Despite advances regarding autonomous functionality for robots, teleoperation remains a means for performing delicate tasks in safety critical contexts like explosive ordnance disposal (EOD) and ambiguous environments. Immersive stereoscopic displays have been proposed and developed in this regard, but bring about their own specific problems, e.g., simulator sickness. This work builds upon standardized test environments to yield reproducible comparisons between different robotic platforms. The focus was placed on testing three optronic systems of differing degrees of immersion: (1) A laptop display showing multiple monoscopic camera views, (2) an off-the-shelf virtual reality headset coupled with a pantilt-based stereoscopic camera, and (3) a so-called Telepresence Unit, providing fast pan, tilt, yaw rotation, stereoscopic view, and spatial audio. Stereoscopic systems yielded significant faster task completion only for the maneuvering task. As expected, they also induced Simulator Sickness among other results. However, the amount of Simulator Sickness varied between both stereoscopic systems. Collected data suggests that a higher degree of immersion combined with careful system design can reduce the to-be-expected increase of Simulator Sickness compared to the monoscopic camera baseline while making the interface subjectively more effective for certain tasks.

Mobile robots with reconfigurable chassis are able to traverse unstructured outdoor environments with boulders or rubble, and overcome challenging structures in urban environments, like stairs or steps. Autonomously traversing rough terrain and such obstacles while ensuring the safety of the robot is a challenging task in mobile robotics. In this paper we introduce a two-phase motion planning algorithm for actively reconfigurable tracked robots. We first use the completeness of a graph search on a regular grid to quickly find an initial path in a low dimensional space, considering only the platform's operating limits instead of the complete state. We then take this initial path to focus the RRT* search in the continuous high-dimensional state space including the actuators of the robot. We do not rely on a detailed structure/terrain classification or use any predefined motion sequences. Hence, our planner can be applied to urban structures, like stairs, as well as rough unstructured environments. Simulation results prove our method to be effective in solving planning queries in such environments.

In this paper we present a method for navigating a multi-robot system through an environment while additionally maintaining a predefined set of constraints. Possible constraints are the requirement to keep up the direct line-of-sight between robots or to ensure that robots stay within a certain distance. Our approach is based on graph structures that model movements and constraints separately, in order to cover different robots and a large class of possible constraints. Additionally, the partition of movement and constraint graph allows us to use known graph algorithms like Steiner trees to solve the problem of finding a target configuration for the robots. We construct so called separated distance graphs from the Steiner tree and the underlying roadmap graph, which allow assembling valid navigation plans fast.

In this paper we present an approach of how to design a robot for using tactile sensors using an evolutionary algorithms. Designing a whisker-equipped robot requires that two design objectives have to be addressed simultaneously. The robot controller must be capable of using the whisker information, and the configuration of the whiskers have to be aligned to the controller: A: Find a "good whisker configuration for a given robot controller. B: Find a "good robot controller for a given whisker configuration. The optimisation using a simulated robot with six whiskers and a simple reactive controller clearly demonstrates that the chosen evolutionary algorithm yields a drastic increase of the robot performance.