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Remote Sensing And Image Interpretation

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Wireless Sensor Networks and Multi-UAV systems for natural disaster management

Effective simulation and testing environment is a vital part in the research of self-driving vehicles. It is capable of testing self-driving software and hardware quickly in a variety of virtual environments at low cost. However, as for the current mainstream simulation platforms, a considerable gap exists between the constructed virtual environment and the actual self-driving platform, which decreases the efficiency of development and makes it difficult to complete the migration from the virtual scenario to the real environment. Therefore, in this paper, we proposed a novel hardware-in-the-loop (HiL) simulation system. It takes the real hardware electronic control unit (ECU) of the self-driving vehicle as a part of the simulation platform, which improves the efficiency of development and testing, and also, the verified algorithms can be implemented into self-driving cars faster than ever before. The proposed HiL simulation system mainly consists of four parts: the vehicle kinematic model simulation, the multi-sensor simulation, the environment simulation, and the ECU hardware. Simulation experiments on applying the HiL system are used to verify the validity of self-driving algorithms in virtual scenes, including perception, planning, decision making, and control. Furthermore, algorithms that are tested in the simulation environment can be rapidly deployed into the real self-driving vehicles. In this paper, we also presented the verification processes of various algorithms, such as planning and control. These algorithms are implemented in the HiL system, and the experimental results show the validity of our proposed platform.

A Novel Integrated Simulation and Testing Platform for Self-Driving Cars With Hardware in the Loop

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

Efficient path planning is one of the main prerequisites for robust navigation of autonomous robots. Especially driving in complex environments containing both streets and unstructured regions is a challenging problem. In this paper we present the application of the Hybrid A* algorithm to a nonholonomic mobile outdoor robot in order to plan near optimal paths in mostly unknown and potentially intricate environments. The implemented algorithm is capable of generating paths with a rate of at least 10 Hz to guarantee real-time behavior.

Application of Hybrid A* to an Autonomous Mobile Robot for Path Planning in Unstructured Outdoor Environments

Contamination with radioactive or chemical substances can have serious consequences for humans and the environment. For this reason, thorough decontamination is of great importance in the decommissioning of nuclear power plants and the remediation of contaminated sites. People who have to carry out such tasks are working under physically demanding conditions and may be exposed to high health risks. To get humans out of those hazardous environments, the ROBDEKON project was launched with the aim of establishing a national competence center for “Robotic Systems for Decontamination in Hazardous Environments”. In ROBDEKON, four research institutions work together with partners from industry to develop practical robot systems for decontamination tasks. The research activities include the development of mobile robots for rough terrain, autonomous construction machines, and robot manipulators as well as decontamination concepts, planning algorithms, multi-sensory 3D mapping of the environment and teleoperation using virtual reality. The focus is on three applications: the decommissioning of nuclear facilities, the decontamination of plant components and handling of waste, and the remediation of landfills and contaminated sites. This paper gives a high-level overview of the goal and the ongoing research activities in ROBDEKON, provides an insight into the infrastructure of the involved laboratories, and presents first project results.

ROBDEKON: Robotic Systems for Decontamination in Hazardous Environments

Safe and efficient path planning for mobile robots in dynamic environments is still a challenging research topic. Most approaches use separate algorithms for global path planning and local obstacle avoidance. Furthermore, planning a path for a sequence of goals is mostly done by planning to each next goal individually. These two strategies generally result in sub-optimal navigation strategies. In this paper, we present an algorithm which addresses these problems in a single combined approach. For this purpose, we model the static and dynamic risk of the environment in a consistent way and propose a novel graph structure based on a state × time × goal lattice with hybrid dimensionality. It allows the joint planning for multiple goals while incorporating collision risk due to dynamic and static obstacles. It computes hybrid solutions which are part trajectory and part path. Finally, we provide some results of our algorithm in action to prove its high quality solutions and real-time capability.

Safe mobile robot motion planning for waypoint sequences in a dynamic environment

Mobile Robot Motion Planning in Multi-Resolution Lattices with Hybrid Dimensionality

If a natural disaster like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams have to get a quick overview of the situation in order to identify possible locations of victims, which need to be rescued, and dangerous locations, which 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 the situation responsive networking of 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 objectives and first research results of the ongoing joint research project SENEKA.

Janko Petereit

Papers

Application of Hybrid A* to an Autonomous Mobile Robot for Path Planning in Unstructured Outdoor Environments

ROBDEKON: Robotic Systems for Decontamination in Hazardous Environments

Safe mobile robot motion planning for waypoint sequences in a dynamic environment

Mobile Robot Motion Planning in Multi-Resolution Lattices with Hybrid Dimensionality

Situation Responsive Networking of Mobile Robots for Disaster Management