On Realizing Multi-Robot Command through Extending the Knowledge Driven Teleoperation Approach

Future crewed planetary missions will strongly depend on the support of crew-assistance robots for setup and inspection of critical assets, such as return vehicles, before and after crew arrival. To efficiently accomplish a high variety of tasks, we envision the use of a heterogeneous team of robots to be commanded on various levels of autonomy. This work presents an intuitive and versatile command concept for such robot teams using a multi-modal Robot Command Terminal (RCT) on board a crewed vessel. We employ an object-centered prior knowledge management that stores the information on how to deal with objects around the robot. This includes knowledge on detecting, reasoning on, and interacting with the objects. The latter is organized in the form of Action Templates (ATs), which allow for hybrid planning of a task, i.e. reasoning on the symbolic and the geometric level to verify the feasibility and find a suitable parameterization of the involved actions. Furthermore, by also treating the robots as objects, robot-specific skillsets can easily be integrated by embedding the skills in ATs. A Multi-Robot World State Representation (MRWSR) is used to instantiate actual objects and their properties. The decentralized synchronization of the MRWSR of multiple robots supports task execution when communication between all participants cannot be guaranteed. To account for robot-specific perception properties, information is stored independently for each robot, and shared among all participants. This enables continuous robot- and command-specific decision on which information to use to accomplish a task. A Mission Control instance allows to tune the available command possibilities to account for specific users, robots, or scenarios. The operator uses an RCT to command robots based on the object-based knowledge representation, whereas the MRWSR serves as a robot-agnostic interface to the planetary assets. The selection of a robot to be commanded serves as top-level filter for the available commands. A second filter layer is applied by selecting an object instance. These filters reduce the multitude of available commands to an amount that is meaningful and handleable for the operator. Robot-specific direct teleoperation skills are accessible via their respective AT, and can be mapped dynamically to available input devices. Using AT-specific parameters provided by the robot for each input device allows a robot-agnostic usage, as well as different control modes e.g. velocity, model-mediated, or domain-based passivity control based on the current communication characteristics. The concept will be evaluated on board the ISS within the Surface Avatar experiments

Semantic State Interpretation For Error Detection

Long-term robot autonomy depends on the ability to detect anomalies and flaws since, along with subsequent diagnosis and repair, it enables the robot to reach the necessary levels of robustness and persistency. In fact, if a problem is not quickly identified and fixed, it could cause the robot to damage itself or, in the worst case scenario, cause injury to those nearby. In this thesis, we propose a framework for detecting anomalous behaviors in autonomous robot

Granulosa Cell Tumor of the Ovary: A Retrospective Study of 31 Cases and a Review of the Literature

Background. Adult granulosa cell tumors (AGCTs) are the most common sex cord-stromal tumors. Unlike epithelial ovarian tumors, they occur in young women and are usually detected at an early stage. The aim of this study was to report the clinical and pathological characteristics of AGCT patients and to identify the prognostic factors. Methods. All cases of AGCTs, treated at Salah Azaïz Institute between 1995 and 2010, were retrospectively included. Kaplan-Meier’s statistical method was used to assess the relapse-free survival and the overall survival. Results. The final cohort included 31 patients with AGCT. The mean age was 53 years (35–73 years). Patients mainly presented with abdominal mass and/or pain (61%, n=19). Mean tumor size was 20 cm. The majority of patients had a stage I disease (61%,  n=19). Two among 3 patients with stage IV disease had liver metastasis. Mitotic index was low in 45% of cases (n=14). Surgical treatment was optimal in almost all cases (90%, n=28). The median follow-up time was 14 years (1–184 months). Ten patients relapsed (32%) with a median RFS of 8.4 years (6.8–9.9 years). Mean overall survival was 13 years (11–15 years). Stage I disease and low-to-intermediate mitotic index were associated with a better prognosis in univariate analysis (resp., p=0.05 and p=0.02) but were not independent prognostic factors. Conclusion. GCTs have a long natural history with common late relapses. Hence, long active follow-up is recommended. In Tunisian patients, hepatic metastases were more frequent than occidental series. The prognosis remains good and initial staging at diagnosis is an important prognostic factor

Toward Multi User Knowledge Driven Teleoperation of a Robotic Team with Scalable Autonomy

This paper proposes a knowledge-driven teleoperation framework that enables multiple operators to command a team of robots to execute complex tasks in an efficient and intuitive manner. The framework leverages a shared knowledge base that captures domain-specific information and procedural knowledge about the task at hand. This knowledge base is used by a hybrid planner to generate context-specifically relevant commands for supervised autonomy robot command as well as direct teleoperation modes. By filtering the available commands, the operators are guided in their decision-making towards efficient task completion. This paper further extends our knowledge driven approach to address the switching between multiple operators and robotic assets, with the aim to be able scale up human-robot team for space exploration. Overall, this work represents a step towards more intelligent and collaborative teleoperation systems. The described system will be used in the Surface Avatar ISS-to-ground experiments slated for July 2023