G. Gruener

Abslracl-This paper relates first experiences using a stateof-the.art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the Sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor featum B frame rate of up 1 30 fames per second. Ils capabilities in terms of basic obstacle avoidance and local path-planning am eralualed and compared to the performance of a shndard laser sEmner. I.

This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.

This work presents a method for probabilistic plane fitting and an application to robotic 3D mapping. The plane is fitted in an orthogonal least-square sense and the output complies with the conventions of the symmetries and perturbation model (SPmodel). In the second part of the paper, the presented plane fitting method is used within a 3D mapping application. It is shown that by using probabilistic information, high precision 3D maps can be generated.

Atomic force microscopy (AFM) investigations of living cells provide new information in both biology and medicine. However, slow cell dynamics and the need for statistically significant sample sizes mean that data collection can be an extremely lengthy process. We address this problem by parallelizing AFM experiments using a two-dimensional cantilever array, instead of a single cantilever. We have developed an instrument able to operate a two-dimensional cantilever array, to perform topographical and mechanical investigations in both air and liquid. Deflection readout for all cantilevers of the probe array is performed in parallel and online by interferometry. Probe arrays were microfabricated in silicon nitride. Proof-of-concept has been demonstrated by analyzing the topography of hard surfaces and fixed cells in parallel, and by performing parallel force spectroscopy on living cells. These results open new research opportunities in cell biology by measuring the adhesion and elastic properties of a large number of cells. Both properties are essential parameters for research in metastatic cancer development.

It is difficult to apply traditional reinforcement learning algorithms to robots, due to problems with large and continuous domains, partial observability, and limited numbers of learning experiences. This paper deals with these problems by combining: (1) reinforcement learning with memory, implemented using an LSTM recurrent neural network whose inputs are discrete events extracted from raw inputs; (2) online exploration and offline policy learning. An experiment with a real robot demonstrates the methodology's feasibility.