David Oertel

Tactile sensor on a magnetic basis using novel 3D Hall sensor - First prototypes and results

In this paper, a new operating principle for a tactile sensor for medical diagnostics is proposed based on the measurement of three-dimensional magnetic fields. A permanent magnet and the newly developed 3D-Hall sensor AS54xx of the Fraunhofer Institute for Integrated Circuits (IIS) in Erlangen, Germany, are embedded in one silicone pad. An external force changes the position of the magnet in relation to the AS54xx, thus changing the measured magnetic field in three dimensions. In contrast to conventional tactile sensors, one sensor cell provides three dimensional information about external forces, thus making it potentially possible to detect tumors by palpation with only this one sensor cell. Three prototypes of the tactile sensor with different silicones and permanent magnets have been fabricated, and the feasibility of the operating principle has been proven for axial forces with laboratory experiments. Hysteresis effects of the tactile sensor have turned out to be negligible.

Failure recovery for modular robot movements without reassembling modules

Modular robots consist of many mechatronic modules that can be connected into various shapes and therefore adapted for a given task or environment. Motion of the robots can be achieved by locomotion generators that control joints connecting the modules. An important advantage of modular robots is their ability to recover from failures by ejecting and replacing damaged modules. This type of failure recovery may be precluded due to inability of the broken modules to cooperate or when no spare modules are available. In such a case, locomotion of a damaged robot should be adapted to allow the robot to reach a repair station or even to finish its task without the need to exchange the broken modules. In this paper, we investigate how to recover from failures using the concept of motion planning with motion primitives and how to adapt the primitives to new situations. The proposed systems allows modular robots to move even if some modules fail. Besides modular robots, the proposed system is suitable also for other robots that can be driven by locomotion generators such as legged or snake-like robots.

Online motion planning for failure recovery of modular robotic systems

Modular robots are built of many basic robotic modules that can be connected into robots of various shapes. These robots are able to recover from a failure by ejecting and replacing damaged modules. Although this type of failure recovery is usually suggested in literature, it may be precluded due inability of the broken modules to cooperate or if no spare modules are available. In such a case, locomotion of a damaged robot should be adapted to allow the robot to reach a repair station or even to finish its task without the need to exchange the broken modules. In this paper, we investigate how to adapt motions of modular robots with respect to failures using the concept of motion planning with motion primitives. The ability of the proposed system to recover from failures is verified in a simulation and also in a HW experiment.

Knowledge-based direction prediction to optimize the null-space parameter of a redundant robot in a telemanipulation scenario

This paper shows an approach of a knowledge-based path-guidance for minimally invasive robotic surgery. For an accurate path-guidance it is important to know or estimate the direction of the tool tips motion. Therefor it is necessary to predict the future direction. In this work, the predicted direction is based on clustering applied to typical trajectory sets, combined with building first and second order Markov models which represent cluster transitions. A coarse prediction is obtained by cluster transitions. For improvement, this is refined by projecting the cluster points distribution on a (unit) sphere surrounding the current tool tip position. For the latter step, a discrete procedure is proposed that takes into account the most likely consecutive cluster(s). The resulting predicted direction can be used to guide the robots movement, especially by controlling the joint angles of a redundant robot in order to avoid joint limits. The main focus of this work is on the actual prediction of direction which is evaluated using a synthetic test scenario.

Self-localization by eavesdropping in acoustic underwater sensor networks

Localization is a common problem in underwater sensor networks. Since global navigation satellite systems do not work underwater, geo-referencing of underwater sensors requires other technologies. In this paper, we present an novel localization approach for nodes in an acoustic underwater sensor network. By combining pressure sensors with the functionality of modern acoustic USBL modems, the nodes are able to self-localize their position inside the network. This can be done in a passive manner, just by listening to the transmitted messages of other network nodes, thereby saving energy and sparing the communication channel from additional traffic. In order to evaluate the performance of the method, experiments have been conducted in simulation and under real conditions in the Middle Atlantic Ocean.

Reducing elevation angle errors of long-range deep-sea acoustic localization by ray tracing and depth measurements

For deep-sea (robotic) applications, accurate acoustic localization is required, e.g. using USBL devices. Internal calculations for these devices usually assume a constant sound velocity within the deployment area ignoring refraction effects due to a depth-dependent sound velocity profile. This work presents the results of long-range deep-sea (5000m) USBL localization tests to estimate absolute positioning accuracy. The experiments lead to a correction approach by incorporating both the depth of acoustic participants as well as the full sound velocity profile. For bigger horizontal distances (> 800m), this approach can largely reduce the resulting (systematic) elevation angle error.