This paper presents a multi-sensor based generic approach to opening doors for a dexterous robot. Once the handle has been located by a computer vision algorithm and properly grasped, we are able to open doors without using a model or other prior knowledge of the door geometry. This is done by combining the sensor information of both a force-torque sensor in the robot wrist and a tactile sensor matrix in the robot gripper itself. Our experimental results show that the combination of both sensors achieves the most successful way to open the door.

This paper presents a tactile language for controlling a robot through its artificial skin. This language greatly improves the multimodal human-robot communication by adding both redundant and inherently new ways of robot control through the tactile mode. We defined an interface for arbitrary tactile sensors, implemented a symbol recognition for multi-finger contacts, and integrated that together with a freely available character recognition software into an easy-to-extend system for tactile language processing that can also incorporate and process data from non-tactile interfaces. The recognized tactile symbols allow for both a direct control of the robot's tool center point as well as abstract commands like "stop" or "grasp object x with grasp type y". In addition to this versatility, the symbols are also extremely expressive since multiple parameters like direction, distance, and speed can be decoded from a single human finger stroke. Furthermore, our efficient symbol recognition implementation achieves real-time performance while being platform-independent. We have successfully used both a multi-touch finger pad and our artificial robot skin as tactile interfaces. We evaluated our tactile language system by measuring its symbol and angle recognition performance, and the results are promising.

Intuitive human-robot cooperation presents a challenge to robots since it demands of them a high level of understanding of the human user. Our approach is to estimate the human intention and select and proactively execute an appropriate robot task without requiring an explicit user command. This paper describes the concept and details of our proactive execution module and how it forms an integral part of our intuitive human-robot cooperation system. We present implementation details and the results of an evaluation scenario.

Location based services with Galileo/GPS satellite receivers integrated in mobile phones are expected to experience a substantial market growth. Satellite signals are usually received on the earth surface with very low signal energy. Attenuation, shadowing, and multipath fading in urban canyons and moderate indoor environments impose challenging acquisition conditions for the state-of-the-art enhanced sensitivity mobile satellite receivers. Residual frequency deviations between the down-conversion frequency and the received signal frequency due to unknown Doppler frequency shifts significantly reduce the signal acquisition sensitivity of current satellite receivers. This paper shows how the residual frequency deviation can be reduced incrementally towards zero much faster than the CDMA code synchronization be detected with sufficient probability. This paper furthermore derives the probability density functions for the signal detection using the adaptive frequency correction method and illustrates the acquisition sensitivity gain of around 4 dB for different Galileo and GPS satellite receiver configurations.

Current enhanced sensitivity GPS receivers developed for moderate indoor reception increase the observation period with noncoherent integration of the envelopes of coherently integrated predetection samples. The paper analyzes a novel method, differential correlation, where each current coherently integrated predetection sample is multiplied with the complex conjugate of the previous predetection sample. The products are then accumulated. The deterministic signal and stochastic noise components, together with their probability density functions and moments, are derived algebraically for differential correlation and conventional noncoherent integration, while also considering signal and implementation nonidealities. The paper shows that differential correlation can offer an average sensitivity gain over conventional noncoherent integration of around 1.5 dB.