Sensor guided, automated systems require the composition of various sensors and data processing algorithms to obtain relevant information for performing their task. Many applications have additional requirements such as a certain accuracy, which has to be achieved despite sensor noise and calibration errors. In this paper we model the configuration of perception systems as a planning problem over probabilistic graphical models. We work on a subset of the full configuration space of perceptions systems, specifically the used sensors, data processing algorithms and view poses. Based on a semantic description of the goal, available sensors and data processing algorithms, our system plans perception steps and sensor data fusion autonomously. The planner operates by constructing a factor graph until the accuracy requirements of tasks are fulfilled or unobtainable with the available action set. We validate our approach in an industrial assembly scenario.

In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substantial challenge due to high-dimensional state spaces and non-linear contact-dynamics. Furthermore, object poses and model parameters, such as friction, are not exactly known and a source of uncertainty. The method proposed in this paper models the task of parts assembly as a belief space planning problem over an underlying impedance-controlled, compliant system. To solve this planning problem we introduce an asymptotically optimal belief space planner by extending an optimal, randomized, kinodynamic motion planner to nondeterministic domains. Under an expansiveness assumption we establish probabilistic completeness and asymptotic optimality. We validate our approach in thorough, simulated and realworld experiments of multiple assembly tasks. The experiments demonstrate our planner's ability to reliably assemble objects, solely based on CAD models as input.

When robots perform manipulation tasks, they need to determine their own movement, as well as how to grasp and release an object. Reasoning about the motion of the robot and the object simultaneously leads to a multi-modal planning problem in a high-dimensional configuration space. In this paper we propose an asymptotically optimal manipulation planner. Our approach extends optimal sampling-based roadmap planners to efficiently explore the configuration space of the robot and the object. We prove probabilistic completeness and global, asymptotic optimality. Extensive simulations of a typical pick-and-place scenario show that our approach significantly outperforms a (nonoptimal) state-of-the-art approach. We implemented our planner on a real manipulator and were able to compute high quality solutions in less than a second.

Most of today's robots, while being equipped with arrays of ever more impressive sensors, still lack a fundamental ability so readily available to biological systems: a sense of touch. With the exception of a few humanoid platforms tactile sensing is typically limited to small areas such as palms or finger tips. In this paper we present a tactile sensing system designed specifically to meet the needs of covering large, possibly curved surfaces of the robot. The sensors are at the same time highly sensitive and robust, cheap to manufacture and can be made to have almost any size and shape. As such the presented system can ideally complement existing systems and their high spatial resolution required in finger tips with a system providing highly sensitive and impact resistant coverage for the rest of the body.

One of the main challenges for service robots during operation lies in the handling of unavoidable uncertainties which originate from model and sensor inaccuracies and which are characteristic for realistic application scenarios. Robustness under real world conditions can only be achieved when the dominant uncertainties are explicitly represented and purposefully managed by the robot's control system. We therefore adopt a probabilistic approach in which perception is regarded as a sequential estimation process and follow a Bayesian filtering methodology. Under these assumptions probabilistic models of the robot's perception systems are key. In this paper we shortly describe a model based object recognition and localization system. However, we do not not focus on the 6D pose estimation procedure itself, but on the method to quantify and compute the uncertainty associated with it. We construct a Gaussian approximation of the resulting pose error using the implicit function theorem. It is then used as a proposal density for importance sampling. Our goal is to sample from the measurement model describing 6D object localization based on local features in a Bayesian filtering context.

This paper describes Atlas, a hybrid metrical/topological approach to SLAM that achieves efficient mapping of large-scale environments. The representation is a graph of coordinate frames, with each vertex in the graph representing a local frame, and each edge representing the transformation between adjacent frames. In each frame, we build a map that captures the local environment and the current robot pose along with the uncertainties of each. Each map's uncertainties are modeled with respect to its own frame. Probabilities of entities with respect to arbitrary frames are generated by following a path formed by the edges between adjacent frames, computed via Dijkstra's shortest path algorithm. Loop closing is achieved via an efficient map matching algorithm. We demonstrate the technique running in real-time in a large indoor structured environment (2.2 km path length) with multiple nested loops using laser or ultrasonic ranging sensors.

In the field of service robotics, robots serve and assist human beings. It is natural for humans to directly interact with the robot via tactile interfaces. This paper introduces several kinds of tactile interactions between a user and the robot as well as interactions of the robot with the environment. All interactions are implemented in a single paradigm: Forces measured from tactile sensors result in motion vectors at the contact points. The motion vectors from different sensors are superimposed and then determine the robot's joint velocities. We present results from our experimental setup consisting of an 8 degrees of freedom manipulator arm mounted on a mobile platform. In the illustrated example, a human interacts with the robot using only the tactile interface.

Siemens has developed a prototype of an autonomous navigation system for mobile service robots. Its suitability for tough everyday operation has been successfully demonstrated since August 1996 with cleaning machines in chain store supermarkets of Albert Heijn BV in the Netherlands. Here, HEFTER CLEANTECH (HCT) cleaning robots are being used in cooperation with the cleaning specialist RTB.

This paper presents a novel local obstacle avoidance module. The main idea of the steer angle field approach is to discard all steer angles that, given the nonholonomic kinematics of the robot, would lead to collisions within a certain hit distance. The approach has been implemented and tested over a period of one year on our mobile robot. Results have shown that the robot is able to operate robustly in unknown, unprepared and cramped in-door environments for hours.>

Autonomous low-cost mobility of robots has been a challenging problem for quite a long time. In this paper the typical problems of sonar based autonomous navigation and the authors' solutions to overcome them are discussed. The authors have created, robust modules for sonar data fusion and safe steering which significantly improve known methods. There are furthermore robot control behaviors which offer specific competence to solve high-level tasks. These behaviors build a hierarchical architecture but have the ability to assess their respective task and autonomously suggest a different, more competent behavior to continue. Thus, the behaviors work together as distributed competencies. The concept described here is being evaluated in a system on the authors' experimental robot ROAMER. The available modules already operate robustly in cluttered office environments.>