Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.

SENEKA - sensor network with mobile robots for disaster management

Mobile robot motion planning in unstructured dynamic environments is a challenging task. Thus, often suboptimal methods are employed which perform global path planning and local obstacle avoidance separately. This work introduces a holistic planning algorithm which is based on the concept of state × time lattices with variable dimensionality and multiple resolutions. The adaptive planning method makes it possible to rapidly plan local maneuvers while still pursuing global optimality. Umfang: XXV, 241 S. Preis: €47.00 | £43.00 | $83.00

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Adaptive State × Time Lattices: A Contribution to Mobile Robot Motion Planning in Unstructured Dynamic Environments

Localization is an important primitive that is utilized in a number of important applications such as location-based mobile services, augmented reality, and autonomous mobile robotics. While the GPS technology is considered the de facto standard for outdoor localization, it is known to suffer from significant accuracy limitation in urban areas. In this work, we present a particle filter–based data fusion technique for localization in urban areas. The proposed localization technique provides more accurate location estimation results due to its ability to efficiently fuse together information collected from diverse sensor technologies. The novelty of our proposed approach stems from its ability to fuse data from diverse sources, namely, phase shift fingerprints collected from Low Power AM Radio (LPAM) towers and inertial measurement sensors. Our simulation results indicate that the proposed approach can achieve an accuracy of 0.5 m using a limited number of LPAM towers as low as 5. Also, the proposed approach requires the collection of a low number of LPAM phase shift fingerprints. Our simulations indicate that 30 fingerprints are enough to provide 0.5 m accuracy in a 100 × 100 m2 deployment.

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

Automatisiertes Fahren: Computer greifen zum Steuer

In this article the development and implementation of a Navigation System (NS) for autonomous mobile robotic applications is presented. The NS is based on a Global Positioning System (GPS) aided Inertial Navigation System (INS) using an Extended Kalman Filter (EKF) sensor fusion algorithm. The development stage of the NS was carried out in simulation using real sensor data from tests. The system was implemented as a Robotic Operating System (ROS) node. Field test were performed on a Pioneer P3-AT mobile robot. The NS presented benefits from the heterogeneity, integration and hardware abstraction advantages of the ROS. The experimental results of the presented NS show a very good performance that will allow the migration to other platforms such as Autonomous Surface and Underwater Vehicles (ASV and AUV) in which the INTELYMEC-CIFICEN-UNCPBA group is working.

GPS aided strapdown inertial navigation system for autonomous robotics applications

An essential key capability for a mobile robot to perform autonomous navigation is the ability to localize itself in its environment. The most basic way to perform localization is dead-reckoning, i. e., to use relative measuring sensors of the robot like odometry (wheel encoders) by incrementally incorporating the measured revolutions of the robots wheels from a known starting position. As these sensors only deliver relative measurements and all sensors are subjected to noise, the uncertainty of the pose grows boundlessly over the covered distance. In outdoor environments navigation sensors like GPS and compass are a viable option. They are measuring absolute quantities and therefore are not suffering from error accumulation but are prone to local disturbances by surrounding objects. The measurements of the compass are degraded by disturbances of the terrestrial magnetic field, e. g., by metal fences or ventilation fans of air condition systems. Using a low-cost differential GPS receiver, the significant remaining source of error is multipath propagation due to reflections and shadowing effects of large objects like buildings. As the reflections are dependent on the constellation of the receiver and the satellites relative to nearby reflecting surfaces the errors are time variant and locally varying. For precise self localization the combination of several sensors is essential as due to the noisy measurements no single sensor is sufficient. The data from the sensors is fused to a combined estimate resulting in a more accurate localization. A new Kalman filter based approach will be presented to perform multi-sensor fusion for on-line localization under realtime constraints. While for indoor applications of mobile robots a 2D localization usually is sufficient, as the robot typically operates on flat floors, a full 6 DoF estimation of position and attitude is necessary in outdoor environments where the assumption of a flat ground cannot be applied. To accomplish the 6 DoF estimation relative measuring sensors and absolute measuring sensors are combined by means of multi-sensor fusion. The fusion combines the advantages of the relative measuring sensors regarding their local precision with the capability of absolute sensors to confine the global uncertainty and thus preventing unbounded error growth.

Arda Saltoglu

Papers

Multi-Sensor Fusion for Localization of a Mobile Robot in Outdoor Environments