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.

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 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.

In order to support a human in a human-machine system, the cooperating automation requires information about the goal pursued by the human. We model human-machine systems as a Partially Observable Markov Decision Process to develop an assistant system operating on maneuver or navigation level featuring an automatic detection of the humans goal which is initially unknown to it. Predicting the next actions of the human allows for computing corresponding assistant actions by employing Partially Observable Monte-Carlo Planning With Observation Widening. These supporting actions are generated based on continuous action-, observation- and state spaces and are executed synchronously to the actions of the human. New information about the humans goal is gathered through observations to further enhance future supporting actions. With a progressing certainty of the goal recognition, the assistant system is increasingly able to assist the human by completing the task both cooperatively or even fully autonomously, thus reducing workload, while always allowing for the human to smoothly in-or decrease their involvement. The assistant system demonstrates its ability to recognize the goal pursued by the human as well as to execute appropriate supporting actions while being robust to the human changing their goal in multiple experiments investigating the interaction of a real human with a simulated cooperative positioning scenario.