Abstract
In the event of a large crisis (think about typhoon Haiyan or the Tohoku earthquake and tsunami in Japan), a primordial task of the rescue services is the search for human survivors on the incident site. This is a complex and dangerous task, which often leads to loss of lives among the human crisis managers themselves. The introduction of unmanned search and rescue devices can offer a valuable tool to save human lives and to speed up the search and rescue process. In this context, the EU-FP7-ICARUS project [1] concentrates on the development of unmanned search and rescue technologies for detecting, locating and rescuing humans. The complex nature and difficult operating conditions of search and rescue operations pose heavy constraints on the mechanical design of the unmanned platforms. In this paper, we discuss the different user requirements which have an impact of the design of the mechanical systems (air, ground and marine robots). We show how these user requirements are obtained, how they are validated, how they lead to design specifications for operational prototypes which are tested in realistic operational conditions and we show how the final mechanical design specifications are derived from these different steps. An important aspect of all these design steps which is emphasized in this paper is to always keep the end-users in the loop in order to come to realistic requirements and specifications, ensuring the practical deployability [2] of the developed platforms. © (2014) Trans Tech Publications, Switzerland.

Abstract

Abstract
Field experiments with a team of heterogeneous robots require human and hardware resources which cannot be implemented in a straightforward manner. Therefore, simulation environments are viewed by the robotic community as a powerful tool that can be used as an intermediate step to evaluate and validate the developments prior to their integration in real robots. This paper evaluates a novel multi-robot heterogeneous cooperative perception framework based on monocular measurements under the MORSE robotic simulation environment. The simulations are performed in an outdoor environment using a team of Micro Aerial Vehicles (MAV) and an Unmanned Ground Vehicle (UGV) performing distributed cooperative perception based on monocular measurements. The goal is to estimate the 3D target position.

Abstract
This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

Abstract
In this paper we propose a novel fully probabilistic solution to the stereo egomotion estimation problem. We extend the notion of probabilistic correspondence to the stereo case which allow us to compute the whole 6D motion information in a probabilistic way. We compare the developed approach against other known state-of-the-art methods for stereo egomotion estimation, and the obtained results compare favorably both for the linear and angular velocities estimation.

Abstract
Visual motion perception from a moving observer is the most often encountered case in real life situations. It is a complex and challenging problem, although, it can promote the arising of new applications. This article presents an innovative and autonomous robotic system designed for active surveillance and a dense optical flow technique. Several optical flow techniques have been proposed for motion perception however, most of them are too computationally demanding for autonomous mobile systems. The proposed HybridTree method is able to identify the intrinsic nature of the motion by performing two consecutive operations: expectation and sensing. Descriptive properties of the image are retrieved using a tree-based scheme and during the expectation phase. In the sensing operation, the properties of image regions are used by a hybrid and hierarchical optical flow structure to estimate the flow field. The experiments prove that the proposed method extracts reliable visual motion information in a short period of time and is more suitable for applications that do not have specialized computer devices. Therefore, the HybridTree differs from other techniques since it introduces a new perspective for the motion perception computation: high level information about the image sequence is integrated into the estimation of the optical flow. In addition, it meets most of the robotic or surveillance demands and the resulting flow field is less computationally demanding comparatively to other state-of-the-art methods.