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

A method, apparatus, and computer program product for a drilling operation. In one advantageous embodiment, the drilling operation may comprise a motor, a rotary sensor, and a controller connected to the rotary sensor. The rotary sensor may be capable of generating a signal in response to rotary movement of the drill motor. The controller may be capable of monitoring a speed of the air motor from the signal generated by the rotary sensor and may be capable of identifying a change from a current layer in a stackup to a new layer in the stackup using the signal.

Adaptive positive feed drilling system

https://repositorio.inesctec.pt/bitstream/123456789/3974/1/P-00M-QZS.pdf

Survey on advances on terrain based navigation for autonomous underwater vehicles

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.

Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring.

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

Navigating an autonomous underwater vehicle (AUV) is a difficult task. Dead-reckoning navigation is subject to unbounded error due to sensor inaccuracies and is inapplicable for mission durations longer than a few minutes. To limit the estimation errors a global referencing method has to be used. SLAM (Simultaneous Localization And Mapping) is such a method. It uses repeated recognition of significant features of the environment to reduce the estimation error. Devices for environment sensing that are used in most land applications like cameras, laser scanners or GNSS signals cannot be used under water: GNSS signals are attenuated very strongly in water and light propagation suffers mainly from turbid water. In more than a few hundred meters water depth there is also no sunlight. Sonic waves suffer much less from these problems and therefore sonar sensors are the prevalent sensor type used under water. A main difficulty is to extract three-dimensional information from side-scan sonar images to perform SLAM as this is an ill-posed inverse problem. An overview of existing approaches to underwater SLAM using sonar data is given in this paper. A short outlook to the system that will be used in the TIETeK project is also presented.

Deep-sea AUV navigation using side-scan sonar images and SLAM

An efficient operation of complex industrial processes requires the continuous diagnosis of the asset functionality. The early detection of potential failures and malfunctions, the identification and localization of present or impending component failures and, in particular, the monitoring of the underlying physical process behaviour is of crucial importance for a cost-effective operation of complex industry assets. With respect to these suppositions a monitoring concept based on machine learning methods has been developed, which allows an integrated and continuous diagnosis of the physical process behavior and phases. The present paper outlines briefly the architecture of the developed distributed diagnostic concept and presents in detail the developed approach for the identification of intrinsic process-phases and the monitoring functionality of the unknown process behaviour based on self-organizing-maps and watershed transformations.

Monitoring of complex industrial processes based on self-organizing maps and watershed transformations

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.

Christian Frey

Papers

SENEKA - sensor network with mobile robots for disaster management

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

Deep-sea AUV navigation using side-scan sonar images and SLAM

Monitoring of complex industrial processes based on self-organizing maps and watershed transformations

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