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

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.

Situation Responsive Networking of Mobile Robots for Disaster Management

For human-robot interaction in industrial applications, monitoring the robot surroundings is essential in order to guarantee the safety of humans sharing the workspace with robots. This contribution presents an approach for obstacle detection and tracking based on the fusion of multiple heterogeneous depth sensors which are mounted on board a mobile manipulator. The proposed methods can track arbitrary obstacles all around the robot. Furthermore, a novel method for extrinsic calibration of the sensors is proposed, using the manipulator as a sensor calibration target. This approach enables a robust and accurate calibration without the need for a dedicated calibration object. Experimental results validate the real-time performance of the proposed obstacle tracking method.

Multi-Sensor Obstacle Tracking for Safe Human-Robot Interaction

Disclosed is an optimizing method for regulating the operating state of a guided machine tool having a tool rotating at a certain speed and subjected to a percussion frequency during a boring action, in which the tool, subjected in addition to a force, is driven into an object of a given material. The method includes, determination of the parameters characterizing the actual operating state of the machine tool; conduction of an adaptive process analysis based on the determined parameters in order to obtain parameter-specific, relevant characteristics; evaluation of the parameter-specific, relevant characteristics within the framework of a logical decision-making process in order to obtain information characterizing the actual operating state; selection of known reference data of the utilized tool describing the operating state and of the to-be-machined material according to a given quality criterion; determination of an optimum operating point for the machine tool determinable by the rotational speed and percussion frequency by comparison of the information characterizing the actual operating state with the selected reference data; and regulation of the machine tool regarding the rotational speed and percussion frequency based on the determined optimum operating point.

Christian Frey

Papers

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

Situation Responsive Networking of Mobile Robots for Disaster Management

Multi-Sensor Obstacle Tracking for Safe Human-Robot Interaction

Optimising method for regulating the operating state of a guided machine tool comprising a rotating percussion tool during a boring process

A 3D Representation of Obstacles in the Robots Reachable Area Considering Occlusions