Christian Alexander Braun

Karlsruhe Institute of Technology

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Sören Hohmann

Karlsruhe Institute of Technology

Simon Rothfuß

Karlsruhe Institute of Technology

Rustam Stolkin

University of Birmingham

Aniketh Ramesh

University of Birmingham

Manolis Chiou

Queen Mary University of London

Philipp Karg

Karlsruhe Institute of Technology

Tianshu Ruan

University of Birmingham

Florian Köpf

Karlsruhe Institute of Technology

Michal Smíšek

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Robert Pöhlmann

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Cooperative Institutions

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Karlsruhe Institute of Technology

Technical University of Munich

University of Birmingham

Bristol Robotics Laboratory

Electro Optical Systems (Germany)

FZI Research Center for Information Technology

Technische Universität Berlin

École Polytechnique Fédérale de Lausanne

University of Freiburg

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Experimental Evaluation of Model Predictive Mixed-Initiative Variable Autonomy Systems Applied to Human-Robot Teams

Aniketh Ramesh Christian Alexander Braun Tianshu Ruan Simon Rothfuß Sören Hohmann

<p>This paper is accepted for the IEEE SMC 2023. Adjusting the level of autonomy in human-machine systems (e.g., human-robot systems) holds great potential for achieving high system performance while maintaining operator involvement. To support operators with the task of setting the proper level of autonomy, we present a novel approach to realise a Model Predictive Controller that determines the optimal LoA for each tessellation in the robot's path plan based on the estimated performance degradation due environmental adversities. We also report on an experimental evaluation of a mixed-initiative system where both the operator and the Model Predictive Controller are in charge of dynamically adjusting the level of autonomy cooperatively while performing a challenging navigational task with a mobile ground robot in a high-fidelity simulation. To this end, we conducted a user study with 15 participants comparing the performance and user experience of the model predictive system with a state-of-the-art system. The results show significant benefits of the model predictive system in terms of a reduction of conflicts for control and an improved user experience. Additionally, there are indications of benefits in terms of robot health and, consequently, performance for the model predictive system.</p>

Model Predictive Control

Work space

Operator (biology)

10.36227/techrxiv.24018825.v1

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Model Predictive Control of the Degree of Automation Optimizing Robot Health

Christian Alexander Braun Aniketh Ramesh Simon Rothfuß Manolis Chiou Rustam Stolkin

Adjusting operator support in human-machine systems is a promising way of combining operator involvement with high overall system performance. Adaptive automation aims to achieve this goal without burdening the operator with the task of selecting and setting the desired amount of support. In this work, two novel adaptive automations are presented. We use the performance measure of "robot health" to formulate the optimal control problem of maximizing the robot health of a human-robot system through the adaption of operator support to develop two model predictive controllers. The first one considers discrete levels of operator support, or levels of automation, the second one uses the continuous conception of the degree of automation. We report on a proof-of-concept simulation study evaluating the proposed model predictive controllers in a collaborative teleoperation of a mobile robot; the results demonstrate the ability of both model predictive controllers to successfully arbitrate control between the operator and the robot's controller to maximize robot health.

Operator (biology)

Model Predictive Control

Teleoperation

10.1109/saci58269.2023.10158596

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Citations (4)

Excitation for Adaptive Optimal Control of Nonlinear Systems in Differential Games

IEEE Transactions on Automatic Control (2022)

Philipp Karg Florian Köpf Christian Alexander Braun Sören Hohmann

This article focuses on the fulfillment of the persistent excitation (PE) condition for signals which result from transformations by means of polynomials. This is essential, e.g., for the convergence of adaptive dynamic programming algorithms due to commonly used polynomial function approximators. As theoretical statements are scarce regarding the nonlinear transformation of PE signals, we propose conditions on the system state such that its transformation by polynomials is PE. To validate our theoretical statements, we develop an exemplary excitation procedure based on our conditions using a feed-forward control approach and demonstrate the effectiveness of our method in a nonzero-sum differential game. In this setting, our approach outperforms commonly used probing noise in terms of convergence time and the degree of PE, shown by a numerical example.

Differential game

10.1109/tac.2022.3145651

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Citations (7)

Experimental Evaluation of Model Predictive Mixed-Initiative Variable Autonomy Systems Applied to Human-Robot Teams

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2023)

Aniketh Ramesh Christian Alexander Braun Tianshu Ruan Simon Rothfuß Sören Hohmann

Adjusting the level of autonomy in human-machine systems (e.g., human-robot systems) holds great potential for achieving high system performance while maintaining operator involvement. To support operators with the task of setting the proper level of autonomy, we present a novel approach to realise a Model Predictive Controller that determines the optimal LoA for each tessellation in the robot's path plan based on the estimated performance degradation due environmental adversities. We also report on an experimental evaluation of a mixed-initiative system where both the operator and the Model Predictive Controller are in charge of dynamically adjusting the level of autonomy cooperatively while performing a challenging navigational task with a mobile ground robot in a high-fidelity simulation. To this end, we conducted a user study with 15 participants comparing the performance and user experience of the model predictive system with a state-of-the-art system. The results show significant benefits of the model predictive system in terms of a reduction of conflicts for control and an improved user experience. Additionally, there are indications of benefits in terms of robot health and, consequently, performance for the model predictive system.

Model Predictive Control

Operator (biology)

Work space

10.1109/smc53992.2023.10394184

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Citations (2)

Excitation for Adaptive Optimal Control of Nonlinear Systems in Differential Games

arXiv (Cornell University) (2021)

Philipp Karg Florian Köpf Christian Alexander Braun Sören Hohmann

This work focuses on the fulfillment of the Persistent Excitation (PE) condition for signals which result from transformations by means of polynomials. This is essential e.g. for the convergence of Adaptive Dynamic Programming algorithms due to commonly used polynomial function approximators. As theoretical statements are scarce regarding the nonlinear transformation of PE signals, we propose conditions on the system state such that its transformation by polynomials is PE. To validate our theoretical statements, we develop an exemplary excitation procedure based on our conditions using a feedforward control approach and demonstrate the effectiveness of our method in a nonzero-sum differential game. In this setting, our approach outperforms commonly used probing noise in terms of convergence time and the degree of PE, shown by a numerical example.

Feed forward

10.48550/arxiv.2105.02260

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Citations (0)

A Continuous and Quantitative Metric for the Levels of Automation

IFAC-PapersOnLine (2019)

Christian Alexander Braun Michael Flad Sören Hohmann

The standard SAE J3016 provides a definition for levels of driving automation; however, shared control algorithms that have successfully been applied to vehicle automation, do not reasonably fit into the levels proposed by the standard. In this paper, the rich literature about levels of automation taxonomies is reviewed regarding the applicability to all automation concepts. Most definitions provide qualitative rankings of different levels of automation for specific applications. However, from an engineering perspective, a quantitative and generic approach would generate great benefit for the design, analysis and evaluation of human-machine systems. Thus, criteria for a desirable metric of levels of automation are discussed and a continuous and quantitative measure based on the options available to the human is proposed. The application to an advanced driver assistant system example is demonstrated and future ways to leverage the potential of the metric within and beyond automated driving are examined.

Leverage (statistics)

10.1016/j.ifacol.2019.12.081

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Citations (6)

Belief Space Control with Intention Recognition for Human-Robot Cooperation

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2022)

Christian Alexander Braun Rinat Prezdnyakov Simon Rothfus Sören Hohmann

The cooperation between humans and robots is of great importance e.g. in medical, industrial or service applications. Here, the task to be pursued by the robot often depends on the current goal of the human. In cases where a direct communication of the human's goal is impractical or even impossible, an estimation of the human's goal is necessary. This estimation as well as potential process or measurement noise introduces uncertainty that needs to be taken into consideration during the planning of the robot's actions. To this end, we propose an automation comprising an Unscented Kalman filter as state estimator, a model based intention recognition algorithm to estimate the human's goal and a model predictive belief space controller based on Belief i-LQG explicitly considering the estimation uncertainty. We report on a simulated scenario featuring a mobile robot platform cooperating with a human. It demonstrates the ability of the proposed automation to actively reduce uncertainty about the system states and the human's goal while successfully pursuing the overall cooperative task.

Human–robot interaction

10.1109/smc53654.2022.9945170

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Citations (0)

Model Predictive Degree of Automation Regulation for Mobile Robots Using Robot Vitals and Robot Health

IFAC-PapersOnLine (2023)

Christian Alexander Braun Aniketh Ramesh Simon Rothfuß Manolis Chiou Rustam Stolkin

Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.

Human–robot interaction

10.1016/j.ifacol.2023.10.1025

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Citations (4)

Heuristic reoptimization of time‐extended multi‐robot task allocation problems

Networks (2024)

Esther Bischoff Saskia Kohn Daniela Hahn Christian Alexander Braun Simon Rothfuß

Abstract Providing high quality solutions is crucial when solving NP‐hard time‐extended multi‐robot task allocation (MRTA) problems. Reoptimization, that is, the concept of making use of a known solution to an optimization problem instance when the solution to a similar problem instance is sought, is a promising and rather new research field in this application domain. However, so far no approximative time‐extended MRTA solution approaches exist for which guarantees on the resulting solution's quality can be given. We investigate the reoptimization problems of inserting as well as deleting a task to/from a time‐extended MRTA problem instance. For both problems, we can give performance guarantees in the form of an upper bound of 2 on the resulting approximation ratio for all heuristics fulfilling a mild assumption. We furthermore introduce specific solution heuristics and prove that smaller and tight upper bounds on the approximation ratio can be given for these heuristics if only temporal unconstrained tasks and homogeneous groups of robots are considered. A conclusory evaluation of the reoptimization heuristic demonstrates a near‐to‐optimal performance in application.

Heuristics

10.1002/net.22217

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Citations (1)

The ARCHES Moon-Analogue Demonstration Mission: Towards Teams of Autonomous Robots for Collaborative Scientific Sampling in Lunar Environments

Martin J. Schuster Bernhard Rebele Marcus Müller Sebastian G. Brunner Andreas Dömel

Teams of mobile robots will play a crucial role in future missions to explore the surface of the Moon. Setting up infrastructure and taking scientific samples are expensive tasks when operating in such distant, challenging, and unknown environments. In contrast to current single-robot space missions, future heterogeneous robotic teams will increase efficiency via enhanced autonomy and parallelization, improve robustness via functional redundancy, as well as benefit from complementary capabilities of the individual robots. We present our heterogeneous robotic team, consisting of flying and driving robots that we plan to deploy on scientific sampling demonstration missions at a Moon-analogue site on Mt. Etna, Sicily, Italy in 2021 as part of the ARCHES project. We give a brief description of the robots' complementary capabilities and present their roles in two mission scenarios.

Robotic spacecraft

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