Evangelos Boukas

Robot Guided Crowd Evacuation

The congregation of crowd undoubtedly constitutes an important risk factor, which may endanger the safety of the gathered people. The solution reported against this significant threat to citizens safety is to consider careful planning and measures. Thereupon, in this paper, we address the crowd evacuation problem by suggesting an innovative technological solution, namely, the use of mobile robot agents. The contribution of the proposed evacuation system is twofold: (i) it proposes an accurate Cellular Automaton simulation model capable of assessing the human behavior during emergency situations and (ii) it takes advantage of the simulation output to provide sufficient information to the mobile robotic guide, which in turn approaches and redirects a group of people towards a less congestive exit at a time. A custom-made mobile robotic platform was accordingly designed and developed. Last, the performance of the proposed robot guided evacuation model has been examined in real-world scenarios exhibiting significant performance improvement during the crucial first response time window.

SPARTAN: Developing a Vision System for Future Autonomous Space Exploration Robots

Mars exploration is expected to remain a focus of the scientific community in the years to come. A Mars rover should be highly autonomous because communication between the rover and the terrestrial operation center is difficult, and because the vehicle should spend as much of its traverse time as possible moving. Autonomous behavior of the rover implies that the vision system provides both a wide view to enable navigation and three-dimensional (3D) reconstruction, and at the same time a close-up view ensuring safety and providing reliable odometry data. The European Space Agency funded project “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) aimed to develop an efficient vision system to cover all such aspects of autonomous exploratory rovers. This paper presents the development of such a system, starting from the requirements up to the testing of the working prototype. The vision system was designed with the intention of being efficient, low-cost, and accurate and to be implemented using custom-designed vectorial processing by means of field programmable gate arrays (FPGAs). A prototype of the complete vision system was developed, mounted on a basic mobile robot platform, and tested. The results on both real-world Mars-like and long-range simulated data are presented in terms of 3D reconstruction and visual odometry accuracy, as well as execution speed. The developed system is found to fulfill the set requirements.

Towards orbital based global rover localization

Space exploratory rovers do well in autonomous or composite semi-autonomous exploration of extraterrestrial surfaces, yet their localization relies on the particular spot they had landed, rather than being universal, i.e. based on the absolute coordinate system of the explored planet. The idea underlaying the work presented in this paper is the transition from the relative to absolute localization by inspecting common Regions of Interest (ROIs) on both rover and orbital imagery. In order to achieve that we propose a method comprising an offline and an onboard procedure. Particularly, prior to the mission the orbital images of the intended landing area are examined to extract ROIs and to construct an offline Global Network (GN). The onboard procedure is based on the rovers self localization which is performed via an inertial aided visual odometry (VO). During its roaming the rover extracts ROIs from the ground and forms a Local Network (LN). The last is iteratively matched with the GN by a specifically designed matching procedure based on Data-Aligned Rigidity-Constrained Exhaustive Search (DARCES). The proposed method is tested on real representative data collected during the ESA Seeker activity. The results indicate that the self-localization of a planetary rover in an absolute frame of reference is feasible, provided that the area includes few discriminative ROIs.

Visual Odometry for autonomous robot navigation through efficient outlier rejection

The ability of autonomous robots to precisely compute their spatial coordinates constitutes an important attribute. In this regard, Visual Odometry (VO) becomes a most appropriate tool, in estimating the full pose of a camera, placed onboard a robot by analyzing a sequence of images. The paper at hand proposes an accurate computationally-efficient VO algorithm relying exclusively on stereo vision. A non-iterative outlier detection technique capable of efficiently discarding outliers of matched features is suggested. The developed technique is combined with an incremental motion estimation approach to estimate the robots trajectory. The accuracy of the proposed system has been evaluated both on simulated data and using a real robotic platform. Experimental results from rough terrain routes show remarkable accuracy with positioning errors as low as 1.1%.

SPARTAN system: Towards a low-cost and high-performance vision architecture for space exploratory rovers

The “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) activity of the European Space Agency (ESA) aims to develop an efficient, low-cost and accurate vision system for the future Martian exploratory rovers. The interest on vision systems for space robots has been steadily growing during the last years. The SPARTAN system considers an optimal implementation of computer vision algorithms for space rover navigation and is designated for application to a space exploratory robotic rover, such as the ExoMars. The goal of the present work is the development of an appropriate architecture for the vision system. Thus, the arrangement and characteristics of the rovers vision sensors will be defined and the required computer vision modules will be presented. The analysis will be performed taking into consideration the constraints defined by ESA about the SPARTAN system.

Path Tracing on Polar Depth Maps for Robot Navigation

In this paper a Cellular Automata-based (CA) path estimation algorithm suitable for safe robot navigation is presented. The proposed method combines well established 3D vision techniques with CA operations and traces a collision free route from the foot of the robot to the horizon of a scene. Firstly, the depth map of the scene is obtained and, then, a polar transformation is applied. A v-disparity image calculation processing step is applied to the initial depth map separating the ground plane from the obstacles. In the next step, a CA floor field is formed representing all the distances from the robot to the traversable regions in the scene. The target point that the robot should move towards to, is tracked down and an additional CA routine is applied to the floor field revealing a traversable route that the robot should follow to reach its target location.

SPARTAN project: Efficient implementation of computer vision algorithms onto reconfigurable platform targeting to space applications

Vision-based robotic applications exhibit increased computational complexity. This problem becomes even more important regarding mission critical application domains. The SPARTAN project focuses in the tight and optimal implementation of computer vision algorithms targeting to rover navigation for space applications. For evaluation purposes, these algorithms will be implemented with a co-design methodology onto a Virtex-6 FPGA device.

Learning the terrain and planning a collision-free trajectory for indoor post-disaster environments

Mobile robots dedicated in post-disaster missions should be capable of moving arbitrarily in unknown cluttered environments so as to accomplish their assigned security task. The paper in hand describes such an agent equipped with collision risk assessment capabilities, while it is able to trace an obstacle-free path in the scene as well. The robot exploits machine learning techniques for the traversability evaluation of the environment by making use of geometrical features, which derive from a postprocessing step of the depth map, obtained by an RGBD sensor. Then, the traversable scenes, are assessed by the likelihood the robot to collide on any arbitrary direction in front of it. Besides, the collision risk likelihood is combined with a path tracing algorithm based on Cellular Automata so that an obstacle-free route is then detected. The proposed method has been examined for several indoor scenarios revealing remarkable efficiency.

Stereo-based Visual Odometry for Autonomous Robot Navigation

Mobile robots should possess accurate self-localization capabilities in order to be successfully deployed in their environment. A solution to this challenge may be derived from visual odometry (VO), which is responsible for estimating the robots pose by analysing a sequence of images. The present paper proposes an accurate, computationally-efficient VO algorithm relying solely on stereo vision images as inputs. The contribution of this work is twofold. Firstly, it suggests a non-iterative outlier detection technique capable of efficiently discarding the outliers of matched features. Secondly, it introduces a hierarchical motion estimation approach that produces refinements to the global position and orientation for each successive step. Moreover, for each subordinate module of the proposed VO algorithm, custom non-iterative solutions have been adopted. The accuracy of the proposed system has been evaluated and compared with competent VO methods along DGPS-assessed benchmark routes. Experimental results of relevance to rough terrain routes, including both simulated and real outdoors data, exhibit remarkable accuracy, with positioning errors lower than 2%.

Modeling Regions of Interest on Orbital and Rover Imagery for Planetary Exploration Missions

ABSTRACT Planetary rover exploration missions require accurate and computationally efficient robot localization in order to perform complex and cooperative tasks. The global localization on planetary environments can be competently addressed by incorporating orbital and ground rover imagery. An indicative approach could include (1) the extraction of regions of interest (ROIs) in orbital images, (2) the extraction of ROIs in rover images, (3) the ROI matching, and (4) the localization. In order to perform adequately in ROI matching, a model should be able to detect common ROIs. The work in hand tackles the problem of extracting such regions of interest that are observable on both orbital and rover images. The dedicated model that was designed and implemented contains a detection and a classification part. The detection of the ROIs is based on both their texture and their geometrical properties. Classification was performed on the result of the detection in order to annotate the ROIs and discard any outliers caused by false detection. The results prove that the model is able to detect commonly observable regions and, therefore, is considered to be an adequate preprocessing step in the context of a global rover localization system.