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.

While robotic arms have been used in a vast range of application areas, so far no extensive reports on the utilization as human-machine interface exist. Compared to HMI devices from literature, the robotic arm used in this work (KUKA LBR iiwa 14 R820) features a relatively large workspace and is able to generate force and torque feedback that surpasses the capabilities of literature devices. We describe the setup allowing to use the robotic arm as HMI and analytically determine the optimal initial pose of it based on the manipulability measure of Yoshikawa. To demonstrate that the robotic arm is able to serve as HMI, we report on a comparative study with a state of the art haptic HMI featuring 20 participants. Additionally, two applications from the context of planetary exploration are presented: The first considers the teleoperation of the pan-tilt unit of a lightweight rover unit and illustrates how the large workspace of the HMI benefits the precision of the teleoperation compared to a setup with a smaller workspace. The second experiment showcases the use of the force feedback of the HMI to enable a cooperation between the operator and a supporting path-following automation in a shared control of a simulated ground robot. Both the study and the applications highlight the performance, precision and reliability of our proposed system.

With human-machine interaction continuously developing, the consideration of human experience in the design of a machine becomes increasingly relevant. An experience measure that rates how well a work or interaction state is perceived by a user is psychological flow. In this paper we introduce a novel approach to assess flow in human-machine interaction by game-theoretically modeling the interaction. To validate our approach, we perform a user study with 30 participant with a study design that allows for the manipulation of the user's experience through three experience modes. The results both validate our study design and provide indications that our approach to assess flow is promising to be developed further.

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.

The experiment reported in this article provides a first experimental evaluation of human–machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision-making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader–follower approaches by comparing them to newly proposed automation designs based on cooperative decision-making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human–machine cooperation. The experiment's results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: It may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human–machine cooperation.

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.

This paper addresses the problem of transfer of control authority between a robot’s AI and a remote human operator, when controlling a Mixed-Initiative (MI) robotic system. We propose a negotiation-theoretic method that enables the robot’s AI and the human operator to cooperatively and dynamically determine (i. e. negotiate) the transfer of control authority between these two agents. An experimental study is presented in which a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) method is compared with our proposed Negotiation-Enabled Mixed-Initiative Control Switcher (NEMICS) algorithm. Results suggest that the NEMICS framework is able to successfully avoid conflicts for control, which is a fundamental challenge encountered with previous MI control methods. Comparing NEMICS with the EMICS, we provide evidence of improved navigational safety (i. e. fewer collisions). Additionally, our usability study suggests that human operators perceived their interactions with NEMICS as less intrusive than with EMICS.

This paper presents the validation and the comparative study of a shared control concept for a large vehicle manipulator (LVM). The state-of-the-art controlling a LVM is manual control: The operator controls the manipulator to carry out a specific task and keeps the vehicle on the road. Easing the work for the operator, an automatic lane-keeping of the vehicle can be taken into account: An automation of the vehicle which keeps it on its reference, but without taking into consideration of the manipulator’s specific task. However, the operator has his specific task with the manipulator, and therefore, such automation may not be satisfying. Therefore, this paper presents the validation and compares the Limited Information Shared Controller (LISC) proposed previously with the manual control mode. This step is crucial, showing the concept’s applicability and benefits compared to the state-of-the-art solution. Thus, the LISC is compared with a non-cooperative controller (NCC) and the manual mode on a real-time simulator with test subjects. It has a more realistic experimental setup than in other studies because there is no predefined manipulator reference. The study results indicate that the NCC can lead to undesired motions of the overall system because the test subjects cannot carry out their specific task. On the other hand, the proposed the LISC of the vehicle can reduce the working load while supporting the operator in carrying out the manipulator’s specific task.

In the past decade, remarkable research has been done on human-machine cooperation to generate synergies and mutual benefits. However, most research so far only considers the control level of interaction with concepts like haptic shared control. This paper focuses on the emerging research on human-machine cooperation on higher levels of interaction to tackle more complex challenges. Therefore, we first introduce a generalized level model based on established models to define our research emphasis on emancipated human-machine cooperation on all levels. Second, the design and results of a study on human-machine cooperation on decision level are presented. We examine the negotiation behavior of humans in a scenario with discrete decision options and a deadline. The results indicate the validity of a previously proposed model based on negotiation theory to describe the observed human behavior. Additionally, the observed influencing factors on the negotiation behavior are crucial for a proper automation design: adaptation and identification methods are required to enable the automation to take part in an emancipated negotiation with a human.

In this paper a new negotiation model is introduced for the design of Advanced Driver Assistance Systems (ADAS). The objective is to establish an emancipated ADAS capable of cooperative decision making. This can be seen as a step from Shared Control to Cooperative Control. Due to the descriptive nature of negotiation theory of cooperative human decision processes, a high human user acceptance is expected if the ADAS is designed accordingly. However, conventional negotiation theory requires adaptation towards the ADAS context. The first extension enables the necessary consideration of a dynamical environment. The second extension is a new asynchronous negotiation protocol, allowing a more realistic human-machine interaction model. Furthermore a new opponent model is introduced to identify human negotiation behavior. This enables the ADAS to adapt itself during the interaction with the human driver, leading to potentially faster negotiations. Our simulation results show that a successful negotiation is possible with the proposed model, motivating further investigations in real applications.

This paper contributes to research investigating if the concept of shared control is extendable to higher levels of human-machine cooperation. For this purpose, an experiment is presented analyzing the guidance level in cooperation among humans to transfer these findings in future assistance system design. Within the experiment, pairs of participants were facing a virtual obstacle avoidance course. They had to decide cooperatively on the maneuvers to avoid the obstacles. The only interaction possibility between team members was via haptically coupled steering wheels. The teams were classified based on the decisiveness of their members. Afterwards the time to reach a cooperative decision was measured. The results show a significant link between the distribution of decisiveness in a team and the time to reach a cooperative decision in ambiguous situations. Furthermore, the experiment reveals a high ability of humans to cooperate on the guidance level in general. This motivates human-imitating design of future assistance systems on guidance level in human-machine cooperation with the intention of high user satisfaction and trust in these systems.