support vector machines. In this pap...

The main contribution of this paper is a novel method for assessing the safety of trajectories by means of their collision probability in dynamic and uncertain environments. The future trajectories of the robot are represented as directed graphs and the uncertain states of the obstacles are represented by probability distributions. Instead of evalu...

An adaptive energy-based swing-up controller for simple pendulums is presented. A state transformation from cartesian to polar phase space followed by approximation steps leads to the fundamental dynamics of the controlled simple pendulum. Based on the fundamental dynamics, the unknown natural frequency is estimated and a control gain is adjusted s...

This paper presents an extension of the standard occupancy grid for 3D environment mapping. The presented approach adds a fusion process after the occupancy update which modifies the resolution of the grid cells in an incremental manner. Consequently, the proposed approach requires fewer grid cells for 3D representation in comparison to a standard...

Zusammenfassung Das Projekt Interactive Urban Robot (IURO) erforscht Mensch-Roboter-Interaktion und autonome Robotik in innerstädtischer Umgebung. In dem beleuchteten Szenario ist der Roboter auf Routenanweisungen von Passanten angewiesen, die er in natürlichsprachlichem Dialog und multimodaler Interaktion erwirbt. Besonderes Augenmerk liegt auf de...

This contribution addresses the problem of com-puting the probability that a linear system with uncertain inputs reaches an unsafe region in the state space. The probability of reaching unsafe states is bounded by an upper limit, by intersecting enclosing hulls of the probability density function for time intervals with the specified unsafe regions...

A balance control approach for legged robots is presented that is based on Zero Moment Point manipulation. Therefore the invariance control method is used, where an output of the system is kept in an admissible region by switching between two controllers, the nominal controller and the corrective controller. The nominal controller is designed to ac...

In this paper a novel control method for geometric invariance control is proposed. Nonlinear SISO systems with unstable internal dynamics can be stabilized. In variance conditions of a given state space region are discussed. Sufficient conditions for an output stabilizing Lyapunov controller to assure invariance ofthat region are derived. Simulatio...

In this paper, we enhance a recently proposed method for adaptive identification of piecewise affine systems by the use of concurrent learning. It is shown that the concurrent use of recorded and instantaneous data leads to exponential convergence of all subsystem parameters under verifiable conditions on the recorded data. A key advantage of the p...