Unmanned Ground Robots for Rescue Tasks

This chapter describes two unmanned ground vehicles that can help search and rescue teams in their difficult, but life-saving tasks. These robotic assets have been developed within the framework of the European project ICARUS. The large unmanned ground vehicle is intended to be a mobile base station. It is equipped with a powerful manipulator arm and can be used for debris removal, shoring operations, and remote structural operations (cutting, welding, hammering, etc.) on very rough terrain. The smaller unmanned ground vehicle is also equipped with an array of sensors, enabling it to search for victims inside semi-destroyed buildings. Working together with each other and the human search and rescue workers, these robotic assets form a powerful team, increasing the effectiveness of search and rescue operations, as proven by operational validation tests in collaboration with end users

Unmanned Ground and Aerial Robots Supporting Mine Action Activities

During the Humanitarian‐demining actions, teleoperation of sensors or multi‐sensor heads can enhance-detection process by allowing more precise scanning, which is useful for the optimization of the signal processing algorithms. This chapter summarizes the technologies and experiences developed during 16 years through national and/or European‐funded projects, illustrated by some contributions of our own laboratory, located at the Royal Military Academy of Brussels, focusing on the detection of unexploded devices and the implementation of mobile robotics systems on minefields

Interoperability in a Heterogeneous Team of Search and Rescue Robots

Search and rescue missions are complex operations. A disaster scenario is generally unstructured, time‐varying and unpredictable. This poses several challenges for the successful deployment of unmanned technology. The variety of operational scenarios and tasks lead to the need for multiple robots of different types, domains and sizes. A priori planning of the optimal set of assets to be deployed and the definition of their mission objectives are generally not feasible as information only becomes available during mission. The ICARUS project responds to this challenge by developing a heterogeneous team composed by different and complementary robots, dynamically cooperating as an interoperable team. This chapter describes our approach to multi‐robot interoperability, understood as the ability of multiple robots to operate together, in synergy, enabling multiple teams to share data, intelligence and resources, which is the ultimate objective of ICARUS project. It also includes the analysis of the relevant standardization initiatives in multi‐robot multi‐domain systems, our implementation of an interoperability framework and several examples of multi‐robot cooperation of the ICARUS robots in realistic search and rescue missions

In-flight launch of unmanned aerial vehicles

This paper considers the development of a system to enable the in-flight-launch of one aerial system by another. The paper will discuss how an optimal release mechanism was developed, taking into account the aerodynamics of one specific mother and child UAV. Furthermore, it will discuss the PID-based control concept that was introduced in order to autonomously stabilize the child UAV after being released from the mothership UAV. Finally, the paper will show how the concept of a mothership UAV + child UAV combination could be usefully taken into advantage in the context of a search and rescue operation

3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

ICAT - Data mining techniques and SAS as a tool for graphical presentation of principal components analysis and disjoint cluster analysis results

Complexity of data analysis in data mining often makes results difficult to interpret. This problem could be solved using various approaches. Principal Component Analysis (PCA) and Disjoint Cluster Analysis (DCA) are methods used for data reduction and summarization. In this paper, PCA and DCA were applied on dataset example containing information about students' courses and time necessary to pass related exams. The SAS software was used as a data mining tool for performing this analysis. Another approach for better interpretation is visualization of results. This means showing important attributes visually to aid informal users to interpret results

Non-Technical Survey Tool Description: TIRAMISU deliverable D.220.2

During the Non-Technical Survey, information on a Suspected Hazardous Area (SHA) is collected and analysed for assessment and reduction/ inclusion purposes. This phase focuses on a scale that is more local than the Advanced General Survey, but unlike the Technical Survey, the Non-Technical Survey does not involve entering the SHA physically. This deliverable includes a description of the advancement in the development of the tools, a first outline of guidelines for using them and a first version of the framework for evaluating their performance.TIRAMISU - D220.2info:eu-repo/semantics/nonPublishe