Rapid programming of applications with down-times near zero is a key to automation of small lotsize production. This paper proposes an approach to program by gestures. It combines the visual observation and interpretation of human activities and gestures, mapping to technical tasks and autonomous execution. Automated planning/generation of the robot motion combined with automated correction of pose deviations (induced from the visual observation) allows an execution which is independent of the observation accuracy. The industrial application: bores and internal threads play a critical role as integral parts of connections, bearings, engines, hydraulic and pneumatic systems. The increased industrial requirement for high quality components demands 100% surface inspection of bores (here with diameters from 4 to 50 mm) at small lotsizes. Since state-of-the-art automation is limited to high volumes, the main target is to develop a semi-autonomous system for rapid and flexible inspection featuring new programming. The main focus is on rapid and intuitive programming of robotic systems. The paper describes the implementation and techniques in case of a bore inspection system

Bores and internal threads play a critical role as integral parts of connections, bearings and engines, hydraulic and pneumatic systems. Defective bore surfaces lead to increased friction and abrasion, leakage, and reduced stability. Therefore, industry demands ever higher quality of components' bore surfaces. Today industrial automation is limited to high part volumes. Current systems miss a high flexibility or are mainly driven by human interaction and control. Therefore the main target of this work is to develop an automatic system for rapid and flexible 100% surface inspection of bores with diameters from 4 to 50 mm. Further a method for the detection and localization of bores on arbitrary metallic objects has to be developed as well as the image processing for the inspection with a vision system has to be done. The main focus is on easy operating of the system achieved with rapid programming for the user. The paper describes the system, in overview and detail, and shows the promising results of the experiments and a public long-term demonstration (4 days) during an industrial fair.

Predictive displays have proven their suitability to compensate time delays in visual feedback which impair the performance of teleoperation applications. Most current implementations employ simple computer graphics for the prediction of the visual feedback given to the operator who is controlling the remote manipulation. Furthermore, they require a well-known remote environment. The aim of the presented work is to improve the efficacy of the predictive display by increasing the degree of immersion and by enhancing the system's flexibility. Potential applications include maintenance and repair of machines located in remote plant- or office-type environments, which are typically non-static and a priori only partially known. Communication media are high- and medium-bandwidth LANs and WANs inducing round trip delays varying from 0.2 to 2 seconds. Additionally, a sufficient bandwidth cannot be assured during the entire remote operation. The paper describes a predictive display system which achieves photo-realism based on autonomously explored and updated scene models. Model acquisition is performed with a binocular CCD camera system. Photo-realism is obtained by using computer graphics techniques, especially the mapping of original, real textures. Besides the concepts, an early stage of implementation and first experimental results in scene reconstruction and photo-realistic scene prediction are presented.

The presented approach integrates proven techniques and original approaches to one robust and fast 3D model-based recognition system. A speedy recognition is achieved by operating on a stream of filtered 3D sensor features, reconstructed for navigation tasks of the robot, instead of using a separate sensor data processing. Furthermore, simple, inexpensive recognition strategies are applied. Robustness is obtained by integrating complementary recognition strategies: four indexing techniques and two (2D and 3D) matching methods for verification, are completed by a hypothesis promotion, based on feedback of information to the sensor system. All strategies differ in their requirements, reliability, selectivity, and temporal constraints. Hypotheses are integrated using fusion, ruling out, and aging techniques. The approach is evaluated experimentally with varying calibration errors, scene complexity, and sensing conditions.

Robust and safe traversal of doors is an important task for autonomous mobile robots. This paper presents a robust and inexpensive approach for recognizing and localizing doors based on monocular grey-level images and minimalistic models. The traversal is achieved by servoing the robot with respect to the door-hypothesis. Robustness against pose errors, scene complexity, and sensing conditions is obtained by combining indexing on significant aggregated features with a multi-view approach that employs consistency over time: pose-hypotheses are filtered via the motion of the robot to reject incorrect ones. The traversal is implemented as a behavior and was evaluated in 150 experiments at different doors under different conditions.

This paper presents our approach to retrieve a dependable three-dimensional description of a partially known indoor environment. We describe the way the sensor data from a video camera is preprocessed by contour tracing to extract the boundary lines of the objects and how this information is transformed into a three-dimensional environmental model of the world. We introduce a dynamic map that operates in a closed loop with various sensor systems improving their performance by filtering and contributing certain knowledge. The filtering relies on the capability of a mobile robot to gather sensor readings from different positions. An important part of our approach is the interaction between the dynamic map, storing and filtering the incoming information, and a module predicting missing sensor features based on structures and reference objects. This interaction helps to generate a more accurate model containing also poor detectable features, that are impossible to extract from a single sensor view.

This paper describes an approach to exploit hypothetical landmarks, based on uncertain, suppositions about objects for feature detection, localization, exploration and object-recognition. Information gained from sensors is combined with additional information obtained from reasoning about the environment. Object recognition based on the generation of plausible completions of explored landmarks and verification by matching in 3D, fairly independent of the robot's behavior, is described. Generated hypothetical landmarks that are missing for suppositions about objects are directly inserted in a dynamic local map of the environment and verified in consecutive sensor-readings. The usage of confirmed and rejected landmarks for the verification of the object hypotheses and for the feature detection and exploration is discussed.