Parallel-continuum robots combine the advantages of both parallel and continuum robotics. They offer a compromise between the inherent compliance and slenderness of continuum robots and the high precision and strength of rigid-link parallel robots. Throughout recent years there has been an increasing research interest in these novel architectures, which form closed kinematic chains that feature flexible, continuous links undergoing elastic deformations. As the number of publications in this emerging research field is steadily increasing, this survey article summarizes and reviews the state of the art in parallel-continuum robots, discussing their design and modeling. A definition and notation for parallel-continuum robots is introduced, allowing for a clear classification. In conclusion, current open research questions and possible applications for such robots are discussed.

This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups.

This paper presents a holistic approach for door opening with a cartesian impedance controlled mobile robot, a KUICA KMR iiwa. Based on a given map of the environment, the robot autonomously detects the door handle, opens doors and traverses doorways without knowledge of a door model or the door's geometry. The door handle detection uses a convolutional neural network (CNN)-based architecture to obtain the handle's bounding box in a RGB image that works robustly for various handle shapes and colors. We achieve a detection rate of 100% for an evaluation set of 38 different door handles, by always selecting for highest confidence score. Registered depth data segmentation defines the door plane to construct a handle coordinate frame. We introduce a control structure based on the task frame formalism that uses the handle frame for reference in an outer loop for the manipulator's impedance controller. It runs in soft real-time on an external computer with approximately 20 Hz since access to inner controller loops is not available for the KMR iiwa. With the approach proposed in this paper, the robot successfully opened and traversed for 22 out of 25 trials at five different doors.